A self-adaptive cognitive enhancement highway digital intelligent interaction system and method

Abstract

The application relates to the technical field of intelligent highway management, in particular to a highway digital intelligent interaction system and method with self-adaptive cognitive enhancement, which comprises a multi-modal intention analysis module, a credibility measurement scheduling module, a semantic alignment retrieval module, a time sequence dynamic prediction module and a strategy adjustment module.In the application, a structured judgment mechanism is constructed by deeply analyzing multi-modal natural language instructions and interface context features, effectively breaking the absolute dependence of hard-coded rules on fixed syntax formats and strict exclusive vocabulary sets, dynamically building differentiated multi-level business channels by comprehensively measuring feature probability distribution and information entropy density, generating professional compliance operation and maintenance reply results by combining multi-dimensional space semantic vectors and knowledge graph bottom layer association, avoiding practical business risks such as recognition failure or incorrect triggering of irrelevant processing processes caused by ambiguous expressions, and guaranteeing the overall response accuracy and operation and maintenance interaction continuity of the core digital system in a dynamic complex scene.

Description

Technical Field

[0001] This invention relates to the field of intelligent highway management technology, and in particular to an adaptive cognitive enhancement digital intelligent interactive system and method for highways. Background Technology

[0002] The field of intelligent highway management technology mainly involves the digital monitoring and business scheduling of transportation infrastructure using computer networks, artificial intelligence, and database technologies. Traditional intelligent interactive digital highway systems rely on servers and display terminals deployed in monitoring centers to receive operational instructions from management personnel. These systems respond to user actions such as clicking menus or entering keywords based on pre-written, fixed code logic. The system's backend database stores structured business data tables. When a user enters a specific query term on the front-end interface, the server-side application retrieves the corresponding preset question template through string matching. If a match is found, a stored procedure is invoked to execute the pre-defined structured query statement, and the retrieved static data fields are directly rendered onto the front-end display screen. For complex business needs, manual form filling or spreadsheet export for offline statistical analysis is required in the backend management system. Existing digital intelligent interactive systems for highways primarily rely on pre-defined keyword matching logic and fixed menu hierarchies to respond to user commands. This hard-coded rule-based operation mode requires user input commands to strictly conform to predefined syntax formats and vocabulary sets. When faced with natural language input containing typos or ambiguous expressions, the underlying string matching algorithm cannot accurately parse semantic relationships, leading to command recognition failures or erroneous triggering of irrelevant business processes. Furthermore, with the dynamic changes in highway management business needs, maintenance personnel must manually modify the judgment rules and query templates in the backend code. This static maintenance method, which heavily relies on manual intervention, cannot adapt to massive non-standardized data interaction scenarios, making it difficult to guarantee the accuracy of system responses and the continuity of business execution when handling complex and ever-changing actual management tasks.

[0003] The existing system relies on preset keyword logic and a fixed menu structure to respond to external commands. Hard-coded rules require that input statements must strictly conform to predefined formats and exclusive vocabulary sets. When faced with ambiguous expressions, the underlying matching mechanism cannot accurately parse the inherent semantic relationships, which can easily lead to intent recognition failure or erroneous triggering of irrelevant business processes. Furthermore, as business needs change dynamically, the backend judgment rules need to be continuously modified manually. The static maintenance mechanism cannot adapt to the interaction scenarios of massive non-standardized real highway data, making it difficult to fully guarantee the overall response accuracy and the consistency of actual business execution. Summary of the Invention

[0004] To address the technical problems of existing technologies, such as the inability of the underlying matching mechanism to accurately parse the inherent semantic relationships when faced with ambiguous expressions, which easily leads to intent recognition failure or erroneous triggering of irrelevant business processes, and the need for continuous manual modification of backend judgment rules as business requirements dynamically change, making static maintenance mechanisms unsuitable for massive non-standardized real-world highway data interaction scenarios, thus making it difficult to fully guarantee the overall response accuracy and the consistency of actual business execution, this invention provides an adaptive cognitive enhancement-based intelligent digital interaction system and method for highways. The technical solution is as follows: On the one hand, an adaptive cognitive-enhanced digital intelligent interaction system for highways is provided, which includes: The multimodal intent parsing module acquires natural language commands and interface context features from highway monitoring terminals, constructs a multimodal intent classification set, builds a cognitive matching sequence of the multimodal intent classification set in the business scenario, judges the logical compliance status of the cognitive matching sequence, and constructs structured intent judgment features. The credibility measurement and scheduling module analyzes the probability distribution pattern of the structured intent judgment features, calculates the information entropy distribution density of the associated cognitive credibility identifier, identifies the node mapping associated information of the information entropy distribution density, and constructs a multi-level business channel. The semantic alignment retrieval module analyzes the data structure of the multi-level business channels, compares the association mapping relationship between the multi-dimensional spatial semantic vector and the highway knowledge graph, and constructs highway operation and maintenance response data. The time-series dynamic prediction module analyzes the status of business nodes included in the highway operation and maintenance response data, calculates the hidden layer evolution trend of the dynamic traffic flow monitoring sequence in the long short-term memory network, identifies the time-series evolution characteristics, and generates traffic prediction results. The strategy adjustment module analyzes the execution response status of the traffic prediction results on the highway digital platform, calculates the cognitive evaluation benchmark of the interactive feedback observation sequence, identifies the deviation distribution pattern, and generates a scheduling update strategy.

[0005] As a further aspect of the present invention, the structured intent determination features include operation behavior attributes, scene parameter entities, and permission rule identifiers; the multi-level business channels include fault-tolerant transmission links, emergency response buses, and regular scheduling interfaces; the highway operation and maintenance response data includes fault diagnosis plans, facility maintenance guidance, and alarm cancellation scripts; the traffic prediction results include congestion probability index, average vehicle speed, and peak emergence time; and the scheduling update strategy includes resource allocation weights, traffic light control plans, and guidance release criteria.

[0006] As a further aspect of the present invention, the multimodal intent parsing module includes: The semantic classification extraction submodule acquires the natural language commands and interface context features of the highway monitoring terminal, converts the natural language commands into lexical tensors, extracts the interface context features associated with coordinate points, inputs the lexical tensor and coordinate points into a multiplier to perform a dot product operation, calculates the tensor inner product of the lexical tensor and coordinate points under the timestamp, maps the tensor inner product to the semantic classification vector space, measures the Euclidean distance of the tensor inner product in the classification vector space, and obtains the intent distribution hash value. The business scenario comparison submodule calls the intent distribution hash value, collects business scenario feature attribute parameters, assembles the intent distribution hash value and feature attribute parameters into a cognitive matching sequence according to time, calculates the data offset difference of the cognitive matching sequence at the business feature nodes, sums the data offset differences at the business feature nodes, subtracts the result of the offset difference from the sequence offset benchmark threshold, and calculates the proportion of the number of nodes exceeding the benchmark threshold in the total number of global nodes to generate the cognitive matching deviation rate. The logical compliance determination submodule, for the cognitive matching deviation rate, sends the cognitive matching deviation rate into a comparator and compares it with the preset compliance benchmark interval boundary line, filters out abnormal discrete points located outside the benchmark interval, extracts the timestamp index sequence associated with the abnormal discrete points, extracts the device operation record array along the timestamp index, and concatenates the operation record array with the cognitive matching deviation rate into a structured intent tensor to obtain the structured intent determination feature.

[0007] As a further aspect of the present invention, the credibility measurement scheduling module includes: The distribution morphology quantification submodule, based on the structured intent determination features, collects a time-series operation record array, extracts the intent occurrence frequency within the time-series operation record array, performs a dot product operation on the structured intent determination features and the intent occurrence frequency, calculates the feature parameters' projection components in multi-dimensional coordinates, reads the discrete variance value of the projection components within a set window, compares the discrete variance value with a set morphology benchmark value, extracts the variance variable sequence within the benchmark value limit, accumulates the variance sequences to obtain the sum, and generates the distribution morphology coefficient. The information entropy calculation submodule calls the distribution morphology coefficient, collects the credibility dictionary set, substitutes the distribution morphology coefficient into the dictionary set to perform key-value indexing, extracts the matching real-time condition identifier matrix, calculates the multi-element partial derivative values ​​of the identifier matrix, maps the partial derivative values ​​to occurrence probability values, calculates the logarithmic self-information of the occurrence probability values, multiplies the self-information with the occurrence probability values, sums them and performs an inverse operation, quantifies the information entropy values ​​within the interval, and divides the information entropy values ​​by the spatial step size parameter to obtain the information entropy density value. The node mapping submodule collects topology node clusters for the information entropy density value, injects the information entropy density value into the routing links of the node cluster, measures the communication attenuation of the routing links, filters available link nodes whose communication attenuation is within the attenuation benchmark limit, summarizes the address codes of available nodes, performs hierarchical sorting of the address codes according to the information entropy density value sorting rules, allocates service frequency band resources and configures capacity, and constructs multi-level service channels.

[0008] As a further aspect of the present invention, the semantic alignment retrieval module includes: The data structure parsing submodule extracts the node bit width parameter within the data structure based on the multi-level business channels, calculates the capacity carrying product, extends and projects the capacity carrying product along the time axis, reads the projection boundary integral parameter, performs a difference operation between the boundary integral parameter and the capacity reference value, extracts the processed deviation sequence, and splices multi-layer node feature elements according to the distribution order of the deviation sequence to obtain the structure parsing quantity. The association mapping comparison submodule, for the structural parsing quantity, obtains the spatial semantic vector and the highway knowledge graph, maps the structural parsing quantity to the addressing index, reads the attributes of the spatial semantic vector in the graph, compares the association mapping relationship between the multidimensional spatial semantic vector and the highway knowledge graph, extracts the edge weight parameters of the mapping nodes, subtracts the edge weight parameters from the weight benchmark value, selects the node set whose difference falls within the tolerance interval, expands the node set by dimension to measure the Euclidean distance, and generates the mapping distance value; The maintenance response construction submodule collects maintenance text dictionaries based on the mapped distance values, substitutes the mapped distance values ​​into the dictionary addressing array, extracts record parameters under the address bits, filters character encoding elements within the record parameters, concatenates the character encoding elements with the mapped distance values ​​into a joint sequence, extracts character vectors at frequency peaks, reassembles the character vectors according to a fixed byte length, reads the instruction metadata combination features within the reassembled byte block, and constructs highway maintenance response data.

[0009] As a further aspect of the present invention, the process of extending and projecting the capacity-bearing product along the time axis and reading the integral parameter of the projection boundary specifically involves: extracting the mean value of the capacity-bearing product on the time axis, dividing the time axis into discrete time slices, and accumulating the mean value of the capacity-bearing product along the discrete time slices to construct a time-bearing surface. The highest and lowest projection points on the positioning time bearing surface are located, and a closed geometric region is constructed by connecting the highest and lowest projection points. The cumulative area value is calculated within the closed geometric region, and the cumulative area value is extracted as the projection boundary integral parameter.

[0010] As a further aspect of the present invention, the time-series dynamic prediction module includes: The node status parsing submodule extracts business node status record parameters based on the highway operation and maintenance response data, calculates the node status projection components, extracts the extreme values ​​of the projection components within a set time window, subtracts the extreme values ​​from the set status benchmark threshold, filters the node coordinate index sequence that exceeds the benchmark threshold, arranges the corresponding coordinates of the index sequence in order and splices them into a feature matrix, calculates the intrinsic root values ​​of the feature matrix, and obtains the node status feature values. The hidden layer evolution calculation submodule calls the node state feature values, collects dynamic traffic flow monitoring sequences, merges the dynamic traffic flow monitoring sequences and node state feature values ​​into a monitoring tensor according to timestamps, substitutes the monitoring tensor into the network hidden layer operation unit, calculates the product of input gate weights and forget gate weights, accumulates the product sum to generate hidden layer cell state parameters, calculates the partial derivatives of the hidden layer cell state parameters on the time axis, extracts the central pole elements in the partial derivative distribution array, and establishes the hidden layer evolution trend quantity. The temporal feature recognition submodule acquires a temporal evolution feature reference sequence for the hidden layer evolution trend quantity, performs a differential comparison operation between the hidden layer evolution trend quantity and the reference sequence, extracts the residual sequence after the differential operation, calculates the power spectral density component of the residual sequence in the frequency domain space, filters feature parameters whose power spectral density components are within the frequency domain reference limit, expands the feature parameters according to the spatial dimension and calculates the average value, multiplies the average value by the time compensation coefficient to generate a bias variable, and generates traffic prediction results.

[0011] As a further aspect of the present invention, the strategy adjustment module includes: The response status analysis submodule, based on the traffic prediction results, collects the execution record matrix of the highway digital platform, merges the traffic prediction results and the execution record matrix according to the timestamp, calculates the projection component of the merged matrix in the time dimension, extracts the response delay parameter within the projection component, compares the response delay parameter with the set response delay benchmark value, extracts the delay sequence that exceeds the benchmark value, accumulates the delay sequences to obtain the response offset sum, and divides the response offset sum by the total number of platform nodes to obtain the execution response status quantity; The cognitive benchmark extraction submodule calls the execution response state quantity to obtain the interactive feedback observation sequence. It uses the execution response state quantity as a weighting coefficient to multiply the interactive feedback observation sequence to extract a weighted observation array. It measures the Euclidean distance between the elements in the weighted observation array, extracts the extreme value parameter of the Euclidean distance within a set window, inputs the extreme value parameter into a differencer to calculate the second derivative, extracts the coordinate index where the second derivative approaches zero, extracts the local feedback feature set according to the coordinate index and calculates the mean, and generates a cognitive evaluation benchmark value. The scheduling strategy update submodule extracts the distribution frequency of the difference parameter in the spatial domain based on the cognitive evaluation benchmark value, compares the distribution frequency with the set frequency threshold, collects the platform scheduling resource inventory parameter, performs a dot product operation on the deviation morphological feature quantity and the inventory parameter to calculate the resource consumption rate, subtracts the resource consumption rate from the set rate limit, extracts the instruction code corresponding to the extreme point in the difference sequence, and generates the scheduling update strategy.

[0012] As a further aspect of the present invention, the process of aligning and merging the traffic prediction results and the execution record matrix according to timestamps, and calculating the projection components of the merged matrix in the time series dimension, specifically involves: extracting the prediction timestamps included in the traffic prediction results, extracting the actual timestamps included in the execution record matrix, comparing the prediction timestamps with the actual timestamps one by one, removing isolated time points that failed to match, and splicing the data corresponding to the retained matching time points to generate the merged matrix. Numerical accumulation is performed along the time-series axis of the merge matrix to construct an accumulated numerical sequence, and the accumulated numerical sequence is extracted as a time-series dimension projection component.

[0013] On the other hand, an adaptive cognitive-enhanced intelligent interaction method for digital highways, which is executed based on the aforementioned adaptive cognitive-enhanced intelligent interaction system for digital highways, includes the following steps: S1: Collect natural language commands and interface context features from highway monitoring terminals, construct a multimodal intent classification set, construct a cognitive matching sequence of the multimodal intent classification set in the business scenario, determine the logical compliance status of the cognitive matching sequence, and construct structured intent judgment features; S2: Analyze the probability distribution pattern of the structured intent judgment features, calculate the information entropy distribution density of the associated cognitive credibility identifier, identify the node mapping associated information of the information entropy distribution density, and construct a multi-level business channel; S3: Analyze the data structure of the multi-level business channels, compare the association mapping relationship between the multi-dimensional spatial semantic vector and the highway knowledge graph, and construct highway operation and maintenance response data; S4: Analyze the status of business nodes included in the highway operation and maintenance response data, calculate the hidden layer evolution trend of the dynamic traffic flow monitoring sequence in the long short-term memory network, identify the temporal evolution characteristics, and generate traffic prediction results; S5: Analyze the execution response status of the traffic prediction results on the highway digital platform, calculate the cognitive evaluation benchmark of the interactive feedback observation sequence, identify the deviation distribution pattern, and generate a scheduling update strategy.

[0014] The beneficial effects of the technical solutions provided in the embodiments of the present invention include at least the following: By deeply analyzing multimodal natural language commands and interface context features to construct a structured judgment mechanism, it effectively breaks the absolute dependence of hard-coded rules on fixed grammatical formats and strict exclusive vocabulary sets. It comprehensively measures the probability distribution of judgment features and information entropy density to dynamically build differentiated multi-level business channels. Combining multi-dimensional spatial semantic vectors and the underlying association of knowledge graphs, it generates professional and compliant operation and maintenance response results, avoiding the actual business risks of recognition failure or erroneous triggering of irrelevant processing procedures due to ambiguous expressions. It observes the hidden evolution trend of execution response status and interaction feedback deviation to generate forward-looking scheduling and update strategies, ensuring the overall response accuracy and operation and maintenance interaction consistency of the core digital system in dynamic and complex scenarios. Attached Figure Description

[0015] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0016] Figure 1 This is a schematic diagram of the system provided by the present invention; Figure 2 This is a schematic diagram of the system framework of the present invention; Figure 3 This is a flowchart of the multimodal intent parsing module in this invention; Figure 4 This is a flowchart of the credibility measurement scheduling module in this invention; Figure 5 This is a flowchart of the semantic alignment retrieval module in this invention; Figure 6 This is a flowchart of the time-series dynamic prediction module in this invention; Figure 7 This is a flowchart of the strategy adjustment module in this invention; Figure 8 This is a flowchart of the method provided by the present invention. Detailed Implementation

[0017] The technical solution of the present invention will now be described with reference to the accompanying drawings.

[0018] To make the technical problems, technical solutions and advantages of the present invention clearer, a detailed description will be given below in conjunction with the accompanying drawings and specific embodiments.

[0019] This invention provides an adaptive cognitive enhancement-based digital intelligent interaction system for highways, such as... Figure 1-2 The diagram shown illustrates an adaptive cognitive-enhanced digital intelligent interaction system for highways. This system includes: The multimodal intent parsing module acquires natural language commands and interface context features from highway monitoring terminals, constructs a multimodal intent classification set, builds a cognitive matching sequence of the multimodal intent classification set in the business scenario, judges the logical compliance status of the cognitive matching sequence, and constructs structured intent judgment features. The credibility measurement and scheduling module analyzes the probability distribution of structured intent judgment features, calculates the information entropy distribution density of associated cognitive credibility identifiers, identifies the node mapping associated information of information entropy distribution density, and constructs multi-level business channels. The semantic alignment retrieval module analyzes the data structure of multi-level business channels, compares the association mapping relationship between multi-dimensional spatial semantic vectors and highway knowledge graphs, identifies the candidate document arrangement and distribution characteristics included in the association mapping relationship, and constructs highway operation and maintenance response data. The time-series dynamic prediction module analyzes the status of business nodes included in the highway operation and maintenance response data, calculates the hidden layer evolution trend of the dynamic traffic flow monitoring sequence in the long short-term memory network, identifies time-series evolution characteristics, analyzes the periodic fluctuation tendency of time-series evolution characteristics, and generates traffic prediction results. The strategy adjustment module analyzes the execution response status of traffic prediction results on the highway digital platform, calculates the cognitive evaluation benchmark of the interactive feedback observation sequence, determines the deviation distribution pattern between the cognitive evaluation benchmark and the reinforcement learning algorithm, optimizes traffic management and resource allocation schemes, and generates scheduling update strategies. The structured intent determination features include operational behavior attributes, scenario parameter entities, and permission rule identifiers. The multi-level business channels include fault-tolerant transmission links, emergency response buses, and regular scheduling interfaces. The highway operation and maintenance response data includes fault diagnosis plans, facility maintenance guidance, and alarm cancellation scripts. The traffic prediction results include congestion probability index, average vehicle speed, and peak emergence time. The scheduling update strategy includes resource allocation weights, traffic light control plans, and guidance release criteria.

[0020] Specifically, such as Figure 2 , 3 As shown, the multimodal intent parsing module includes: The semantic classification extraction submodule acquires the natural language commands and interface context features of the highway monitoring terminal, converts the natural language commands into lexical tensors, extracts the interface context features associated with coordinate points, inputs the lexical tensor and coordinate points into a multiplier to perform a dot product operation, calculates the tensor inner product of the lexical tensor and coordinate points under the timestamp, maps the tensor inner product to the semantic classification vector space, measures the Euclidean distance of the tensor inner product in the classification vector space, and obtains the intent distribution hash value. The system synchronously retrieves natural language command audio data packets and interface context feature logs via directional microphone arrays deployed at highway toll stations and along main roads, as well as through the terminal console's operation interface. The audio data packet format is limited to 16-bit quantization depth and a 44100 Hz sampling rate uncompressed pulse-code modulation signal. The interface context feature log contains the absolute pixel coordinates of interactive buttons on the display screen, millisecond-level timestamp records, and hexadecimal operation codes. The natural language audio feature matrix is ​​mapped to a high-dimensional semantic space, transforming it into a 512-dimensional one-dimensional floating-point vocabulary tensor. Using a coordinate system eccentric transformation component, the relative pixel positions within the interface context feature log are converted into global two-dimensional absolute coordinate positions based on the interface resolution scaling factor. The 512-dimensional vocabulary tensor and the global two-dimensional absolute coordinate positions containing horizontal and vertical coordinate components are input into the floating-point multiplier queue built into the on-chip digital signal processor, performing element-level dot product operations on the alignment dimension of the tensor and the coordinate positions. For example, when a component of the lexical tensor has a value of 1.5 and the corresponding coordinate point component has a value of 2.0, the dot product is 3.0. Summing the components yields a tensor inner product of 45.8. This inner product value of 45.8 is fed into the multi-dimensional semantic classification vector space of the support vector machine classifier. Based on the spatial vector distance calculation rules, the Euclidean distance between the coordinate point of this inner product and the centers of the three preset baseline intent clusters in the classification vector space is measured, yielding a minimum Euclidean distance value of 12.4. This Euclidean distance value of 12.4 is then input into the secure hash algorithm version 2 encryption engine to generate a 256-bit intent distribution hash value.

[0021] The business scenario comparison submodule calls the intent distribution hash value, collects the business scenario feature attribute parameters, assembles the intent distribution hash value and feature attribute parameters into a cognitive matching sequence according to time, calculates the data offset difference of the cognitive matching sequence at the business feature nodes, sums the data offset differences at the business feature nodes, subtracts the sum of offset differences from the sequence offset baseline threshold, and calculates the proportion of the number of nodes exceeding the baseline threshold in the total number of global nodes to generate the cognitive matching deviation rate. The intent distribution hash value and multi-dimensional business scenario feature attribute parameters are concatenated bit-wise according to the chronological order of collection, forming a cognitive matching sequence of 1024 bytes. The real-time feature value of the cognitive matching sequence at the 5th preset business feature node is obtained through a differential calculation component, and its difference is calculated with the value of the baseline feature sequence at the same node in the previous historical time period, yielding a data offset difference of 3.2 at that business feature node. A loop instruction set is used to traverse all 40 feature nodes in the sequence, and the data offset differences at each business feature node are continuously summed in a hardware accumulation register, resulting in a sum of offset differences of 28.6. This sum of 28.6 is then fed into a hardware subtractor, and the pre-programmed sequence offset baseline threshold of 15.0 in the read-only memory is subtracted, resulting in an excess difference of 13.6. The sequence offset baseline threshold of 15.0 is set based on the arithmetic mean of the offset difference data over the previous 30 normal operating cycles plus twice the standard deviation. The number of abnormal nodes exceeding the baseline threshold was counted as 8. This number was then divided by the total number of global nodes, which was 40, resulting in a cognitive matching deviation rate of 20%.

[0022] The logical compliance determination submodule, for the cognitive matching deviation rate, sends the cognitive matching deviation rate into a comparator and compares it with the preset compliance benchmark interval boundary line, filters out abnormal discrete points located outside the benchmark interval, extracts the timestamp index sequence associated with the abnormal discrete points, extracts the device operation record array along the timestamp index, and concatenates the operation record array with the cognitive matching deviation rate into a structured intent tensor to obtain the structured intent determination feature. The cognitive matching deviation rate is fed into a digital window comparator and compared with the upper and lower limits of a pre-stored compliance benchmark range of 5% to 15%. 20% of the values ​​are identified as falling outside the upper limit of 15%, and these anomalous data points are marked as anomalous discrete points in the global topology map. The timestamp index sequence associated with the triggering of this anomalous discrete point is extracted from the system's global clock bus. A time window extending 5 seconds forward and backward along this timestamp index sequence is used to extract a device operation record array from the distributed solid-state storage cluster, containing data on device CPU load rate, memory throughput, and network port packet loss rate. A tensor concatenation operator is invoked to perform a multidimensional tensor concatenation operation between the feature matrix of this device operation record array and the 20% cognitive matching deviation rate, expanding and reconstructing it into a 64x64 structured intent tensor. This structured intent determination feature is then output to the data message queue.

[0023] Specifically, such as Figure 2 , 4 As shown, the credibility measurement scheduling module includes: The distribution morphology quantification submodule, based on structured intent determination features, collects a time-series operation record array, extracts the intent occurrence frequency within the time-series operation record array, performs a dot product operation on the structured intent determination features and intent occurrence frequency, calculates the feature parameters' projection components in multi-dimensional coordinates, reads the discrete variance value of the projection components within a set window, compares the discrete variance value with a set morphology benchmark value, extracts the variance variable sequence within the benchmark value limit, accumulates the variance sequences to obtain the sum, and generates the distribution morphology coefficient. A time-series operation record array of the past 24 hours is collected. This array is scanned using a counter component to extract the frequency of occurrence of five preset key fault intents. The multidimensional matrix values ​​of the structured intent determination features and the one-dimensional vector of intent occurrence frequencies are fed into a tensor processing unit for dot product operations to calculate the projection component matrix of the multidimensional feature parameters along each coordinate axis in an orthogonal coordinate system. Multiple sets of discrete data points within a 10-minute time window are extracted from the projection component matrix. The sum of the squares of the deviations of each set of data points from their arithmetic mean is calculated, yielding a discrete variance value of 4.5. This discrete variance value of 4.5 is then compared with a preset morphological baseline value ranging from 3.0 to 5.0. The upper limit of 5.0 and the lower limit of 3.0 of the morphological baseline value are obtained by estimating the confidence interval of the historical variance data distribution frequency under stable operation conditions in the fourth quarter of each year for highways. A sequence of 15 variance variables within the baseline value limit is extracted, and the 15 values ​​in the sequence are summed using a hardware accumulator to generate a distribution shape coefficient with a value of 65.4.

[0024] The information entropy calculation submodule calls the distribution morphology coefficient, collects the credibility dictionary set, substitutes the distribution morphology coefficient into the dictionary set to perform key-value indexing, extracts the matching real-time condition identifier matrix, calculates the multi-element partial derivative values ​​of the identifier matrix, maps the partial derivative values ​​to occurrence probability values, calculates the logarithmic self-information of the occurrence probability values, multiplies the self-information with the occurrence probability values, sums them and performs an inverse operation, quantifies the information entropy values ​​within the interval, and divides the information entropy values ​​by the spatial step size parameter to obtain the information entropy density value. The distribution morphological coefficient 65.4 is converted into a hash-addressable address and substituted into the credibility dictionary set to perform a key-value index lookup operation, extracting the real-time conditional identifier matrix that strictly matches the key value. This matrix is ​​a 16x16 double-precision floating-point array. Based on the Gaussian elimination algorithm, the partial derivative values ​​of each diagonal element in the identifier matrix with respect to time are calculated to obtain an array of partial derivatives containing the dynamic rate of change of the matrix. The Softmax normalization function is called to map each element in the partial derivative array to a value range of 0 to 1, generating an occurrence probability value with a sum of 1.0. In the specific data stream, for the first element, its occurrence probability value is 0.25. The logarithmic operation component is called to calculate the base-2 logarithm of this 0.25 occurrence probability value, generating a self-information of -2.0. Multiplying the self-information of -2.0 by the probability of occurrence of 0.25 yields -0.5. After traversing all elements of the matrix, the product is summed. Finally, the summation is passed through a hardware inverter to perform a sign inversion operation, quantizing the information entropy value within the interval to 1.8. This information entropy value of 1.8 is fed into a divider and divided by a pre-configured spatial step size parameter of 0.6, where the spatial step size parameter 0.6 is derived from the mapping of the standard geographical distance constant between physical optical cable nodes, resulting in an information entropy density value of 3.0.

[0025] The node mapping submodule collects information on the topology node cluster based on the information entropy density value, injects the information entropy density value into the routing links of the node cluster, measures the communication attenuation of the routing links, filters available link nodes whose communication attenuation is within the attenuation benchmark limit, summarizes the address codes of available nodes, performs hierarchical sorting of the address codes according to the information entropy density value sorting rules, allocates service frequency band resources and configures capacity, and constructs multi-level service channels. A topology discovery data packet is sent to the network resource management layer to collect topology node cluster information containing 30 routing switching nodes. An information entropy density value of 3.0 is injected as a probe payload into each routing link of the node cluster. The communication attenuation of each routing link is measured using a calculation model based on the round-trip time difference between the sender and receiver and the packet loss rate. A filter component is invoked to select available link nodes whose communication attenuation is within the upper attenuation baseline limit of 10 dB, and the media access control address codes of qualified available nodes are summarized into the addressing table. A fast sorting algorithm component is activated to perform a hierarchical reordering operation on the summarized address codes according to the sorting rule from largest to smallest information entropy density value. Based on the sorted address hierarchy queue, the system's total bandwidth of 10 Gbps service frequency band resources is allocated according to a capacity allocation mechanism of 2 Gbps for high-priority nodes and 0.5 Gbps for low-priority nodes, constructing a multi-level service channel with three priority gradients. Specific routing link communication attenuation measurement data are shown in Table 1. Table 1: Routing Link Communication Attenuation Measurement Record Table 1 144500 Node 101 Node 205 -40 dBmW 4.2 decibels Within the boundary 2 144502 Node 102 Node 208 -55 dBmW 12.5 decibels Exceeding the limit 3 144505 Node 103 Node 210 -38 dBmW 3.8 decibels Within the boundary Table 1 shows the signal strength at the receiving end of multiple routing links after the injection of information entropy density value probes, as well as the actual measured communication attenuation parameters, verifying the basis for the screening action.

[0026] Specifically, such as Figure 2 , 5 As shown, the semantic alignment retrieval module includes: The data structure parsing submodule extracts the node bit width parameter within the data structure based on the multi-level business channels, calculates the capacity carrying product, extends and projects the capacity carrying product along the time axis, reads the projection boundary integral parameter, performs a difference operation between the boundary integral parameter and the capacity benchmark value, extracts the processed deviation sequence, and splices multi-level node feature elements according to the distribution order of the deviation sequence to obtain the structure parsing quantity. The node width parameter of each transmission node within the underlying Ethernet data structure is extracted, revealing a single node width parameter of 64 bits. This 64-bit width parameter is fed into a multiplier-accumulator and multiplied by the transmission frequency constant allocated to each channel, calculating the theoretical peak data carrying capacity of a single channel to be 512 megabytes per second. This capacity carrying capacity product is recorded in a time-series graph array, projected forward along the time axis to form a 1-hour fluctuation curve projection component. A numerical integrator is invoked, and the trapezoidal integration rule is used to read the projection boundary integration parameter of this fluctuation curve within its upper and lower limits, yielding an integration parameter value of 1850.5. This boundary integration parameter of 1850.5 is fed into a differential logic gate array and subtracted from the system's fixed capacity baseline value of 2000.0, extracting a deviation parameter of -149.5. Multiple results within the time-series window are then assembled to obtain the processed deviation sequence. The 3-layer node communication protocol header feature elements cached in memory are concatenated and spliced ​​strictly according to the distribution order rule of alternating positive and negative values ​​in the deviation sequence, generating a structure parser containing 256 bytes in memory.

[0027] The association mapping comparison submodule, for structural parsing, obtains spatial semantic vectors and highway knowledge graphs, maps structural parsing to addressing indexes, reads the attributes of spatial semantic vectors in the graph, compares the association mapping relationship between multidimensional spatial semantic vectors and highway knowledge graphs, extracts the edge weight parameters of mapping nodes, subtracts the edge weight parameters from the weight benchmark value, selects the node set whose difference falls within the tolerance interval, expands the node set by dimension to measure the Euclidean distance, and generates the mapping distance value; A remote procedure call request is sent to the knowledge platform service cluster to obtain a pre-trained high-dimensional spatial semantic vector model and a highway knowledge graph structure library constructed from nodes and edge relationship entities. The structure resolution is converted into a hexadecimal addressing index code via the graph access interface. Based on this addressing index code, the entity attributes associated with the multi-dimensional spatial semantic vector in the highway knowledge graph, including traffic flow level and event alarm level, are directly located and read. A graph matching algorithm is invoked to compare the association mapping relationship between the high-dimensional spatial semantic vector feature set and the existing entities in the highway knowledge graph, extracting the edge weight parameter representing the association tightness between the matched mapping nodes as 0.85. The edge weight parameter 0.85 is input into the subtraction operator and subtracted from the system's preset ideal weight benchmark value of 1.0, resulting in an edge weight difference value of 0.15. The absolute value comparator is invoked to perform boundary judgment on this difference with the allowable tolerance range of 0.1 to 0.2, selecting a total of 25 candidate nodes whose differences fall exactly within this tolerance range to form a node set. The multidimensional feature matrix of each node in the node set is expanded into a one-dimensional vector along the column dimension and the multinomial Euclidean distance is calculated. After summing the squared differences of each dimension of the vector and taking the square root, a mapping distance value of 8.4 is generated to represent the semantic similarity.

[0028] The maintenance response construction submodule collects maintenance text dictionaries based on mapping distance values, substitutes the mapping distance values ​​into the dictionary addressing array, extracts record parameters under the address bits, filters character encoding elements within the record parameters, concatenates the character encoding elements with the mapping distance values ​​into a joint sequence, extracts character vectors at frequency peaks, reassembles the character vectors according to a fixed byte length, reads the instruction metadata combination features within the reassembled byte block, and constructs highway maintenance response data. The system reads a standardized highway maintenance text dictionary file deployed on the local disk. Using a built-in offset address calculator, it multiplies the mapping distance value of 8.4 by the address mapping constant to generate a physical dictionary addressing array number. This addressing number is then used to read the record parameters containing operation plans and device status codes stored at the corresponding address in the dictionary. A character truncation operator is invoked to filter out control character encoding elements within the record parameters that belong to the unified character encoding standard format. The filtered character encoding elements and floating-point byte data with a mapping distance value of 8.4 are fed into a buffer for byte-level concatenation, generating a joint sequence containing multiple semantic instructions. A frequency statistics algorithm scans this joint sequence to extract the core instruction character vectors corresponding to positions where the frequency reaches a local peak. This character vector is then reassembled and segmented into a standard 16-byte fixed length, removing redundant padding codes. The parsing engine reads the instruction metadata combination features within the reassembled byte blocks, including the protocol version, instruction opcode, and checksum, and converts them into a natural language text stream to construct highway maintenance response data for terminal use.

[0029] Specifically, such as Figure 2 ,6 As shown, the time-series dynamic prediction module includes: The node status parsing submodule extracts business node status record parameters based on highway operation and maintenance response data, calculates node status projection components, extracts the extreme values ​​of the projection components within a set time window, subtracts the extreme values ​​from the set status benchmark threshold, filters the node coordinate index sequence that exceeds the benchmark threshold, arranges the corresponding coordinates of the index sequence in order and splices them into a feature matrix, calculates the intrinsic root values ​​of the feature matrix, and obtains the node status feature values. The text content is parsed using a regular expression matching component to extract service node status record parameters containing device power supply voltage and network connectivity identifiers. These record parameters are then fed into an orthogonal matrix converter to perform matrix multiplication, calculating the node status projection component numerical array in the principal component feature space coordinate system. An extreme value search algorithm is used to iterate through and compare the extracted projection component numerical array within a 5-minute rolling time window, extracting a local maximum extreme value of 24.5. This maximum extreme value of 24.5 is input into a hardware subtractor to subtract a pre-configured state baseline threshold of 18.0, yielding an excess value of 6.5. The state baseline threshold of 18.0 is determined by statistically analyzing the 95th percentile values ​​of the projection components in historical normal operation records. A sequence of service node coordinate indices with a difference greater than 0 and exceeding the baseline threshold is selected. The two-dimensional physical coordinates corresponding to these index sequences are then concatenated into a 5x5 two-dimensional feature matrix, arranged according to a north-to-south and east-to-west orientation rule. The linear algebra library is called to perform singular value decomposition on the 5x5 two-dimensional feature matrix. The maximum intrinsic root value of the feature matrix is ​​calculated to be 12.8, and it is directly assigned as the node state feature value of 12.8 for downstream prediction.

[0030] The hidden layer evolution calculation submodule calls the node state feature values, collects dynamic traffic flow monitoring sequences, merges the dynamic traffic flow monitoring sequences and node state feature values ​​into a monitoring tensor according to the timestamp, substitutes the monitoring tensor into the network hidden layer operation unit, calculates the product of the input gate weight and the forgetting gate weight, accumulates the product sum to generate the hidden layer cell state parameters, calculates the partial derivatives of the hidden layer cell state parameters on the time axis, extracts the central pole elements in the partial derivative distribution array, and establishes the hidden layer evolution trend quantity; The node's state feature value 12.8 is retrieved from the register, and a command is sent to the roadside sensing edge computing unit to collect dynamic traffic flow monitoring time series data at a frequency of 10 Hz. The dynamic traffic flow monitoring sequence data array and the one-dimensional node state feature value 12.8 are combined and merged according to microsecond-level absolute timestamp alignment logic to generate a three-dimensional monitoring tensor with a dimension of 128 steps. This three-dimensional monitoring tensor is substituted into the hidden layer operation unit of the Long Short-Term Memory Neural Network model. Inside the hidden layer of the network, the input gate is first constructed through a fully connected layer and a sigmoid nonlinear activation function to calculate the input gating weights used to determine how much new information to retain. Secondly, the forget gate is constructed through another set of fully connected layers and a sigmoid activation function to calculate the forget gating weights used to determine how much historical information to discard. The input gating weights are multiplied by the candidate state matrix, and the forget gating weights are multiplied by the old cell state of the previous time step, and the product calculation is performed in parallel through a multiplier. The product results of the above two parts are fed into a hardware adder for accumulation calculation, and the accumulated sum is used to generate the hidden layer cell state parameter with a dimension of 256 at the current time step. For the hidden layer cell state parameters, the time dimension axis parameters are extracted, and the partial derivative numerical array of the cell state parameters on the time axis is calculated using the finite difference method. The peak detection function is called to search for the central pole element with the largest slope change in the partial derivative distribution array, and its value is directly extracted to establish a hidden layer evolution trend quantity to characterize the state evolution rate.

[0031] The temporal feature recognition submodule obtains a temporal evolution feature reference sequence for the hidden layer evolution trend quantity, performs a differential comparison operation between the hidden layer evolution trend quantity and the reference sequence, extracts the residual sequence after the differential operation, calculates the power spectral density component of the residual sequence in the frequency domain space, filters feature parameters whose power spectral density components are within the frequency domain reference limit, expands the feature parameters according to the spatial dimension and calculates the average value, multiplies the average value by the time compensation coefficient to generate the bias variable, and generates the traffic prediction result. For the established hidden layer evolution trend, a preset time-series evolution feature reference sequence is retrieved from the cloud-based historical model library. The single-dimensional hidden layer evolution trend is aligned with the long-sequence reference sequence and then fed into a differential comparison logic gate. A bit-by-bit subtraction differential comparison operation is performed to extract the residual sequence after filtering out common-mode trends. A Fast Fourier Transform (FFT) is performed on the residual sequence to convert the time-domain data to the frequency domain, and the square of the amplitude value at each frequency point is calculated to obtain the power spectral density component of the residual sequence in the frequency domain. A digital bandpass comparator is used to filter out qualified feature parameters whose power spectral density components fall within the frequency domain reference limit of 5 watts per hertz to 15 watts per hertz. The qualified feature parameter multidimensional matrix is ​​flattened one-dimensionally according to the latitude and longitude spatial dimensions, and the arithmetic mean is obtained by summing and dividing by the total number of elements, resulting in an average value of 10.2. The average value of 10.2 is input into the multiplier and multiplied by a time compensation factor of 1.5, preset based on the current ambient temperature and network latency, to generate a bias variable with a value of 15.3. The bias variable, containing the frequency domain average state and the time compensation factor, is then encapsulated to generate traffic prediction results for scheduling. The specific configuration and selection of the relevant power spectral density component reference limits are shown in Table 2.

[0032] Table 2: Comparison of Frequency Domain Reference Limits for Power Spectral Density Components 1 500 Hz 12.4 watts per hertz 5.0 watts per hertz 15.0 watts per hertz Parameters are retained if they are qualified. 2 1000 Hz 18.2 watts per hertz 5.0 watts per hertz 15.0 watts per hertz Parameter exceeding limits rejection 3 1500 Hz 8.6 watts per hertz 5.0 watts per hertz 15.0 watts per hertz Parameters are retained if they are qualified. Table 2 shows the logical decision-making process for retaining or removing feature parameters by listing the power spectral density component measurements at different center frequency points and comparing them with the upper and lower limit reference boundaries.

[0033] Specifically, such as Figure 2 , 7 As shown, the strategy adjustment module includes: The response status analysis submodule, based on traffic prediction results, collects the execution record matrix of the highway digital platform, merges the traffic prediction results and the execution record matrix according to timestamps, calculates the projection components of the merged matrix in the time dimension, extracts the response delay parameters within the projection components, compares the response delay parameters with the set response delay benchmark value, extracts the delay sequence that exceeds the benchmark value, accumulates the delay sequences to obtain the response offset sum, and divides the response offset sum by the total number of platform nodes to obtain the execution response status quantity; The system requests and downloads an execution record matrix containing the task issuance and completion times of each functional node from the service bus of the backend highway digital platform. The traffic prediction result array and the execution record matrix are aligned using timestamp data generated from the same clock source and then horizontally merged to form a merged matrix with a related dimension. A matrix mapping algorithm is used to calculate the projection components of the merged matrix on the time-series coordinates, and response delay parameters representing end-to-end instruction transmission and processing delays are extracted from these projection components. Each extracted response delay parameter is fed into a digital subtractor and compared to a platform-defined 100-millisecond response delay baseline. All abnormal delay data exceeding the 100-millisecond baseline limit are extracted to form a delay sequence. The sum of all abnormal delay milliseconds in the delay sequence is obtained by summing them using an accumulator register. This sum of response offsets is then fed into a divider and divided by the total number of active nodes (20) to calculate the average delay response state of a single node.

[0034] The cognitive benchmark extraction submodule calls the execution response state quantity to obtain the interactive feedback observation sequence. The execution response state quantity is used as a weighting coefficient to multiply by the interactive feedback observation sequence to extract a weighted observation array. The multi-dimensional positive Euclidean distance of the elements of the weighted observation array is calculated. The extreme value parameter of the Euclidean distance within a set window is extracted. The extreme value parameter is input into the differencer to calculate the second derivative. The coordinate index of the second derivative tends to zero is extracted. The local feedback feature set is truncated according to the coordinate index and the mean is calculated to generate the cognitive evaluation benchmark value. The corrected Euclidean distance is calculated using the following formula: ; Where D represents the corrected Euclidean distance, N represents the total number of sampling points, i represents the sorting index variable of the sampling points, and w represents the weight coefficient corresponding to the execution response state quantity. This represents the observed value at the i-th sampling point. b represents the reference value corresponding to the i-th sampling point, and b represents the dynamic observation compensation bias parameter. The normalized scaling factor representing the elements of the weighted observation array. A scaling factor representing the deviation between the interactive feedback observation sequence and the reference sequence. The constant representing the distribution range adjustment of the elements in the weighted observation array; In the specific implementation scenario, the total number of sampling points N is set to 3. For the first sampling point with sorting index variable i=1, the system extracts its observed value. The value is 4.0, which corresponds to the baseline reference value. The value is 2.0. The difference is calculated using a subtractor to obtain the deviation variable, which is 2.0. The system then multiplies this deviation variable by a predefined scaling factor. (Set to 1.5), multiplying gives 3.0; inputting into the square calculator yields a squared result of 9.0; then multiplying it by the adjustment constant. (Set to 2.0), the first local adjustment square term is 18.0. For the second sampling point of the sorting index variable i=2, the observed value... The value is 5.0, which is the baseline reference value. The value is 4.0. The difference is taken to obtain a deviation variable of 1.0; multiplied by the scaling factor... (Both are 1.5), multiplying them gives 1.5; squaring themselves gives 2.25; multiplying by the adjustment constant gives 2.25. (Both are 2.0), resulting in a local adjustment square term of 4.5 for the second term. For the third sampling point with sorting index variable i=3, the observed values... The reference value is 6.0. The result is 6.0. Subtracting this value yields 0.0. Similarly, the third local adjustment square term is calculated to be 0.0. The system's internal accumulator performs a global summation of the local adjustment square terms obtained from the above three traversal operations, resulting in a value of 22.5. The system then multiplies this summation value of 22.5 by the normalization scaling factor. (Configured to 0.4) The magnitude was adjusted to 9.0. Next, the system subtracted the bias parameter b (set to 1.0) calculated based on the no-load condition, resulting in 8.0. Finally, the system multiplied this value 8.0 by the previously calculated weight coefficient w (set to 1.2 based on network throughput priority), thus calculating the precise corrected Euclidean distance D to be 9.6; The advantage of this operational logic is that by introducing a scaling factor for the deviation between the interactive feedback observation sequence and the benchmark sequence, as well as a constant for adjusting the distribution range of the weighted observation array elements, it can significantly amplify the feature response amplitude of core key feature nodes in the case of uneven distribution of multidimensional data. At the same time, it uses dynamic observation compensation bias parameters to perform subtraction operations to filter out the noise interference of the underlying communication, thereby improving the signal-to-noise ratio and convergence stability of feature distance measurement in complex business parallel scenarios.

[0035] The scheduling strategy update submodule extracts the frequency of the difference parameter in the spatial domain based on the cognitive evaluation benchmark value, compares the distribution frequency with the set frequency threshold, collects the platform scheduling resource inventory parameter, performs a dot product operation on the deviation morphological feature quantity and the inventory parameter to calculate the resource consumption rate, subtracts the resource consumption rate from the set rate limit, extracts the instruction code corresponding to the extreme point in the difference sequence, and generates the scheduling update strategy. A call request is sent to the infrastructure management platform to collect platform scheduling resource inventory parameters, including the remaining number of CPU cores and memory capacity of the current cloud server. The deviation morphological characteristic quantity, represented by a scalar, is input into the digital dot product unit along with the inventory parameter array to calculate the dynamic resource consumption rate of the current system state. This resource consumption rate value is then fed into a subtractor to subtract a set safe resource consumption rate limit value. The device recovery instruction code corresponding to the point where the difference is greater than zero and at an extreme value is extracted from the subtracted difference sequence. Based on the extracted device recovery instruction code, a scheduling update strategy for automatic resource allocation is directly generated. Specific test data related to the platform resource scheduling instruction parameters are shown in Table 3. Table 3: Implementation Record of Platform Resource Scheduling and Update Strategy 1 128 gigabytes 15.0 gigabytes per hour 12.5 gigabytes per hour No limit exceeded, no extreme value Null instruction hold 2 64 gigabytes 15.0 gigabytes per hour 18.2 gigabytes per hour Exceeding the limit of 3.2 extreme values Release memory cache 3 32 gigabytes 15.0 gigabytes per hour 22.5 gigabytes per hour Exceeding the limit of 7.5 extreme values Forcefully terminate the suspended process As shown in Table 3, the decision-making process data of the platform under different available memory capacities is listed in detail. This process involves calculating the resource consumption rate and comparing the limit parameters to extract extreme states and generate corresponding device recovery command codes.

[0036] Please see Figure 8 The adaptive cognitive enhancement-based intelligent digital interaction method for highways is implemented based on the aforementioned adaptive cognitive enhancement-based intelligent digital interaction system for highways, and includes the following steps: S1: Collect natural language commands and interface context features from highway monitoring terminals, construct a multimodal intent classification set, construct a cognitive matching sequence of the multimodal intent classification set in the business scenario, determine the logical compliance status of the cognitive matching sequence, and construct structured intent judgment features; S2: Analyze the probability distribution pattern of structured intent judgment features, calculate the information entropy distribution density of associated cognitive credibility identifiers, identify the node mapping associated information of information entropy distribution density, and construct multi-level business channels; S3: Analyze the data structure of multi-level business channels, compare the association mapping relationship between multi-dimensional spatial semantic vectors and highway knowledge graphs, and construct highway operation and maintenance response data; S4: Analyze the status of business nodes included in the highway operation and maintenance response data, calculate the hidden layer evolution trend of the dynamic traffic flow monitoring sequence in the long short-term memory network, identify the temporal evolution characteristics, and generate traffic prediction results; S5: Analyze the execution response status of traffic forecast results on the highway digital platform, calculate the cognitive evaluation benchmark of the interactive feedback observation sequence, identify the deviation distribution pattern, and generate scheduling update strategies.

[0037] The above are merely specific embodiments of the present invention, but the scope of protection of the present invention is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in the present invention should be included within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of protection of the described technical solutions.

Claims

1. An adaptive cognitive-enhanced digital intelligent interactive system for highways, characterized in that, The system includes: The multimodal intent parsing module acquires natural language commands and interface context features from the highway monitoring terminal. The interface context features include the absolute pixel coordinates of interactive buttons on the display screen, millisecond-level timestamp records, and hexadecimal operation codes. It constructs a multimodal intent classification set, constructs a cognitive matching sequence of the multimodal intent classification set in the business scenario, judges the logical compliance status of the cognitive matching sequence, and constructs structured intent judgment features including operation behavior attributes, scene parameter entities, and permission rule identifiers. The credibility measurement and scheduling module analyzes the probability distribution pattern of the structured intent judgment features, calculates the information entropy distribution density of the associated cognitive credibility identifier, identifies the node mapping association information of the information entropy distribution density, and constructs a multi-level business channel, including fault-tolerant transmission links, emergency response buses, and regular scheduling interfaces. The semantic alignment retrieval module analyzes the data structure of the multi-level business channels, compares the association mapping relationship between the multi-dimensional spatial semantic vector and the highway knowledge graph, and constructs highway operation and maintenance response data, including fault diagnosis plans, facility maintenance guidance and alarm cancellation scripts. The time-series dynamic prediction module analyzes the status of business nodes included in the highway operation and maintenance response data, calculates the hidden layer evolution trend of the dynamic traffic flow monitoring sequence in the long short-term memory network, identifies the time-series evolution characteristics, and generates traffic prediction results, including congestion probability index, average vehicle speed, and peak emergence time. The strategy adjustment module analyzes the execution response status of the traffic prediction results on the highway digital platform, calculates the cognitive evaluation benchmark of the interactive feedback observation sequence, identifies the deviation distribution pattern, and generates scheduling update strategies, including resource allocation weights, traffic light control plans, and guidance release criteria. The multimodal intent parsing module includes: The semantic classification extraction submodule acquires the natural language commands and interface context features of the highway monitoring terminal, converts the natural language commands into lexical tensors, extracts the interface context features associated with coordinate points, inputs the lexical tensor and coordinate points into a multiplier to perform a dot product operation, calculates the tensor inner product of the lexical tensor and coordinate points under the timestamp, maps the tensor inner product to the semantic classification vector space, measures the Euclidean distance of the tensor inner product in the classification vector space, and obtains the intent distribution hash value. The business scenario comparison submodule calls the intent distribution hash value, collects business scenario feature attribute parameters, assembles the intent distribution hash value and feature attribute parameters into a cognitive matching sequence according to time, calculates the data offset difference of the cognitive matching sequence at the business feature nodes, sums the data offset differences at the business feature nodes, subtracts the result of the offset difference from the sequence offset benchmark threshold, and calculates the proportion of the number of nodes exceeding the benchmark threshold in the total number of global nodes to generate the cognitive matching deviation rate. The logical compliance determination submodule, for the cognitive matching deviation rate, sends the cognitive matching deviation rate into a comparator and compares it with the preset compliance benchmark interval boundary line, filters out abnormal discrete points located outside the benchmark interval, extracts the timestamp index sequence associated with the abnormal discrete points, extracts the device operation record array along the timestamp index, and concatenates the operation record array with the cognitive matching deviation rate into a structured intent tensor to obtain the structured intent determination feature. The semantic alignment retrieval module includes: The data structure parsing submodule extracts the node bit width parameter within the data structure based on the multi-level business channels, calculates the capacity carrying product, extends and projects the capacity carrying product along the time axis, reads the projection boundary integral parameter, performs a difference operation between the boundary integral parameter and the capacity reference value, extracts the processed deviation sequence, and splices multi-layer node feature elements according to the distribution order of the deviation sequence to obtain the structure parsing quantity. The association mapping comparison submodule, for the structural parsing quantity, obtains the spatial semantic vector and the highway knowledge graph, maps the structural parsing quantity to the addressing index, reads the attributes of the spatial semantic vector in the graph, compares the association mapping relationship between the multidimensional spatial semantic vector and the highway knowledge graph, extracts the edge weight parameters of the mapping nodes, subtracts the edge weight parameters from the weight benchmark value, selects the node set whose difference falls within the tolerance interval, expands the node set by dimension to measure the Euclidean distance, and generates the mapping distance value; The maintenance response construction submodule collects maintenance text dictionaries based on the mapped distance values, substitutes the mapped distance values ​​into the dictionary addressing array, extracts record parameters under the address bits, filters character encoding elements within the record parameters, concatenates the character encoding elements with the mapped distance values ​​into a joint sequence, extracts character vectors at frequency peaks, reassembles the character vectors according to a fixed byte length, reads the instruction metadata combination features within the reassembled byte block, and constructs highway maintenance response data.

2. The adaptive cognitive enhancement-based intelligent digital interaction system for highways according to claim 1, characterized in that, The credibility measurement scheduling module includes: The distribution morphology quantification submodule, based on the structured intent determination features, collects a time-series operation record array, extracts the intent occurrence frequency within the time-series operation record array, performs a dot product operation on the structured intent determination features and the intent occurrence frequency, calculates the feature parameters' projection components in multi-dimensional coordinates, reads the discrete variance value of the projection components within a set window, compares the discrete variance value with a set morphology benchmark value, extracts the variance variable sequence within the benchmark value limit, accumulates the variance sequences to obtain the sum, and generates the distribution morphology coefficient. The information entropy calculation submodule calls the distribution morphology coefficient, collects the credibility dictionary set, substitutes the distribution morphology coefficient into the dictionary set to perform key-value indexing, extracts the matching real-time condition identifier matrix, calculates the multi-element partial derivative values ​​of the identifier matrix, maps the partial derivative values ​​to occurrence probability values, calculates the logarithmic self-information of the occurrence probability values, multiplies the self-information with the occurrence probability values, sums them and performs an inverse operation, quantifies the information entropy values ​​within the interval, and divides the information entropy values ​​by the spatial step size parameter to obtain the information entropy density value. The node mapping submodule collects topology node clusters for the information entropy density value, injects the information entropy density value into the routing links of the node cluster, measures the communication attenuation of the routing links, filters available link nodes whose communication attenuation is within the attenuation benchmark limit, summarizes the address codes of available nodes, performs hierarchical sorting of the address codes according to the information entropy density value sorting rules, allocates service frequency band resources and configures capacity, and constructs multi-level service channels.

3. The adaptive cognitive enhancement-based intelligent interactive system for digital highways according to claim 2, characterized in that, The process of extending and projecting the capacity-bearing product along the time axis and reading the integral parameters of the projection boundary is as follows: extracting the mean value of the capacity-bearing product on the time axis, dividing the time axis into discrete time slices, and accumulating the mean value of the capacity-bearing product along the discrete time slices to construct the time-bearing surface; The highest and lowest projection points on the positioning time bearing surface are located, and a closed geometric region is constructed by connecting the highest and lowest projection points. The cumulative area value is calculated within the closed geometric region, and the cumulative area value is extracted as the projection boundary integral parameter.

4. The adaptive cognitive enhancement-based intelligent digital interaction system for highways according to claim 3, characterized in that, The time-series dynamic prediction module includes: The node status parsing submodule extracts business node status record parameters based on the highway operation and maintenance response data, calculates the node status projection components, extracts the extreme values ​​of the projection components within a set time window, subtracts the extreme values ​​from the set status benchmark threshold, filters the node coordinate index sequence that exceeds the benchmark threshold, arranges the corresponding coordinates of the index sequence in order and splices them into a feature matrix, calculates the intrinsic root values ​​of the feature matrix, and obtains the node status feature values. The hidden layer evolution calculation submodule calls the node state feature values, collects dynamic traffic flow monitoring sequences, merges the dynamic traffic flow monitoring sequences and node state feature values ​​into a monitoring tensor according to timestamps, substitutes the monitoring tensor into the network hidden layer operation unit, calculates the product of input gate weights and forget gate weights, accumulates the product sum to generate hidden layer cell state parameters, calculates the partial derivatives of the hidden layer cell state parameters on the time axis, extracts the central pole elements in the partial derivative distribution array, and establishes the hidden layer evolution trend quantity. The temporal feature recognition submodule acquires a temporal evolution feature reference sequence for the hidden layer evolution trend quantity, performs a differential comparison operation between the hidden layer evolution trend quantity and the reference sequence, extracts the residual sequence after the differential operation, calculates the power spectral density component of the residual sequence in the frequency domain space, filters feature parameters whose power spectral density components are within the frequency domain reference limit, expands the feature parameters according to the spatial dimension and calculates the average value, multiplies the average value by the time compensation coefficient to generate a bias variable, and generates traffic prediction results.

5. The adaptive cognitive enhancement-based intelligent digital interaction system for highways according to claim 1, characterized in that, The strategy adjustment module includes: The response status analysis submodule, based on the traffic prediction results, collects the execution record matrix of the highway digital platform, merges the traffic prediction results and the execution record matrix according to the timestamp, calculates the projection component of the merged matrix in the time dimension, extracts the response delay parameter within the projection component, compares the response delay parameter with the set response delay benchmark value, extracts the delay sequence that exceeds the benchmark value, accumulates the delay sequences to obtain the response offset sum, and divides the response offset sum by the total number of platform nodes to obtain the execution response status quantity; The cognitive benchmark extraction submodule calls the execution response state quantity to obtain the interactive feedback observation sequence. It uses the execution response state quantity as a weighting coefficient to multiply the interactive feedback observation sequence to extract a weighted observation array. It measures the Euclidean distance between the elements in the weighted observation array, extracts the extreme value parameter of the Euclidean distance within a set window, inputs the extreme value parameter into a differencer to calculate the second derivative, extracts the coordinate index where the second derivative approaches zero, extracts the local feedback feature set according to the coordinate index and calculates the mean, and generates a cognitive evaluation benchmark value. The scheduling strategy update submodule extracts the distribution frequency of the difference parameter in the spatial domain based on the cognitive evaluation benchmark value, compares the distribution frequency with the set frequency threshold, collects the platform scheduling resource inventory parameter, performs a dot product operation on the deviation morphological feature quantity and the inventory parameter to calculate the resource consumption rate, subtracts the resource consumption rate from the set rate limit, extracts the instruction code corresponding to the extreme point in the difference sequence, and generates the scheduling update strategy.

6. The adaptive cognitive enhancement-based intelligent digital interaction system for highways according to claim 5, characterized in that, The process of aligning and merging traffic prediction results with the execution record matrix by timestamp and calculating the projection components of the merged matrix in the time series dimension is as follows: extract the prediction timestamps included in the traffic prediction results, extract the actual timestamps included in the execution record matrix, compare the prediction timestamps with the actual timestamps one by one, remove isolated time points that failed to match, and splice the data corresponding to the retained matching time points to generate the merged matrix. Numerical accumulation is performed along the time-series axis of the merge matrix to construct an accumulated numerical sequence, and the accumulated numerical sequence is extracted as a time-series dimension projection component.

7. An adaptive cognitive enhancement-based digital intelligent interaction method for highways, characterized in that, The adaptive cognitive enhancement highway digital intelligent interaction system according to any one of claims 1-6 includes the following steps: S1: Collect natural language commands and interface context features from highway monitoring terminals, construct a multimodal intent classification set, construct a cognitive matching sequence of the multimodal intent classification set in the business scenario, determine the logical compliance status of the cognitive matching sequence, and construct structured intent judgment features; S2: Analyze the probability distribution pattern of the structured intent judgment features, calculate the information entropy distribution density of the associated cognitive credibility identifier, identify the node mapping associated information of the information entropy distribution density, and construct a multi-level business channel; S3: Analyze the data structure of the multi-level business channels, compare the association mapping relationship between the multi-dimensional spatial semantic vector and the highway knowledge graph, and construct highway operation and maintenance response data; S4: Analyze the status of business nodes included in the highway operation and maintenance response data, calculate the hidden layer evolution trend of the dynamic traffic flow monitoring sequence in the long short-term memory network, identify the temporal evolution characteristics, and generate traffic prediction results; S5: Analyze the execution response status of the traffic prediction results on the highway digital platform, calculate the cognitive evaluation benchmark of the interactive feedback observation sequence, identify the deviation distribution pattern, and generate a scheduling update strategy.

Images