An intelligent evaluation recommendation system and method based on a fusion convolutional neural network

Abstract

The application relates to the technical field of computer application, and discloses an intelligent evaluation recommendation system and method fusing a convolutional neural network, which comprises the following steps: extracting file data to generate a feature abstract through an evaluation data management module, and constructing a standardized whole editing data set; implementing similarity matching on the standardized whole editing data set through a similarity matching module; calculating batch average mean square error and adding a working condition yielding loss factor to construct a total loss function with a deterministic topological path, so as to constrain the parameter update path of a sorting model, and reordering and outputting a target text set; and the application applies discrete rule constraints on the update direction of the neural network through the total loss function, reduces the gradient direction deviation of the artificial intelligence middleware under non-ideal working conditions, and improves the convergence precision and sorting track stability of the model.

Description

Technical Field

[0001] This invention belongs to the field of computer application technology, and in particular relates to an intelligent evaluation and recommendation system and method that integrates convolutional neural networks. Background Technology

[0002] Currently, in large-scale text sequence processing and data stream distribution architectures, deploying distributed vector representation middleware in conjunction with a post-processor network structure to extract feature matrices is a common control method. By performing encoder vector transformation on the text, a high-dimensional feature stream is continuously fed to the reordering calculation module to support dynamic matching under complex conditions. This middleware feature flow mechanism embedded in space relies on a fixed mapping coordinate system. Its physical mechanism is to measure the similarity between different text sequences through geometric distance measurement, map the feature tensors generated by text encoding to a fixed vector region, and feed the vector data to a convolutional neural network or attention mechanism to achieve the mapping between feature distribution and final sorting labels.

[0003] While existing architectures often maintain the computing throughput efficiency of middleware nodes by optimizing the underlying hardware's roller shape, bus wiring, or physical heat dissipation performance, software-level control methods and error correction mechanisms still have unavoidable shortcomings under dynamic boundary conditions such as high concurrency and non-ideal input. For example, Chinese invention patent application CN116127940A discloses a text matching method, device, and computer-readable storage medium. It enriches the semantic representation capabilities of long texts by extracting sub-representations of multi-granularity representation units such as characters, sentences, full text, and entities and performing concatenation and fusion. However, the application benchmark for this technology is that the input text stream has a flat surface. Steady-state context distribution, when encountering non-stationary and heterogeneous conditions such as incomplete input data, random character replacement, or sudden interruptions, relies on a static feature fusion mechanism that cannot dynamically adjust the axial distribution of the high-dimensional space according to the changing control seed. This leads to a deviation in representation alignment of literally similar input sequences at the flow layer, causing coherent superposition of data noise within the space and inducing semantic confusion at the feature extraction node. In addition, post-matching, due to its over-reliance on expectation loss, lacks constraint interception guarantees under non-stationary cross-sections. Once individual documents experience prediction deviations, it is very easy to cause abnormal gradient divergence, making it impossible to maintain the stability of the reordered output trajectory under the impact of dynamic data flow.

[0004] Therefore, the technical problem to be solved by this invention is how to construct a state topology gating mechanism that can integrate the control of the seed to modulate the axial spatial distribution of the high-dimensional feature tensor, and introduce regularization penalty by reconstructing the structure of the error function of the reordering network, so as to achieve orthogonal decoupling of the feature vector space while trunculating the non-steady gradient jump and balancing the response performance and the ordering stability. Summary of the Invention

[0005] This invention aims to solve the problem of scaling high-dimensional embedded features based on discrete control seeds to eliminate semantic alignment drift under non-stationary conditions, and to reconstruct the reordering loss function to fully truncate non-stationary gradient jumps.

[0006] In this technical solution, an intelligent evaluation and recommendation system integrating convolutional neural networks includes: The evaluation is based on the interconnected data management module, similarity matching module, ranking recommendation module, and auxiliary evaluation conclusion generation module. The data management module extracts input file data through optical character recognition scanning to obtain structured text data, and works with the text summarization generation algorithm to calculate key feature summary data to construct a standardized and compiled dataset. The similarity matching module uses a convolutional neural network to perform vector-level text similarity matching on a standardized and compiled dataset, generating similarity matching result data. The ranking and recommendation module introduces the internal probability loss function of the ranking model constrained by the working condition retreat loss factor. When updating parameters, the average error algorithm is used to construct the total loss function, and the target text set is ranked according to the ranking model to output the recommendation list data. The auxiliary evaluation conclusion generation module automatically filters historical reference datasets based on the evaluation request data to be processed, uses text summarization technology to obtain key content of historical reference datasets as basic text data, and identifies core historical text features as structural skeletons through text similarity matching, and integrates and calculates to generate recommended evaluation text data.

[0007] Preferably, the evaluation data management module includes: normalizing and organizing historical evaluation data, recommendation model data, and reference analysis data as independent document data; using optical character recognition to scan and extract the content of each independent document data to obtain structured text data; using a text summarization generation model to extract key content from the structured text data to generate key feature summary data; and converting unstructured documents into standardized compiled datasets containing associated tags.

[0008] Preferably, the ranking recommendation module includes: calculating output scores for target evaluation text pairs to obtain the model output scores corresponding to each target evaluation text; when an increase in input text noise data is detected and a data interruption occurs, controlling the working condition yield loss factor to be greater than a preset stable baseline threshold, wherein the working condition yield loss factor is calculated by the ranking recommendation module under the corresponding working condition, and the working condition yield loss factor explicitly acts on the model parameter vector of the ranking model; and performing boundary truncation on the neural network weight update path of the ranking recommendation module according to the working condition yield loss factor to suppress drastic jumps in the parameter space.

[0009] Preferably, the working condition retreat loss factor is dynamically constructed from the residual variance and the divergent gradient; when the neural network weight update path is subject to boundary truncation constraints, the working condition retreat loss factor monotonically increases with the increase of text noise data, which is used to forcibly suppress the jump of the parameter space of the ranking recommendation module under the non-steady section, and ensure that the output recommendation list data remains stable under the impact of dynamic data flow.

[0010] Preferably, the similarity matching module includes: inputting structured text data from the standardized truncated dataset into the convolutional neural network of the similarity matching module; extracting deep semantic feature vectors from the structured text data using the convolutional and pooling layers of the convolutional neural network; calculating the cosine similarity between the deep semantic feature vectors to generate similarity matching result data, and transmitting the similarity matching result data to the ranking and recommendation module.

[0011] Preferably, the auxiliary evaluation conclusion generation module includes: in response to the received evaluation request data to be processed, automatically retrieving the standardized and compiled dataset to filter out the historical reference dataset; using a text summarization extraction model to obtain the key content of the historical reference dataset as the basic text data; using a similarity matching module to identify the historical core text features that have a similarity higher than a preset similarity threshold with the basic text data as the structural skeleton, and generating recommended evaluation text data through multi-feature fusion calculation.

[0012] Preferably, the independent document data includes historical evaluation data, recommendation model data, and reference analysis data; format normalization and sorting are carried out by deleting redundant blank lines and non-standard characters in the independent document data, and uniformly converting the text content into a standard simplified Chinese text data stream, so as to achieve self-consistency of the data format within the evaluation basis data management module.

[0013] Preferably, the auxiliary evaluation conclusion generation module and the evaluation basis data management module communicate with each other in real time; the system also includes a recommendation result output module, which is connected to the auxiliary evaluation conclusion generation module, and presents the original request stream data and recommendation evaluation text data in real time in the recommendation result output list, reducing the system's data response latency.

[0014] Preferably, the text summarization algorithm calculates the word frequency inverse document frequency weight of each sentence in the structured text data, selects the top 5 sentences by weight as key feature summary data, and associates the key feature summary data as attribute labels with the corresponding structured text data, thus completing the structured compilation of the standardized dataset. The evaluation basis data management module, similarity matching module, ranking recommendation module, and auxiliary evaluation conclusion generation module are all deployed in the artificial intelligence middleware architecture. The artificial intelligence middleware architecture provides standard data routing interfaces and state transition monitoring units to manage the data flow and parameter updates between modules, ensuring the overall logical high cohesion of the intelligent evaluation and recommendation system.

[0015] An intelligent evaluation and recommendation method incorporating convolutional neural networks, implemented through an intelligent evaluation and recommendation system incorporating convolutional neural networks, includes the following steps: Step S1: Using the evaluation basis data management module, the input file data is extracted through optical character recognition scanning to obtain structured text data, and key feature summary data is generated in conjunction with the text summarization generation algorithm to construct a standardized compilation dataset. Step S2: Use the similarity matching module to run a convolutional neural network to perform vector-level text similarity matching on the standardized and compiled dataset, and generate similarity matching result data; Step S3: The working condition concession loss factor is introduced into the ranking recommendation module and used to constrain the internal probability loss function of the ranking model. When updating the parameters, the average error algorithm is used to construct the total loss function, and the target text set is ranked according to the ranking model to output the recommendation list data. Step S4: The auxiliary evaluation conclusion generation module automatically filters the historical reference dataset based on the evaluation request data to be processed, uses text summarization extraction technology to obtain the key content of the historical reference dataset as the basic text data, and identifies the core text features of the history as the structural skeleton through text similarity matching, and generates recommended evaluation text data through fusion calculation.

[0016] Compared with existing technologies, the intelligent evaluation and recommendation system integrating convolutional neural networks of this invention has the following advantages: 1. In intelligent evaluation and recommendation using convolutional neural networks, the state topology gate operator introduces adversary identifiers and application state flags as discrete control seeds. It directly performs element-wise scaling on the 768-dimensional feature vector output by the pre-trained model, transforming the axial distribution of the evaluation problem state tensor in the logic flow layer. This allows literal identical search statements to be automatically mapped to mutually orthogonal vector subspaces, thereby eliminating dynamic semantic alignment drift caused by alternating inference camps or processing states. It also blocks the coherent superposition of text noise under non-stationary data, providing discrete feature inputs for subsequent collaborative attention mechanisms and ensuring that the optimization trajectory in the feature matching stage adheres to the steady-state benchmark of the inference system framework.

[0017] 2. The ranking module introduces an average error calculation architecture to reconstruct the loss function, and strongly couples a condition-based backoff loss factor dynamically constructed from residual variance and divergent gradient into the loss function. This applies a regularization penalty to the model parameter vector. When edge conditions such as text noise or sudden data interruption are detected in the input evaluation problem, the condition-based backoff loss factor monotonically increases, thereby applying a rigid boundary truncation to the neural network weight update path. This constrains the parameter space parameter deviation under unsteady conditions, avoids errors in the prediction of document relevance affecting the overall ranking direction of the model, and ensures the structural stability of the output recommendation list under dynamic data flow changes.

[0018] 3. The evaluation relies on data management technology to uniformly compile various independent documents, including existing adjudication data, model data, and research reports. Optical character recognition (OCR) is used to scan and extract document content to obtain text data in a unified format. This is then combined with a text summarization algorithm to automatically generate document summary information, transforming unstructured documents into a compiled dataset with associated tags. This dataset is deeply coupled with a subsequent text similarity matching algorithm based on convolutional neural networks and a recommendation mechanism based on an improved ranking algorithm, forming a complete logical closed loop from the structured compilation of original documents to personalized recommendations. This ensures data format self-consistency within the knowledge base and reduces data response latency. Attached Figure Description

[0019] Figure 1 This is a flowchart of the multi-module collaborative text reordering and recommendation method of the present invention; Figure 2 This is a system architecture diagram of the distributed heterogeneous data scheduling and middleware architecture of the present invention. Detailed Implementation

[0020] The technical solutions of the embodiments of this application will be clearly described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, not all embodiments. All other embodiments obtained by those skilled in the art based on the embodiments of this application are within the scope of protection of this application.

[0021] An intelligent evaluation and recommendation system incorporating convolutional neural networks includes: The evaluation is based on the interconnection of the data management module, similarity matching module, ranking and recommendation module, and auxiliary evaluation conclusion generation module. The evaluation relies on the data management module, which extracts input file data through optical character recognition scanning to obtain structured text data, and then uses a text summarization algorithm to calculate key feature summary data to construct a standardized and compiled dataset. The similarity matching module uses a convolutional neural network to perform vector-level text similarity matching on a standardized and compiled dataset, generating similarity matching result data. The ranking and recommendation module introduces the internal probability loss function of the ranking model constrained by the working condition retreat loss factor. When updating parameters, the average error algorithm is used to construct the total loss function, and the target text set is ranked according to the ranking model to output the recommendation list data. The auxiliary evaluation conclusion generation module automatically filters historical reference datasets based on the evaluation request data to be processed, uses text summarization technology to obtain key content of historical reference datasets as basic text data, and identifies core historical text features as structural skeletons through text similarity matching, and integrates and calculates to generate recommended evaluation text data.

[0022] Preferably, the evaluation data management module includes: normalizing and organizing historical evaluation data, recommendation model data, and reference analysis data as independent document data; using optical character recognition to scan and extract the content of each independent document data to obtain structured text data; using a text summarization generation model to extract key content from the structured text data to generate key feature summary data; and converting unstructured documents into standardized compiled datasets containing associated tags.

[0023] Preferably, the ranking recommendation module includes: calculating output scores for target evaluation text pairs to obtain the model output scores corresponding to each target evaluation text; when an increase in input text noise data is detected and a data interruption occurs, controlling the working condition yield loss factor to be greater than a preset stable baseline threshold, wherein the working condition yield loss factor is calculated by the ranking recommendation module under the corresponding working condition, and the working condition yield loss factor explicitly acts on the model parameter vector of the ranking model; and performing boundary truncation on the neural network weight update path of the ranking recommendation module according to the working condition yield loss factor to suppress drastic jumps in the parameter space.

[0024] Preferably, the working condition retreat loss factor is dynamically constructed from the residual variance and the divergent gradient; when the neural network weight update path is subject to boundary truncation constraints, the working condition retreat loss factor monotonically increases with the increase of text noise data, which is used to forcibly suppress the jump of the parameter space of the ranking recommendation module under the non-steady section, and ensure that the output recommendation list data remains stable under the impact of dynamic data flow.

[0025] Preferably, the similarity matching module includes: inputting structured text data from the standardized truncated dataset into the convolutional neural network of the similarity matching module; extracting deep semantic feature vectors from the structured text data using the convolutional and pooling layers of the convolutional neural network; calculating the cosine similarity between the deep semantic feature vectors to generate similarity matching result data, and transmitting the similarity matching result data to the ranking and recommendation module.

[0026] Preferably, the auxiliary evaluation conclusion generation module includes: in response to the received evaluation request data to be processed, automatically retrieving the standardized and compiled dataset to filter out the historical reference dataset; using a text summarization extraction model to obtain the key content of the historical reference dataset as the basic text data; using a similarity matching module to identify the historical core text features that have a similarity higher than a preset similarity threshold with the basic text data as the structural skeleton, and generating recommended evaluation text data through multi-feature fusion calculation.

[0027] Preferably, the independent document data includes historical evaluation data, recommendation model data, and reference analysis data; format normalization and sorting are carried out by deleting redundant blank lines and non-standard characters in the independent document data, and uniformly converting the text content into a standard simplified Chinese text data stream, so as to achieve self-consistency of the data format within the evaluation basis data management module.

[0028] Preferably, the auxiliary evaluation conclusion generation module and the evaluation basis data management module communicate with each other in real time; the system also includes a recommendation result output module, which is connected to the auxiliary evaluation conclusion generation module, and presents the original request stream data and recommendation evaluation text data in real time in the recommendation result output list, reducing the system's data response latency.

[0029] Preferably, the text summarization algorithm calculates the word frequency inverse document frequency weight of each sentence in the structured text data, selects the top 5 sentences by weight as key feature summary data, and associates the key feature summary data as attribute labels with the corresponding structured text data, thus completing the structured compilation of the standardized dataset. The evaluation basis data management module, similarity matching module, ranking recommendation module, and auxiliary evaluation conclusion generation module are all deployed in the artificial intelligence middleware architecture. The artificial intelligence middleware architecture provides standard data routing interfaces and state transition monitoring units to manage the data flow and parameter updates between modules, ensuring the overall logical high cohesion of the intelligent evaluation and recommendation system.

[0030] An intelligent evaluation and recommendation method incorporating convolutional neural networks, implemented through an intelligent evaluation and recommendation system incorporating convolutional neural networks, includes the following steps: Step S1: Using the evaluation basis data management module, the input file data is extracted through optical character recognition scanning to obtain structured text data, and key feature summary data is generated in conjunction with the text summarization generation algorithm to construct a standardized compilation dataset. Step S2: Use the similarity matching module to run a convolutional neural network to perform vector-level text similarity matching on the standardized and compiled dataset, and generate similarity matching result data; Step S3: The working condition concession loss factor is introduced into the ranking recommendation module and used to constrain the internal probability loss function of the ranking model. When updating the parameters, the average error algorithm is used to construct the total loss function, and the target text set is ranked according to the ranking model to output the recommendation list data. Step S4: The auxiliary evaluation conclusion generation module automatically filters the historical reference dataset based on the evaluation request data to be processed, uses text summarization extraction technology to obtain the key content of the historical reference dataset as the basic text data, and identifies the core text features of the history as the structural skeleton through text similarity matching, and generates recommended evaluation text data through fusion calculation.

[0031] Example 1: In an AI middleware service scenario for collaborative interaction of multi-source heterogeneous text data, the AI ​​middleware is deployed on the interface nodes of the heterogeneous data scheduling to distribute the processing of structured and unstructured text data in the business data stream. Given the non-stationary nature of the input text stream, the same text expression can have different evaluation implications depending on the business entity, processing timeline, or data flow state. When feature vectors are directly input into the similarity ranking network, the lack of a dynamic decoupling loop triggers semantic alignment drift at the logical flow layer, causing a misalignment in the retrieval direction. This exposes the insufficient adaptability of existing technologies in dealing with changing operating conditions due to reliance on manually fixed rules. The evaluation is based on the data management module receiving independent data containing multi-source background data. The document data is processed by using optical character recognition (OCR) scanning to extract the content of each independent document to obtain structured text data. The evaluation criteria data management module, in conjunction with a text summarization generation model, extracts key content from the structured text data to generate key feature summary data. This summary data is then used to convert unstructured documents into a standardized, compiled dataset containing associated tags. The similarity matching module receives the standardized, compiled dataset and inputs the structured text data from it into its internal convolutional neural network. The convolutional and pooling layers of the convolutional neural network extract deep semantic feature vectors from the structured text data. To eliminate semantic alignment drift caused by sudden changes in operating conditions at the source of data flow, the system calls a state topology gating operator between the pre-trained model and the convolutional neural network. The state topology gating operator utilizes adversary identifiers extracted from the application list. With application status flag bit As a discrete control seed, the 768-dimensional feature vector output by the pre-trained model is subjected to element-wise scaling. The specific multi-dimensional scaling transformation formula for the features is defined as follows: ,in This represents the element-wise product operator executed by the tensor processor. and Based on and The integer value is matched with the feature adjustment vector containing the scaling slope factor and orthogonal offset constant in the local lookup table. This vector is used to transform the axial distribution of the evaluation problem state tensor in the logic flow layer, so that the search statements with the same literal meaning are mapped to mutually orthogonal vector subspaces, thus blocking the coherent superposition of text noise under non-stationary data.

[0032] The similarity matching module calculates the cosine similarity between deep semantic feature vectors to generate similarity matching result data. The system executes an initial state definition procedure to fix the input environment conditions, requiring the host distributed computing nodes to have a floating-point operation capability of no less than 10 TFLOPS, a memory bandwidth of no less than 100 GB / s, and a residual bandwidth of no less than 500 Mbps in the network data bus, thus constructing a deterministic initialization environment. The state topology gating operator achieves spatial orthogonal decoupling by scaling the feature vectors output by the pre-trained model. Based on the dimensionless integer values ​​of the adversary identifier extracted from the application list and the application state flag bits, the state topology gating operator matches discrete control coefficients containing scaling slope factors and orthogonal offset constants in a local lookup table. Then, using the element-wise multiply-add instructions in the tensor processor, each channel component in the 768-dimensional feature vector is multiplied by the scaling slope factor and superimposed with the orthogonal offset constant. The system constructs the component distribution of the feature vector along the high-dimensional axis, executes the quantization procedure for process judgment, and uses the bus monitoring component to extract the reconstructed feature tensor samples online to monitor the evolution of the decoupling process. The bus monitoring component calculates the cosine distance between the current sampling point feature tensor and the historical feature moving average vector, calculates the sample variance in 768 channel dimensions, and divides the sample variance by the mean magnitude of the historical moving average vector to obtain the dimensionless feature variance variability index. When the feature variance variability index is less than the stability threshold of 0.15 for 5 consecutive sampling points, it is determined that the feature space has completed steady-state decoupling, and the system allows the feature vector to enter the next step. Here, the adversary identifier represents the integer constant representing different inference camps, the application status flag represents the classification status quantity of the business flow time sequence stage, and the stability threshold of 0.15 is the maximum tolerance limit of feature drift calibrated through offline simulation test set. The similarity matching result data is then transmitted to the ranking and recommendation module.

[0033] The ranking and recommendation module receives similarity matching results data, calculates output scores for target evaluation text pairs to obtain the model output scores corresponding to each target evaluation text. When an increase in input text noise data and a data interruption are detected, the working condition yield loss factor is controlled to be greater than a preset stable baseline threshold. The working condition yield loss factor is calculated by the ranking and recommendation module under the corresponding working condition and applies to the model parameter vector of the ranking model. Based on the working condition yield loss factor, the neural network weight update path of the ranking and recommendation module is truncated to suppress parameter space jumps. The ranking and recommendation module introduces the working condition yield loss factor to constrain the internal probability loss function of the ranking model. For any set of evaluation document pairs, the first evaluation document is... The second evaluation document is The model output scores are respectively and According to the model's predictions, the first evaluation document Compared to the second evaluation document The probability of being more relevant is expressed by the formula: ,in, The first evaluation document for model predictions Compared to the second evaluation document More relevant probability, The coefficients are scalar constants. For the first evaluation document The model outputs a score. For the second evaluation document The model outputs a score; during parameter updates, an average error algorithm is used to construct a total loss function with a fixed topological path. The parameter update path of the ranking model is constrained, and the total loss function is used. The algebraic structure is restricted to: ,in, For the total loss function, For true relative relevance labels, This represents the total number of document pairs within the current batch. The number of neurons in the hidden layer. Given the known input feature components, These are the connection weight parameters from the input layer to the hidden layer, where the subscripts use a single-level hyphen format to lock the single-level index topology. For the bias term of the hidden layer, These are the connection weight parameters from the hidden layer to the output layer. For the global bias term of the output layer, The scalar constant is used to adjust the weighting factor. The working condition yield loss factor represents the parameter vector of the sorting model. The penalty, and the working condition yield loss factor when the neural network weight update path is subject to boundary truncation constraints. It monotonically increases with the increase of text noise data, and is used to forcibly suppress the jump of the parameter space of the ranking recommendation module under non-steady-state sections, so as to ensure that the output recommendation list data maintains structural stability under the impact of dynamic data flow, thereby reducing the gradient direction deviation of artificial intelligence middleware under non-ideal working conditions and improving the convergence accuracy and ranking trajectory stability of the model.

[0034] The aforementioned underlying control loop for boundary truncation of the weight update path using the working condition retreat loss factor is as follows: the working condition retreat loss factor term in the total loss function serves as a regularization penalty, generating a damped gradient proportional to the parameter offset velocity during backpropagation; the optimizer of the re-ranking recommendation module adds a geometric projection operator when performing parameter updates, which monitors the Euclidean norm of the model parameter vector in real time; when the working condition retreat loss factor monotonically increases with noise, causing the penalty term to exceed the preset safety boundary upper limit, the aforementioned geometric projection operator is activated, forcibly projecting the calculated parameter update step size back into a closed hypersphere centered on the baseline model parameters with a radius of 2.5, rigidly truncating the weight update path exceeding the hypersphere boundary, thereby transforming the scalar penalty of the loss function into a boundary rigid truncation constraint at the geometric level of the parameter space, preventing unsteady discrete transitions of the parameters. The geometric projection operator is implemented through a hardware-level control loop. When the rigid truncation occurs and the loss factor exceeds the preset safety boundary limit, the geometric projection operator sends a hardware interrupt instruction to the central processing unit of the distributed computing node. This directly suspends the current backpropagation parameter update process and drives the state transition monitoring unit to rewrite the state control bit in the local register. The state control bit is changed from the normal operating value to the interception lock value. The interception lock value causes the standard data routing interface in the AI ​​middleware architecture to cut off the write channel of the current gradient data to the model parameter storage area in physical memory. The physical storage address pointer is forcibly reset to the safe storage area of ​​the baseline model parameters that were fitted and solidified under the previous steady-state operating condition. When the central processing unit completes the orthogonal projection calculation of the current parameter step size into the closed hypersphere with a radius of 2.5, the state control bit returns to the normal operating value. The system reopens the write permission to physical memory and resumes data flow. The rigid interception of abnormal gradients is completed by using the address reset and process suspension mechanism at the physical storage level.

[0035] When the ranking and recommendation module reorders and outputs the recommendation list data, the auxiliary evaluation conclusion generation module responds to the received evaluation request data to be processed, automatically retrieves the standardized and compiled dataset to filter out the historical reference dataset, uses the text summarization extraction model to obtain the key content of the historical reference dataset as the basic text data, and uses the similarity matching module to identify the historical core text features with similarity higher than the preset similarity threshold as the structural skeleton. It then integrates the historical evaluation conclusion data with the user-defined judgment rules to automatically generate the recommendation evaluation text data dynamically balanced by the current working conditions. This allows relevant researchers to interactively correct the data based on the judgment template rules, avoiding bias caused by human experience, and enabling the entire intelligent evaluation and recommendation system to maintain full-scenario auxiliary support in a heterogeneous data processing environment.

[0036] The aforementioned steps for fusion calculation of multiple data sources to generate recommendation evaluation text data are as follows: The auxiliary evaluation conclusion generation module parses and identifies the structural skeleton of the historical core text features, extracting a text template matrix containing structured paragraph control characters, logical argument slots, and technical effect reporting domains; the module performs word segmentation and dependency parsing on the basic text data, extracting the core verbs and technical noun phrases, and filling these phrases one by one into the corresponding logical argument slots in the aforementioned text template matrix according to semantic similarity alignment rules; the module reads user-defined judgment rules, and when it is determined that the current working condition control parameters trigger a specific rule threshold, it automatically retrieves matching legal judgment statement phrases from the historical evaluation conclusion data and adds them as constraint seeds to the technical effect reporting domain; the system calls a preset text splicing state machine, and performs string splicing and deduplication smoothing processing on the filled template matrix according to the preset timing of the structured paragraph control characters, thereby automatically generating recommendation evaluation text data that conforms to the current working condition constraints and has a closed logical loop.

[0037] Example 2: The current test platform includes distributed computing nodes. Each distributed computing node has a floating-point operation capability greater than or equal to 10 TFLOPS and a memory bandwidth greater than or equal to 100 GB / s. The test input loads a test dataset containing 50,000 non-stationary business text sequences to provide heterogeneous data processing conditions. Scalar constants are used to adjust the weighting factors. Adjusting the structural risk penalty of the ranking model, where... The scalar constant is used to adjust the weighting factor to balance empirical risk and regularization constraints. The magnitude of the weighting factor is limited by the power of the input feature noise. As the power of uncertain noise in the text stream increases, the scalar constant adjusts the weighting factor. To enhance parameter constraints, the upper limit of the protection range is adjusted using scalar constants to control the weighting factor. The value is set to a fixed scalar value of 0.02 under the current operating conditions.

[0038] The experimental groups included a control group and an experimental group using the improved middleware architecture. 15% of the text input channel was injected with random character replacements and noise interference to simulate non-ideal input conditions. When the control group processed 1000 pairs of evaluation documents containing noise interference, the primary feature vectors were input into the similarity ranking network. The lack of a dynamic decoupling loop caused significant discrete jumps in the iterative trajectory of the parameter space. The intermediate process parameters recorded by the data acquisition module indicated that the connection weight parameters from the hidden layer to the output layer... The update trajectory did not converge after 200 alternating iterations, and the total loss function... Maintaining a constant working section of 0.42, the sorting alignment deviation rate of the output recommendation list data is fixed at 24.6%.

[0039] The experimental group invoked the state topology gating operator under the same input conditions. The state topology gating operator used the adversary identifier. With application status flag bit As a discrete control seed, the 768-dimensional feature vector output by the pre-trained model is scaled element-wise, so that the features of the retrieved text with the same literal meaning are mapped to mutually orthogonal vector subspaces. The intermediate feature data output by the calculation module shows the working condition retreat loss factor. As the text noise data increases, the factor for loss due to operating conditions monotonically increases from the initial baseline of 0.05 to 0.38. The first evaluation document of the model prediction under the constraint change Compared to the second evaluation document More relevant probability probability The mathematical formula for calculation is limited to: ,in, The first evaluation document for model predictions Compared to the second evaluation document More relevant probability, The coefficients are scalar constants. For the first evaluation document The model outputs a score. For the second evaluation document The model outputs a score; when updating parameters, the ranking model calls the average error to calculate the topology reconstruction loss factor and the total loss function. The algebraic relation is expressed as: ,in, For the improved total loss function, For true relative relevance labels, This represents the total number of document pairs within the current training batch. For the activation function feature mapping operator, The number of neurons in the hidden layer. Given the known input feature components, These are the connection weight parameters from the input layer to the hidden layer, where the subscripts use a single-level hyphen format to lock the single-level index topology. For the bias term of the hidden layer, These are the connection weight parameters from the hidden layer to the output layer. For the global bias term of the output layer, The scalar constant is used to adjust the weighting factor. The working condition yield loss factor represents the parameter vector of the sorting model. The regularization penalty term, where The parameter vector for the ranking model; the working condition retreat loss factor when the neural network weight update path is subject to boundary truncation constraints. The parameter space of the ranking model increases monotonically with the increase of text noise data, which is used to suppress the jump of the parameter space under the unsteady section. The ranking alignment deviation rate of the recommended list data output by the experimental group is controlled at 2.3%, which reduces the gradient direction deviation of the middleware service node under the perturbation state and improves the convergence accuracy and ranking trajectory stability of the model.

[0040] To test the physical constraints of text noise variation on middleware performance stability, the test platform measured the index response when the text noise ratio increased in a gradient. When the text noise ratio was set at the absolute lower limit of 5%, the normal median of 15%, and the protection upper limit of 30%, the sorting alignment deviation rates of the recommended list data output by the test group were 1.1%, 2.3%, and 4.8%, respectively, and the parameter curves showed a stable near-linear anti-interference trajectory. When the noise input deviated from the limited range and rose to the 35% out-of-range parameter boundary, the system operation triggered a nonlinear performance inflection point caused by feature coherence overload. At this point, the vector subspace of the 768-dimensional feature tensor tended to saturate, causing the operating condition to yield the loss factor. The regularization penalty boundary enters an overload degenerate state, where, For the ranking model parameter vector, constrain the total loss function. The ranking recommendation module's output scores tend to diverge, exhibiting discrete and disordered behavior. This causes the ranking alignment deviation rate of the recommendation list data to rise to 18.2%. The quantitative characteristic data of the performance inflection point provides the physical boundary basis for the working window limitation. The parameter response of the experimental group within the specified working window is determined by the total loss function. The algebraic topological constraint path is maintained by the precise hedging of the unsteady gradient, and the data characteristics correspond to the operating state of the middleware service architecture to reduce gradient direction deviation, improve convergence accuracy and sorting trajectory stability.

[0041] Example 3: This example combines Figures 1 to 2This document describes an intelligent evaluation and recommendation system and method that integrates convolutional neural networks, such as... Figure 1 As shown, step S1 utilizes the evaluation basis data management module to extract input file data through optical character recognition scanning to obtain structured text data, and coordinates with the text summarization generation algorithm to calculate key feature summary data to construct a standardized compilation dataset. Step S2 utilizes the similarity matching module to run a convolutional neural network to perform vector-level text similarity matching on the standardized compilation dataset, generating similarity matching result data. Step S3 utilizes the ranking recommendation module to introduce the working condition concession loss factor and use it to constrain the internal probability loss function of the ranking model. When updating parameters, the average error algorithm is used to construct the total loss function, and the target text set is ranked according to the ranking model to output the recommendation list data. Step S4 utilizes the auxiliary evaluation conclusion generation module to automatically filter historical reference datasets based on the evaluation request data to be processed, uses text summarization extraction technology to obtain the key content of the historical reference datasets as basic text data, and identifies historical core text features as the structural skeleton through text similarity matching, and integrates and calculates to generate recommended evaluation text data.

[0042] like Figure 2 As shown, the distributed heterogeneous data scheduling interface node includes a network interface layer, a network detector, an anonymization and desensitization processing program, and an initial state definition procedure. The network interface layer performs transient transmission delay loss measurement and obtains the network delay lag time. The network detector performs physical boundary condition measurement and satisfies a bus residual bandwidth ≥ 500 Mbps. The anonymization and desensitization processing program performs anonymization and desensitization processing before authorized raw data extraction. The initial state definition procedure initializes the environment and locks the physical boundary conditions at the input end. The distributed heterogeneous data scheduling interface node is connected to the host distributed computing node through a network data bus. The host distributed computing node meets the following requirements: processor core base operating frequency ≥ 2.0 GHz, floating-point operation capability ≥ 10 TFLOPS, and memory bandwidth ≥ 100 GB / s. Heterogeneous computing is deployed within the host distributed computing node. The microservice container, a heterogeneous microservice container, is an AI middleware architecture. This AI middleware architecture includes a standard data routing interface, a state transition monitoring unit, an evaluation basis data management module, a similarity matching module, a ranking and recommendation module, an auxiliary evaluation conclusion generation module, a recommendation result output module, and a process judgment quantification procedure. The standard data routing interface manages data flow between modules; the state transition monitoring unit manages parameter updates for each module; the evaluation basis data management module constructs a standardized and compiled dataset; the similarity matching module includes a pre-trained model and a state topology gating operator; the ranking and recommendation module restricts non-steady-state jumps in the parameter space; the auxiliary evaluation conclusion generation module performs high-similarity historical core feature skeleton recognition; the recommendation result output module presents the original request stream and the recommendation evaluation text; and the process judgment quantification procedure performs real-time monitoring and calculation of physical parameters.

[0043] Example 4: In an AI middleware deployment environment for collaborative interaction of multi-source heterogeneous text data, the AI ​​middleware is connected to the interface node of the distributed heterogeneous data scheduling. Due to the high-frequency throughput change characteristics of the multi-source text stream at the input end within the specified running cycle, when the evaluation basis data management module extracts the real-time sequence stream in the distributed inference service node, the data sampling section is prone to deep aliasing, which causes the parameter update trajectory of the re-ranking recommendation network to jump discretely and generate gradient divergence. To ensure the control stability of the system under this abnormal working condition, this method starts the anonymization and desensitization processing procedure by default before any data extraction, and the acquisition of all original data is based on the explicit authorization of the user.

[0044] To fully disclose the online calibration path of the AI ​​middleware for the aforementioned data throughput fluctuations, the pre-trained model of the similarity matching module executes a three-stage feature processing flow after receiving multi-source input features. The first stage is input featureization, where the system reads the raw message received from the network interface and converts it into a raw feature tensor containing specific multi-dimensional dense vectors. The second stage is online optimization control. The similarity matching module allocates hardware register resources of the local processor and calls the state topology gating operator to optimize the original feature tensor. Perform a programmed discrete-time transformation, which selects adversary identifiers extracted from the application list. With application status flag bit As a discrete control seed, element-wise scaling is performed on the 768-dimensional feature vector output by the pre-trained model to reconstruct the original feature tensor. The axial distribution in the logical flow layer allows literal search statements to be automatically mapped to mutually orthogonal vector subspaces. The third stage is the reconstruction calibration trigger. When the bus monitoring component at the system level detects that the variability index of the input feature exceeds the preset stability threshold for 5 consecutive sampling points, it determines that the current static context mapping is invalid. The system triggers the reset of the sliding time window and uses the latest cached 512 text increment fragments to perform adaptive weight fine-tuning on the model. In actual operation, the aforementioned variability index of the input feature is determined by the following pure text logic steps: the bus monitoring component obtains the 768-dimensional original feature tensor of the current input at each sampling point and maintains a historical feature moving average vector with a length of 50 sampling points.

[0045] The component calculates the cosine distance between the current feature tensor and the historical feature moving average vector, and calculates the sample variance of this distance sequence across 768 channel dimensions. This sample variance is then divided by the mean magnitude of the historical moving average vector, and normalization is performed to obtain a dimensionless scalar value, which serves as the variability index for the current sampling point. The aforementioned preset stability threshold is set to a fixed value of 0.15 in the current industrial middleware configuration. When the variability index calculated for five consecutive sampling points is greater than 0.15, it confirms that a non-stationary alignment drift exceeding the system tolerance boundary has occurred in the feature space, thus triggering subsequent adaptive weight fine-tuning.

[0046] The ranking and recommendation module introduces a working condition concession loss factor to impose discrete rule constraints on the internal probability loss function of the ranking model. The adaptive control loop relationship is defined as follows: ,in, The working condition yield loss factor represents the parameter vector of the sorting model. The penalty items, This is the noise adaptive damping factor. For the ranking model, the first Real-time weight parameters of each hidden layer node. These are the parameters of the baseline model fitted and solidified under the preceding steady-state conditions, representing the parameter safety state baseline of the ordination model. The parameter vector of the currently constrained ranking model Total dimension, noise adaptive damping factor The specific value is obtained by monitoring the instantaneous data loss rate flowing through the middleware network interface. Real-time acquisition; the calculation process satisfies algebraic calculation formulas: ,in, This is the noise adaptive damping factor. For the instantaneous data loss rate, the aforementioned mapping logic of dynamically constructing the working condition backoff loss factor from residual variance and divergent gradient is as follows: During system operation, the prediction residual variance and gradient divergence of the re-ranking recommendation model directly determine the iteration step size and direction of the model parameter vector in the optimization space. When the increase in text noise causes the model's prediction output to deviate, the residual variance of the current training batch increases, and the magnitude of the divergent gradient calculated by backpropagation surges accordingly. This causes the real-time weight parameters of the hidden layer nodes in the ranking model to deviate from the baseline model parameters fitted and solidified under the previous steady-state working condition within one current update cycle. Therefore, by calculating the sum of squares of the differences between the real-time weight parameters and the baseline model parameters, the magnitude of the model parameter space anomaly caused by the superposition of residual variance and divergent gradient can be directly and in real-time quantified. On this basis, the system multiplies and combines this anomaly magnitude with the damping factor that reflects the instantaneous information loss characteristics, thereby completely constructing a penalty term that can dynamically constrain the non-steady-state gradient, ensuring that the residual variance and divergent gradient are deterministically represented and transmitted in the algebraic constraint path.

[0047] When noise adaptive damping factor With the instantaneous data loss rate When the value increases monotonically, the total loss function... The numerical range boundary is restricted by the penalty term, thus establishing a damping hedging mechanism for non-steady gradients at the system level. This forcibly suppresses the discrete jumps in the model parameter space of the ranking and recommendation module under extreme throughput conditions, ensuring that the output recommendation list data maintains stable convergence performance under dynamic data flow impact. Finally, the auxiliary evaluation conclusion generation module receives the ranking results output by the ranking and recommendation module and automatically generates recommendation evaluation text data dynamically balanced by the current operating conditions. The final output recommendation list ranking alignment deviation rate is stable within the safety index range of 3.1%, and the system as a whole maintains normal information flow under long-term high-concurrency throughput conditions.

[0048] Example 5: In an AI middleware deployment environment with multi-source heterogeneous text data collaborative interaction, when the system faces the lifecycle boundary switching of replacing old and new algorithms or rearranging heterogeneous microservice containers, the data throughput flow of interface nodes generates transient transmission latency loss. At this time, when the network data bus carries sudden evaluation requests, due to the mismatch of initial parameters, the convolutional layer of the similarity matching module experiences abnormal divergence of feature squares. In order to maintain normal control stability of the entire middleware service architecture under the switching section, the system automatically triggers a standardized on-site deployment pre-calibration process. When the process starts, the initial state definition procedure forcibly locks the physical boundary conditions of the input end, that is, the bus residual bandwidth of the current idle network interface is greater than or equal to 500Mbps as measured by the network detector, and the basic operating frequency of the processor core allocated to the AI ​​middleware by the host machine is not less than 2.0GHz, in order to construct a deterministic initialization bearing environment.

[0049] The process judgment quantification procedure provides a basis for switching control without subjective experience intervention by performing real-time monitoring of physical parameters in the pre-calibration process. After receiving the anonymized incremental calibration text stream, the processor reads the network delay lag time generated by the network interface layer within the sliding observation period. The initial loss is obtained, where The network latency lag time is defined, and the network latency lag time is defined. Substituting into the fixed linear mapping regression operator, the initial calibration weight parameters are calculated online. The processor uses these initial calibration weight parameters to drive the feature matrix adjuster of the similarity matching module in situ, applying adaptive bias hedging to the neural network layer biases in the reordering network to suppress gradient divergence caused by temporal discontinuities in the convolutional layers. When the bus monitoring component measures that the feature variance variability index is less than the stability threshold of 0.05 for three consecutive sampling periods, the system automatically terminates the pre-calibration process and switches to the standard distributed operation mode, achieving a smooth steady-state transition of the entire intelligent evaluation and recommendation system in a multi-source heterogeneous scheduling environment. In actual operation, the network latency reflects the transient lag in data flow interaction under distributed scheduling environment. The resulting feature flow misalignment will induce statistical features at the input of the reordering network. The aforementioned fixed linear mapping regression operator internally stores a latency bias sensitivity coefficient matrix pre-calibrated through an offline simulation training set. The number of rows in this matrix is ​​consistent with the latency monitoring dimension of the network interface, and the number of columns corresponds exactly to the bias vector dimension of the first convolutional layer in the reordering network, i.e., 256 feature dimensions. The processor inputs the latency lag time into this regression operator, performs matrix multiplication, and calculates a continuous bias adjustment vector with a dimension of 256 online. After receiving this vector, the feature matrix adjuster adds it in place to the initial bias parameters of the convolutional layer. It uses numerical offset to shift the zero-point trigger threshold of the feature activation function, offsetting the drastic changes in data flow variance caused by temporal discontinuities from the digital logic level, realizing data flow normalization at the network level, and blocking the cascading divergence of abnormal gradients.

[0050] The embodiments of this application have been described above with reference to the accompanying drawings. Unless otherwise specified, the embodiments and features in the embodiments of this application can be combined with each other. This application is not limited to the specific embodiments described above. The specific embodiments described above are merely illustrative and not restrictive. Those skilled in the art can make many other forms under the guidance of this application without departing from the spirit of this application and the scope of protection of this invention, and all of these forms are within the protection scope of this application.

Claims

1. An intelligent evaluation and recommendation system integrating convolutional neural networks, characterized in that, include: The evaluation is based on the interconnection of the data management module, similarity matching module, ranking and recommendation module, and auxiliary evaluation conclusion generation module. The evaluation is based on the data management module extracting input file data through optical character recognition scanning to obtain structured text data, and cooperating with the text summarization generation algorithm to calculate key feature summary data, which is used to construct a standardized compilation dataset; The similarity matching module uses a convolutional neural network to perform vector-level text similarity matching on a standardized and compiled dataset, generating similarity matching result data. The ranking and recommendation module introduces the internal probability loss function of the ranking model constrained by the working condition retreat loss factor. When updating parameters, the average error algorithm is used to construct the total loss function, and the target text set is ranked according to the ranking model to output the recommendation list data. The auxiliary evaluation conclusion generation module automatically filters historical reference datasets based on the evaluation request data to be processed, uses text summarization technology to obtain key content of historical reference datasets as basic text data, and identifies core historical text features as structural skeletons through text similarity matching, and integrates and calculates to generate recommended evaluation text data.

2. The intelligent evaluation and recommendation system integrating convolutional neural networks according to claim 1, characterized in that, The evaluation data management module includes: normalizing and organizing historical evaluation data, recommendation model data, and reference analysis data as independent document data; using optical character recognition to scan and extract the content of each independent document data to obtain structured text data; using a text summarization generation model to extract key content from the structured text data to generate key feature summary data; and converting unstructured documents into standardized compiled datasets containing associated tags.

3. The intelligent evaluation and recommendation system integrating convolutional neural networks according to claim 1, characterized in that, The ranking and recommendation module includes: calculating output scores for target evaluation text pairs to obtain the model output scores corresponding to each target evaluation text; when an increase in input text noise data is detected and a data interruption occurs, controlling the working condition yield loss factor to be greater than a preset stable baseline threshold, wherein the working condition yield loss factor is calculated by the ranking and recommendation module under the corresponding working condition, and the working condition yield loss factor is explicitly applied to the model parameter vector of the ranking model; and performing boundary truncation on the neural network weight update path of the ranking and recommendation module based on the working condition yield loss factor to suppress drastic jumps in the parameter space.

4. The intelligent evaluation and recommendation system integrating convolutional neural networks according to claim 3, characterized in that, The working condition retreat loss factor is dynamically constructed from the residual variance and the divergent gradient. When the neural network weight update path is subject to boundary truncation constraints, the working condition retreat loss factor monotonically increases with the increase of text noise data. This is used to forcibly suppress the jump of the parameter space of the ranking recommendation module under the non-steady-state section, and ensure that the output recommendation list data remains stable under the impact of dynamic data flow.

5. The intelligent evaluation and recommendation system integrating convolutional neural networks according to claim 1, characterized in that, The similarity matching module includes: inputting structured text data from the standardized and compiled dataset into the convolutional neural network of the similarity matching module; extracting deep semantic feature vectors from the structured text data using the convolutional and pooling layers of the convolutional neural network; calculating the cosine similarity between the deep semantic feature vectors to generate similarity matching result data; and transmitting the similarity matching result data to the ranking and recommendation module.

6. The intelligent evaluation and recommendation system integrating convolutional neural networks according to claim 1, characterized in that, The auxiliary evaluation conclusion generation module includes: responding to the received evaluation request data to be processed, automatically retrieving the standardized and compiled dataset to filter out the historical reference dataset; using a text summarization extraction model to obtain the key content of the historical reference dataset as the basic text data; using a similarity matching module to identify the historical core text features with similarity higher than a preset similarity threshold with the basic text data as the structural skeleton, and generating recommended evaluation text data through multi-feature fusion calculation.

7. The intelligent evaluation and recommendation system integrating convolutional neural networks according to claim 2, characterized in that, Independent document data includes historical evaluation data, recommendation model data, and reference analysis data; format normalization and organization transforms the text content into a standard simplified Chinese text data stream by removing redundant blank lines and non-standard characters from the independent document data, thus achieving self-consistency of the data format within the evaluation basis data management module.

8. The intelligent evaluation and recommendation system integrating convolutional neural networks according to claim 1, characterized in that, The auxiliary evaluation conclusion generation module and the evaluation basis data management module communicate with each other in real time. The system also includes a recommendation result output module, which is connected to the auxiliary evaluation conclusion generation module. The original request stream data and recommendation evaluation text data are presented in real time in the recommendation result output list, reducing the system's data response latency.

9. The intelligent evaluation and recommendation system integrating convolutional neural networks according to claim 1, characterized in that, The text summarization generation algorithm calculates the word frequency inverse document frequency weight of each sentence in the structured text data, selects the top 5 sentences by weight as key feature summary data, and associates the key feature summary data as attribute labels with the corresponding structured text data to complete the structured compilation of the standardized dataset. The evaluation basis data management module, similarity matching module, ranking recommendation module, and auxiliary evaluation conclusion generation module are all deployed in the artificial intelligence middleware architecture. The AI ​​middleware architecture provides standard data routing interfaces and state transition monitoring units to manage data flow and parameter updates between modules, ensuring high logical cohesion of the overall intelligent evaluation and recommendation system.

10. A method for intelligent evaluation and recommendation incorporating convolutional neural networks, used to implement the intelligent evaluation and recommendation system incorporating convolutional neural networks as described in claim 1, characterized in that, Includes the following steps: Step S1: Using the evaluation basis data management module, the input file data is extracted through optical character recognition scanning to obtain structured text data, and key feature summary data is generated in conjunction with the text summarization generation algorithm to construct a standardized compilation dataset. Step S2: Use the similarity matching module to run a convolutional neural network to perform vector-level text similarity matching on the standardized and compiled dataset, and generate similarity matching result data; Step S3: The working condition concession loss factor is introduced into the ranking recommendation module and used to constrain the internal probability loss function of the ranking model. When updating the parameters, the average error algorithm is used to construct the total loss function, and the target text set is ranked according to the ranking model to output the recommendation list data. Step S4: The auxiliary evaluation conclusion generation module automatically filters the historical reference dataset based on the evaluation request data to be processed, uses text summarization extraction technology to obtain the key content of the historical reference dataset as the basic text data, and identifies the core text features of the history as the structural skeleton through text similarity matching, and generates recommended evaluation text data through fusion calculation.

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