Multi-model collaborative cross-domain intelligent management service system and method

Abstract

The invention provides a multi-model collaborative cross-domain intelligent management service system and method, and relates to the technical field of cross-domain resource collaboration.The multi-model collaborative cross-domain intelligent management service system comprises a terminal node layer and a data fusion layer which are deployed on edge equipment, and a collaborative model layer and a central scheduling layer which are deployed on a cloud server; the data fusion layer integrates multi-field original data and provides data for the collaborative model layer; the collaborative model layer constructs a collaborative evolution matrix of a cloud parent model-terminal sub-model, and the terminal sub-model performs personalized training based on terminal node data and outputs an encryption parameter increment to the cloud parent model to aggregate and generate a group reference model; based on the real-time collected data and the group reference model, a progressive decision-making mechanism is fused to obtain a decision-making result; and the central scheduling layer triggers a service based on a decision result of the collaborative model layer and responds to a service request initiated by a user. The method solves the problems that an existing system is poor in architecture collaboration, unbalanced in privacy and efficiency and fixed in decision-making ability, and has high universality, expandability and evolutionary performance.

Description

Technical Field

[0001] This invention relates to the field of cross-domain resource collaboration technology, and to a multi-model collaborative cross-domain intelligent management service system and method. Background Technology

[0002] Existing intelligent management service systems share common limitations in the fields of healthcare management, education assessment management, and autonomous driving assistance management, including: (1) Poor architectural coordination: It often adopts a linear structure of "data-model-application", with local processing and cloud coordination being disconnected, making it difficult to achieve efficient cross-level resource scheduling; (2) The contradiction between privacy and efficiency: Uploading raw data to the cloud in a centralized manner leads to the risk of privacy leakage, while pure local processing lacks the support of group patterns and is not accurate enough; (3) Passive service model: It relies on users to take initiative, cannot actively trigger services based on real-time data, and has a weak ability to handle ambiguous needs (such as dialect expression and ambiguous road condition recognition). (4) Insufficient cross-domain adaptability: Designed for a single domain, core technologies (such as model training and data transfer) are difficult to transfer to other scenarios, resulting in low reusability; (5) Solidified decision-making ability: It relies heavily on fixed rules or static models and cannot evolve from "auxiliary tool" to "expert-like decision-making" through the accumulation of knowledge by multiple subjects. It is difficult to cope with the dynamic needs of complex scenarios (such as driving in extreme weather and responding to sudden road conditions).

[0003] To address the aforementioned issues, this invention proposes a general architecture and implementation method that supports multi-domain reuse, namely "layered collaboration + model evolution + proactive service + progressive decision-making". Summary of the Invention

[0004] To address the aforementioned issues, this invention provides a cross-domain intelligent management service system and method for multi-model collaboration, realizing a general intelligent management service that integrates multi-source data and protects privacy, promotes multi-model collaborative evolution, proactive service scheduling, and the accumulation of incremental decision-making capabilities.

[0005] To achieve the above objectives, the present invention provides a cross-domain intelligent management service method for multi-model collaboration, comprising: a terminal node layer and a data fusion layer deployed on edge devices, and a collaborative model layer and a central scheduling layer deployed on cloud servers; The data fusion layer is used for: Integrate raw data from multiple fields, perform desensitization processing, timeliness management, and feature transformation on the raw data, and provide data for the collaborative model layer; The collaborative model layer is used for: A co-evolutionary matrix of cloud-based parent model and terminal sub-model is constructed, including a domain-specific model, a general model, terminal sub-models, and a cloud-based parent model. The terminal sub-models are trained in a personalized manner based on terminal node data and output encrypted parameter increments to the cloud-based parent model. The cloud-based parent model integrates the parameter increments from multiple terminals to generate a group benchmark model. Based on the data provided by the data fusion layer and the group benchmark model, a progressive decision-making mechanism is integrated to obtain the model output results and decision results. The central scheduling layer is used for: Based on the decision results of the collaborative model layer, services are triggered to respond to service requests initiated by users and to provide multi-terminal collaborative responses. Integrate resources from multiple terminal nodes, match the optimal service path based on the model output results of the collaborative model layer, perform resource scheduling, and generate service notifications. The terminal node layer is used for: Each terminal node provides a service interaction entry point. Based on the user node, the raw data is collected in real time and uploaded to the data fusion layer via an encrypted network. It receives service notifications from the central scheduling layer and conducts proactive dialogue interaction. Based on the professional service node, domain services are executed. Based on the resource support node, supporting resources are provided. Based on the management node, domain-specific configurations are supported.

[0006] As a further improvement of the present invention, the terminal node layer is provided with a multimodal acquisition module, which collects scene data in real time through multimodal sensors and uploads it to the data fusion layer.

[0007] As a further improvement of the present invention, the data fusion layer includes a multi-source data access module, a desensitization and parameterization module, and a data freshness management module; The multi-source data access module receives heterogeneous data uploaded by the terminal node layer; The desensitization and parameterization module sets desensitization rules, performs desensitization processing on the heterogeneous data, and maps the desensitized heterogeneous data to domain feature parameters. The data freshness management module sets a domain freshness time threshold, caches real-time data through an edge caching mechanism, and quickly indexes the data through timestamps to obtain real-time data within the domain freshness time threshold.

[0008] As a further improvement of the present invention, the collaborative model layer includes a scenario-based model matrix, a terminal sub-model, and a cloud-based parent model; The scenario-based model matrix includes domain-specific models and general models. The general models include an active dialogue strategy model, a resource matching model, and a progressive decision-making capability evolution model. The terminal sub-model adopts a lightweight model and is deployed on each terminal node. It is personalized based on the data of each terminal node and outputs encrypted parameters incrementally to the cloud master model. The cloud-based master model integrates the parameter increments of multiple terminal nodes by weighting the sample size according to the federated aggregation algorithm, thereby generating a population benchmark model.

[0009] As a further improvement of the present invention, the proactive dialogue strategy model includes: accurately identifying fuzzy data based on confidence or information entropy, triggering proactive dialogue for fuzzy data, setting a simplified interaction method for high-risk scenarios, and setting multi-round guided dialogue for low-risk scenarios.

[0010] As a further improvement of the present invention, the collaborative model layer also includes an active learning algorithm; The active learning algorithm improves accuracy for fuzzy data through an uncertainty judgment-directional interaction-parameter optimization process. The uncertainty judgment is based on confidence or information entropy to trigger interaction, and the judgment result is obtained based on the interaction. The gradient descent algorithm is used to update the weights of the terminal sub-model based on the judgment result to achieve parameter optimization of the terminal sub-model.

[0011] As a further improvement of the present invention, the central scheduling layer is also provided with an access control and compliance management module and a service quality monitoring and optimization module; The permission and compliance control module divides role permissions based on the RBAC model and verifies the compliance of data calls in real time. The service quality monitoring and optimization module monitors service response speed, accuracy, and customer satisfaction, and automatically triggers parameter optimization of the corresponding model in the collaborative model layer for low-quality services.

[0012] As a further improvement of the present invention, the collaborative model layer includes a progressive decision-making capability evolution module, which includes a multi-subject knowledge annotation sub-module, a ladder-style decision-making capability sub-module, and a cross-domain knowledge transfer sub-module. The multi-agent knowledge annotation submodule is used for: It supports real-time annotation of decision-making basis by all roles at the terminal node layer. The annotation data includes decision-making actions, logical explanations, and scene tags. The tiered decision-making capability submodule is used for: A third-order threshold for the evolution of decision-making ability is defined. The service quality monitoring module of the central scheduling layer dynamically evaluates the decision accuracy and human intervention rate, and compares them with the third-order threshold to determine whether the current evolution has reached the assisted decision-making stage, the semi-autonomous decision-making stage, or the autonomous decision-making stage. The cross-domain knowledge transfer submodule is used for: Extract common features of decision-making logic from different domains and construct a cross-domain decision-making knowledge graph.

[0013] As a further improvement of the present invention, the service quality monitoring module of the central scheduling layer dynamically evaluates the decision accuracy rate and the rate of human intervention, compares them with a third-order threshold, and determines whether the current evolution has reached the assisted decision-making stage, the semi-autonomous decision-making stage, or the autonomous decision-making stage; including: The assisted decision-making stage must meet the requirement that the matching degree between the decision scheme and the annotation rules is ≥90%, and must be executed after manual review; the semi-autonomous decision-making stage must meet the requirement that the decision scheme covers 70% of the common scenarios, and that manual intervention is required for complex scenarios; the autonomous decision-making stage must meet the requirement that the decision accuracy rate is ≥95%, and can independently generate traceable reports that conform to the domain specifications.

[0014] This invention also provides a method for cross-domain intelligent management services with multi-model collaboration, including: Each terminal node in the terminal node layer collects raw data and transmits it to the data fusion layer, where the data fusion layer performs desensitization and parameterization processing on the raw data. Each terminal sub-model in the collaborative model layer generates personalized results based on local parameters and the original data of the terminal node it belongs to, and the encrypted parameters are incrementally uploaded to the cloud parent model. The cloud-based master model aggregates the incremental parameters of multiple terminals to generate a group pattern, and feeds it back to the sub-models of each terminal; The central scheduling layer triggers services based on the output of the cloud-based master model or user requests, and schedules terminal node resources to execute services. Terminal nodes provide service results, and the central scheduling layer monitors service quality and triggers model optimization. The progressive decision-making capability evolution module of the collaborative model layer evolves in real time and dynamically upgrades decision-making capabilities through multi-subject knowledge annotation, step-by-step capability iteration, and cross-domain knowledge transfer.

[0015] Compared with the prior art, the beneficial effects of the present invention are as follows: This invention comprises a data fusion layer, a collaborative model layer, a central scheduling layer, and a terminal node layer. Through a "local processing + cloud collaboration" architecture, it achieves multi-source data privacy protection and efficient utilization. It employs a "cloud-based parent model - terminal child model" federated evolutionary model and an active learning algorithm, balancing personalized services with group pattern analysis. It innovatively introduces a progressive decision-making capability evolution module, achieving dynamic upgrades from "assisted decision-making" to "autonomous decision-making" through multi-subject knowledge annotation, tiered capability iteration, and cross-domain knowledge transfer, particularly suitable for the real-time and high-risk requirements of safe driving scenarios. It supports adaptation to multiple fields such as healthcare, education, and safe driving, improving service accuracy and response speed through proactive service triggering and cross-domain resource scheduling. This invention solves problems such as poor collaboration, imbalance between privacy and efficiency, and rigid decision-making capabilities in existing system architectures, exhibiting strong versatility, scalability, and evolutionary potential.

[0016] In this invention, the raw data collected based on the terminal node layer is processed only locally on the terminal. Only the encrypted parameter increment is uploaded, which clarifies the requirements through dialogue and avoids privacy leakage. This local clarification + cloud aggregation mode improves the accuracy of group pattern recognition.

[0017] The incremental decision-making mechanism provided in this invention achieves long-term accumulation and dynamic upgrading of decision-making capabilities through multi-agent knowledge injection, step-by-step capability iteration, and cross-domain knowledge transfer. Compared with traditional intelligent systems that rely on static models or preset rules and cannot cope with the dynamic needs of complex scenarios, and whose decision-making logic is opaque, this invention defines a three-order evolution threshold of "assisted-semi-autonomous-autonomous". Combined with "multi-agent knowledge annotation", it transforms the tacit professional knowledge of various fields and roles into explicit rules that the system can learn and injects into the decision-making model, solving the problem that "algorithms rely only on data statistics and lack humanistic logic". Through the evolution of decision-making capabilities, it breaks through the limitations of "fixed rules" in traditional systems and achieves a leap from "tool assistance" to "expert-like decision-making" through long-term knowledge accumulation, thereby improving decision-making accuracy. Through cross-domain logic adaptation, common decision-making logic is transferred and reused, reducing the access cost of new fields.

[0018] The progressive decision-making mechanism provided in this invention generates a "rule chain visualization report" at each decision-making stage, clearly defining the decision basis and its connection to domain compliance. Simultaneously, it verifies the exception handling logic through "counterfactual simulation" (e.g., "ignoring the fact that an oversized truck would increase the collision risk by 40%), thus overcoming the trust bottleneck of AI's "black box decision-making," making it particularly suitable for liability determination needs in high-risk fields such as healthcare and driving.

[0019] The proactive dialogue and incremental decision-making provided in this invention, compared to traditional services that rely on users to actively initiate requests (such as clicking buttons or entering commands) and are difficult to predict, achieves proactive service. Specifically, proactive dialogue, combined with "data freshness management," clarifies needs in advance before users are aware of them, while incremental decision-making proactively pushes preventative solutions through group pattern mining. This "proactive perception-demand prediction-dynamic adaptation" service model reduces response delays and improves user satisfaction.

[0020] The proactive dialogue and progressive decision-making mechanism provided in this invention breaks through the core limitations of traditional intelligent systems, such as "passive response, rigid decision-making, and weak cross-domain adaptability." Through the reconstruction of the technical mechanism, it achieves natural interaction, evolutionary decision-making, and universal scenario application.

[0021] This invention provides a cross-domain knowledge transfer sub-module. Compared to traditional intelligent systems that require separate development of interaction and decision-making modules for each domain, resulting in high development costs and low reusability, this invention's two modules achieve cross-domain value through "common feature extraction + knowledge graph transfer," realizing a shift from "scenario-specific development" to "common logic transfer" and reducing adaptation costs.

[0022] This invention reconstructs intelligent systems from "mechanical tools" into "communicative, learning, and trustworthy" cross-domain service entities through quantitative triggering, evolutionary mechanisms, and cross-domain migration. It not only solves the pain points of existing technologies (weak fuzzy processing, rigid decision-making, and low reusability) but also provides a universal solution for the implementation of intelligent services in multiple fields. Attached Figure Description

[0023] Figure 1 This is a diagram of a cross-domain intelligent management service system architecture for multi-model collaboration, as disclosed in one embodiment of the present invention. Detailed Implementation

[0024] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0025] The present invention will now be described in further detail with reference to the accompanying drawings: like Figure 1 As shown, the present invention provides a cross-domain intelligent management service method for multi-model collaboration, comprising: a terminal node layer and a data fusion layer deployed on edge devices, and a collaborative model layer and a central scheduling layer deployed on cloud servers; (1) Data fusion layer, used for: Integrate raw data from multiple fields, perform data anonymization, timeliness management, and feature transformation on the raw data, and provide data for the collaborative model layer; in, The data fusion layer includes a multi-source data access module, a de-identification and parameterization module, and a data freshness management module; The multi-source data access module receives heterogeneous data uploaded by the terminal node layer, such as: for the health field, vital sign data (blood sugar, heart rate), dialect voice symptom descriptions, medical records, etc.; for the education field, answer records, classroom interaction data, feedback on learning difficulties (such as "can't do it" or "didn't understand"), etc.; for the safe driving field, vehicle status data (vehicle speed, brake pedal travel, steering wheel angle, tire pressure), environmental perception data (LiDAR point cloud, camera images, millimeter-wave radar obstacle information), and interactive data (user voice command "construction ahead", temporary traffic control information issued by traffic managers); the data format supports structured (numerical, labeled) and unstructured (voice, text, image).

[0026] The desensitization and parameterization module sets desensitization rules to desensitize heterogeneous data and maps the desensitized heterogeneous data to domain feature parameters. Specifically, for the health domain, desensitization rules include removing "medical record ID and gene identifier" from health data; for education data, removing "student name and school code"; and for the safe driving domain, removing vehicle VIN codes and precise geographical locations from driving data (retaining them at the "regional road segment" granularity), and anonymizing "specific user vehicle" as "terminal ID_XXX". The mapping of the desensitized heterogeneous data to domain feature parameters is a parameterization transformation. Examples for the health domain include: "blood glucose 8.5 mmol / L → metabolic abnormality parameter"; for the education domain, "answering questions incorrectly → weak knowledge point parameter"; and for the safe driving domain, "vehicle speed 80 km / h + distance to the vehicle in front 50m → following safety factor 0.6" and "heavy rain + camera obstruction rate 70% → high environmental risk level".

[0027] The data freshness management module sets domain freshness time thresholds and caches real-time data through an edge caching mechanism. It also uses timestamps to quickly index the data, retrieving real-time data within the domain freshness time threshold. For example, in real-time scenarios (such as emergency care, classroom interaction, and driving at urban intersections), real-time data within 5 minutes (health), 30 seconds (education), or 100ms (high-risk driving scenarios) is retrieved, ensuring timeliness through timestamp indexing and edge caching. In non-real-time scenarios (such as chronic disease management, homework grading, and highway cruising in open areas), staggered uploads are used (e.g., in education scenarios, homework submissions are uploaded in batches by class during peak evening study periods; in driving scenarios, low-risk driving data is uploaded in batches by hour) to reduce resource consumption.

[0028] (2) Collaborative model layer, used for: A co-evolution matrix of cloud-based parent model and terminal sub-model is constructed, including domain-specific model, general model, terminal sub-model and cloud-based parent model. The terminal sub-model is trained in a personalized manner based on terminal node data and outputs encrypted parameter increments to the cloud-based parent model. The cloud-based parent model integrates the parameter increments from multiple terminals to generate a group benchmark model. Based on the data provided by the data fusion layer and the group benchmark model, an incremental decision-making mechanism is integrated to obtain the model output results and decision results. in, The collaborative model layer includes a scenario-based model matrix, terminal sub-models, and a cloud-based parent model; The scenario-based model matrix includes domain-specific models and general models. Domain-specific models include: a vital sign warning model and a medication recommendation model for the health field; an academic assessment model and a learning resource matching model for the education field; and a driving style adaptation model and a local risk warning model (calculating collision time TTC and minimum safe distance) for the safe driving field. General models include an active dialogue strategy model (handling dialects and fuzzy feedback), a resource matching model, and an evolutionary decision-making ability model. The terminal sub-model adopts a lightweight model (such as the MobileNet architecture) and is deployed on each terminal node. It is trained in a personalized manner based on the local authorized data of each terminal node (such as adjusting the "dialect-semantics" mapping weight, the "answer error-knowledge blind spot" association weight, and the "driving habit-following distance threshold" adaptation parameters (increased by 20% for cautious users)). Only the encrypted parameters are incrementally transmitted to the cloud parent model. The encrypted parameter increment is encrypted using the national cryptographic SM4 / AES-256 algorithm and injected with differential privacy noise with a noise intensity ≤0.1, which can avoid the leakage of the original data. The cloud-based master model integrates parameter increments from multiple terminal nodes based on federated aggregation algorithms (such as FedAvg) that weights are allocated according to sample size (e.g., weight percentage = {number of samples from this school / region / model} / {total number of samples}) to generate a population baseline model.

[0029] Furthermore, The proactive dialogue strategy model includes: accurately identifying fuzzy data based on confidence or information entropy (such as dialectal speech "diarrhea", learning feedback "cannot do", obstacle recognition confidence < 0.7 in backlight driving scenarios, semantic matching degree of user dialect commands < 0.8), triggering proactive dialogue for fuzzy data, setting up a simplified interaction method for high-risk scenarios, and setting up multi-round guided dialogue for low-risk scenarios.

[0030] For high-risk scenarios (such as safe driving), a "minimalist interaction" (steering wheel buttons, voice prompts "yes / no") is designed, while for low-risk scenarios (such as educational assessments), a "multi-turn guided dialogue" is adopted to achieve dynamic matching between "scenario risk and interaction complexity". For example, when driving in heavy rain, the system can complete "fuzzy obstacle recognition" with only one button confirmation, avoiding distraction of the driver and resolving the contradiction between "interaction efficiency and scenario safety".

[0031] The collaborative model layer also includes active learning algorithms; The active learning algorithm improves accuracy for fuzzy data through an uncertainty judgment-directional interaction-parameter optimization process. The uncertainty judgment triggers interaction based on confidence level or information entropy, obtains a judgment result based on the interaction, and then uses a gradient descent algorithm to update the terminal sub-model weights, thereby optimizing the terminal sub-model parameters. Specifically, the uncertainty judgment module triggers interaction based on confidence level (S = {acoustic feature matching degree} × 0.6 + {semantic relevance} × 0.4) or information entropy (H > 1.2); in safe driving scenarios, it supports safe interaction methods such as steering wheel button confirmation and voice message "yes / no"; the parameter optimization module uses a gradient descent algorithm (learning rate η = 0.01) to update model weights (e.g., in a safe driving scenario, after the interaction result is confirmed as 'construction cone', the recognition weight of 'orange-red columnar object + reflective features' is strengthened). The collaborative model layer includes a progressive decision-making capability evolution module, which in turn includes a multi-agent knowledge annotation sub-module, a ladder-style decision-making capability sub-module, and a cross-domain knowledge transfer sub-module. The multi-agent knowledge annotation submodule is used for: It supports real-time annotation of decision-making basis at the terminal node layer for all roles (doctors, teachers, users, managers, etc.). The annotated data includes three-dimensional data: "decision action - logical explanation - scene label". For example: in the health field: doctors annotate "medication exclusion logic" (e.g., "patient has a history of asthma → prohibit drugs containing pseudoephedrine"), and users annotate "symptom description correction" (e.g., "'heart tightness' is actually 'squeezing sensation behind the sternum'"); in the education field: teachers annotate "attribution of wrong questions" (e.g., "calculation error stems from 'confusion of fraction conversion rules' rather than carelessness'"), and parents annotate "learning feedback" (…). For example, “Children are more focused on animated teaching”); the labeled data includes three elements: “decision action + logical explanation + scene label” (e.g., “Recommended A exercise → because students have ‘L2 fraction application’ and prefer gamified scenes → label: third grade elementary school math”), and only “de-privacy rules” (excluding the original data) are uploaded after local encrypted storage; in the field of safe driving: traffic experts labeled “reduce to 20km / h → because a 3-meter lateral safety distance needs to be maintained in the construction area → label: heavy rain + urban road”, and users labeled “slightly adjust to the left → because an oncoming truck occupies the lane → label: meeting on a narrow road”, etc. The tiered decision-making capability submodule is used for: A three-tiered threshold is defined for the evolution of decision-making ability. The service quality monitoring module of the central scheduling layer dynamically evaluates decision accuracy and human intervention rate, comparing these with the three-tiered thresholds to determine whether the current evolution has reached the assisted decision-making stage, semi-autonomous decision-making stage, or autonomous decision-making stage. Specifically, in the assisted decision-making stage: the matching degree of the decision scheme is ≥90% (compared to the annotation rules), and manual review is required before execution (e.g., in the health field, "medication recommendations require doctor confirmation," in the education field, "learning suggestions require teacher adjustments," and in the safe driving field, driving optimization suggestions require user confirmation such as "slow down due to construction ahead"). In the semi-autonomous decision-making stage: coverage of 70% of common scenarios (e.g., in the health field, "known hypertension +..."). The system automatically excludes nephrotoxic drugs for diabetic patients, automatically recommends shopping scenario exercises for fifth-grade students who got their scores wrong in the education field, and automatically optimizes lane keeping parameters under normal road conditions in the safe driving field. Manual intervention is only triggered in complex scenarios (such as multiple diseases overlapping or interdisciplinary knowledge blind spots). In the autonomous decision-making stage, it masters the "exceptional handling rules" (such as "adjusting children's medication dosage according to body surface area" in the health field, "affirming the thought process before providing feedback on introverted students' wrong answers" in the education field, and "paying extra attention to hazard lights at night in construction areas during heavy rain" in the safe driving field). The decision accuracy rate is ≥95% and complies with the domain standards. It can be executed independently and generate traceable decision reports.

[0032] The cross-domain knowledge transfer submodule is used for: Extract common features of decision-making logic from different domains (such as "clarification of fuzzy needs → targeted interaction → rule solidification"), construct cross-domain decision-making knowledge graphs, with a reuse rate of ≥70% (such as transferring "dialect semantic clarification rules (such as "diarrhea → diarrhea")" in the health domain to "clarification of learning confusion (such as "cannot do it" → "incorrect application of formula") in the education domain and "dialect instruction processing and vehicle light signal semantic recognition" in the safe driving domain; "group ability patterns" in the education domain (such as "correlation weight between junior high school students' mathematical logic and spatial imagination") can assist in "physical fitness assessment of adolescents" in the health domain (such as "correlation analysis between athletic ability and math scores")).

[0033] (3) Central scheduling layer, used for: Based on the decision results triggered by the collaborative model layer, services are provided (e.g., in the health field, blood glucose > 7.0 mmol / L for 3 consecutive days after breakfast → triggering remote consultation service; in the education field, student error rate in score application > 50% → triggering push of special practice questions service; in the safe driving field, following safety coefficient < 0.5 or environmental risk level = high → triggering deceleration warning service), responding to user-initiated service requests (e.g., medicine purchase request in the health field, assessment request in the education field, long press of the steering wheel help button request in the safe driving field) and providing multi-terminal collaborative response (e.g., in the health field, one-click emergency rescue → simultaneous location, allergy history to hospital + dispatch of ambulance; in the safe driving field, receiving "accident 200m ahead" signal → pushing "decelerate to 30km / h + alternative lane recommendation"). The cross-domain resource scheduling module integrates resources from multiple terminal nodes (e.g., hospitals and pharmacies in the health field, teachers and parents in the education field, and roadside equipment, surrounding vehicles, and traffic management nodes in the safe driving field). Based on the model output results of the collaborative model layer, it matches the optimal service path (e.g., in the health field, ambulance dispatch is sorted according to "distance weight 0.4 + specialist matching weight 0.6"; in the safe driving field, it connects with "real-time signal timing" to optimize intersection traffic decisions "based on 8 seconds remaining on the green light, it suggests accelerating through or stopping smoothly"), performs resource scheduling, and generates service notifications. in, The central scheduling layer also includes a permission and compliance control module and a service quality monitoring and optimization module; The permission and compliance control module divides role permissions based on the RBAC model (e.g., in the education field, parents can only view their children's data; in the health field, doctors can only access authorized medical records; in the safe driving field, traffic managers can only mark regional risk rules) and verifies the compliance of data access in real time (e.g., in the health field, the temporary authorization period for emergency scenarios is 30 minutes, and it is automatically revoked after expiration). The service quality monitoring and optimization module monitors indicators such as service response speed (e.g., target response time ≤ 500ms, response time ≤ 50ms in high-risk scenarios in the field of safe driving), accuracy (target accuracy ≥ 95%), and customer satisfaction. For low-quality services (e.g., identification error > 5%), it automatically triggers parameter optimization of the corresponding model in the collaborative model layer.

[0034] Furthermore, The service quality monitoring module of the central scheduling layer dynamically evaluates decision accuracy and human intervention rate, compares them with a third-order threshold, and determines whether the current evolution has reached the assisted decision-making stage, the semi-autonomous decision-making stage, or the autonomous decision-making stage; including: The decision support stage requires that the matching degree between the decision plan and the annotation rules be ≥90%, and that the decision be implemented after manual review; the semi-autonomous decision stage requires that the decision plan cover 70% of the common scenarios, and that manual intervention is required for complex scenarios; the autonomous decision stage requires that the decision accuracy rate be ≥95%, and that the decision be able to independently generate traceable reports that conform to the domain specifications.

[0035] (4) Terminal node layer, used for: Each terminal node provides a service interaction entry point. Based on real-time collection of raw data from user nodes (e.g., vital signs in the health field; answer records in the education field; steering wheel operations and voice commands in the safe driving field), the data is uploaded to the data fusion layer via an encrypted network. It receives service notifications from the central scheduling layer and engages in proactive dialogue interaction via voice / text (supporting dialects). Based on professional service nodes, it executes domain-specific services (e.g., in the health field, hospital nodes handle consultations; in the education field, teacher nodes submit classroom assessments; in the safe driving field, traffic management terminals annotate temporary control rules, and vehicle maintenance nodes provide "fault-decision impact" parameters). Based on resource support nodes, it provides supporting resources (e.g., in the health field, pharmacy nodes manage inventory; in the education field, parent nodes provide family data feedback; in the safe driving field, roadside equipment provides supplementary data on the external environment, and traffic lights provide real-time timing). Based on management nodes, it supports domain-specific configurations (e.g., in the health field, hospital management terminals adjust warning thresholds; in the education field, school management terminals customize assessment dimensions; in the safe driving field, fleet management terminals set "regional driving risk thresholds," etc.).

[0036] in, The terminal node layer is equipped with a multimodal acquisition module, which collects scene data in real time through multimodal sensors and uploads it to the data fusion layer. The terminal node layer receives service notifications from the central scheduling layer and engages in proactive dialogue interaction via voice or text.

[0037] This invention also provides a method for cross-domain intelligent management services with multi-model collaboration, including: Each terminal node in the terminal node layer collects raw data and transmits it to the data fusion layer, where the data fusion layer performs desensitization and parameterization on the raw data. Each terminal sub-model in the collaborative model layer generates personalized results based on local parameters and the original data of the terminal node it is located in, and the encrypted parameters are incrementally uploaded to the cloud parent model; The cloud-based master model aggregates the incremental parameters from multiple terminals to generate group patterns and feeds them back to the sub-models on each terminal. The central scheduling layer triggers services based on the output of the cloud-based master model or user requests, and schedules terminal node resources to execute services. Terminal nodes provide service results, and the central scheduling layer monitors service quality and triggers model optimization. The progressive decision-making capability evolution module of the collaborative model layer evolves in real time and dynamically upgrades decision-making capabilities through multi-subject knowledge annotation, step-by-step capability iteration, and cross-domain knowledge transfer. Example 1:

[0038] Applying the multimodal collaborative cross-domain intelligent management service system and method of this invention to the field of safe driving, the specific process includes: Step 1, Data Acquisition: The user node (vehicle terminal) collects data on "heavy rain weather + vehicle speed of 60km / h + camera recognition of a blurry orange-red object (confidence level of 65%)" and transmits it to the data fusion layer; Step 2, Data Preprocessing: The data fusion layer desensitizes the raw data and marks it as "High Environmental Risk Level + Suspected Construction Cone → Safety Warning Parameter"; Step 3, Model Collaborative Processing: When the terminal sub-model has low confidence in recognizing obstacles, it triggers an active dialogue (steering wheel buttons + voice prompt): "Is the blurry object in front a construction cone?" After the user confirms "yes" by pressing the button, the local recognition weight of "heavy rain + orange-red object → construction cone" is updated, and the encrypted parameters are uploaded incrementally to the cloud parent model. Step 4, Progressive Decision Support Stage: Based on the traffic expert's annotation that "a 3-meter lateral safety distance must be maintained in the construction area + speeding down to 20km / h", the system generates a suggestion of "speeding down to 20km / h + slight adjustment to the left lane", which needs to be confirmed by the user before execution. The "heavy rain + construction cone" recognition rules are recorded. Step 5, Progressive Semi-Autonomous Decision-Making Stage: After accumulating 500+ data points on construction scenarios during heavy rain, the terminal sub-model optimizes the association weight of "wiper frequency + construction cone recognition" (e.g., "when the wipers are running at high frequency, prioritize the recognition of orange-red objects"). The cloud-based parent model aggregates the patterns of "detour trajectories of different vehicle types (sedans / SUVs) around construction areas". When "heavy rain + orange-red columnar objects" are detected, it automatically recommends "decelerate to 20km / h + keep the left lane". User intervention is only triggered in complex scenarios such as "construction area length > 50 meters". Step 6, Gradual Decision-Making Autonomous Stage: After accumulating 3,000+ annotations, the system masters the exception rules for "construction in heavy rain" (such as "extra attention to hazard lights is required for nighttime construction" and "a 1.5 times safety distance is required for construction next to large trucks"). After a vehicle enters a section of road in heavy rain, it autonomously completes the entire process of "construction cone recognition → deceleration → lane planning → acceleration and recovery after passing" without user intervention.

[0039] Step 7, Service Triggering and Scheduling: The central scheduling layer links with road test equipment data (confirming "construction area length 30 meters") and simultaneously pushes a prediction suggestion of "restoring the original lane after construction is completed"; Step 8, Feedback and Optimization: Each user provides feedback that their suggestion is reasonable through their terminal (vehicle terminal). The service quality monitoring module evaluates the decision accuracy (96%) based on user feedback and triggers fine-tuning of model parameters. Example 2:

[0040] This invention applies a multimodal collaborative cross-domain intelligent management service system and method to the field of safe driving. Unlike Embodiment 1, it adds data collection and processing for the evaluation dimension to the data fusion layer, a driving risk assessment model to the collaborative model layer, an evaluation result linkage engine to the central scheduling layer, and an evaluation center interface to the vehicle terminal at the terminal node layer, thus achieving a closed loop of evaluation, feedback, and evolution. The specific implementation process includes: Step 1: The data fusion layer collects vehicle status data (speed, braking), environmental data, and detailed driving behavior data. Operational characteristics include: frequency of rapid acceleration / braking, steering wheel rotation amplitude (>90° / s is considered aggressive operation), lane departure duration, and number of times a vehicle runs red lights. Scene characteristics include: driving time in low-visibility scenarios such as nighttime / heavy rain, fatigue driving characteristics after more than 4 hours of continuous driving, and speed compliance rate in sensitive areas such as schools / hospitals. Interaction characteristics include: response speed to system warnings (e.g., decelerating within 1 second after receiving a "following too closely" warning is considered a valid response). Parameterized conversion rules: mapping behavioral data to quantifiable evaluation indicators, such as: "Number of emergency braking / Total driving time" → "Braking aggression coefficient"; "Number of speeding in sensitive areas" → "Scenario compliance rate" (0-100%, <60% is a low score). Step 2, Collaborative Model Layer: The terminal sub-model (driving risk assessment model) generates an **individual dynamic assessment report** based on local driving behavior data, including: real-time risk score (0-100 points, combined with the current operation and environment, such as "speeding in heavy rain → 30 points deducted in real time"); historical behavior profile ("percentage of aggressive operations" and "coverage of compliant scenarios" in the past 30 days); improvement suggestions (such as "the number of rapid accelerations in the past week exceeded twice the group average, it is recommended to start smoothly").

[0041] Step 3, Collaborative Model Layer: The cloud-based master model integrates multi-terminal evaluation data through a federated aggregation algorithm to generate group benchmarks and regional characteristics, such as: urban road group risk thresholds (e.g., "the tolerance value for aggressive operation on ring roads during morning rush hour is 0.6") and regional driving patterns (e.g., "the benchmark value for 'compliance rate of continuous curve deceleration' on mountain road sections is 85%)). Step 4: Central Dispatch Layer. The assessment results are linked to the engine. Internally, when an individual's real-time risk score is <60, a tiered intervention is triggered (low risk: voice prompt "Current driving is too aggressive"; high risk: push "Accident rate comparison table for the past 3 months" + recommended driver training services); External Interface: Outputs anonymized assessment reports (excluding vehicle VIN and precise trajectory, only including "risk level label" and "annual compliance rate") to third parties (such as insurance companies and fleet management platforms), supporting insurance pricing (e.g., "Annual compliance rate > 90% → premium discount of 15%)"); Fleet assessment (e.g., "Freight vehicle 'fatigue driving frequency' is linked to driver performance").

[0042] Step 5, Terminal Node Layer: The vehicle terminal assessment center interface displays a real-time risk score dashboard (dynamically displayed in red / yellow / green colors); historical behavior trend graphs (such as "the curve of the number of emergency brakings in the past 6 months"); third-party service entry points (such as clicking "insurance discounts" to jump to the page of the cooperating insurance company); the assessment center interface assessment feedback node supports users to raise objections to the assessment results (such as "the system misjudged the emergency braking as an aggressive operation, when it was actually to avoid a pedestrian"); the feedback data is incorporated into the terminal sub-model parameter optimization (adjusting the assessment weight of "emergency avoidance scenario").

[0043] The new module in this embodiment is based on the original system's four-layer architecture, without the need to reconstruct the core logic, reflecting the "cross-domain universal" design concept; at the same time, it integrates real-time risks with historical behavior, and the evaluation result = 70% real-time operational risk (such as current speeding) + 30% historical compliance rate (such as no violations in the past 3 months), avoiding misjudgments caused by single data. Example 3:

[0044] Currently, industrial equipment (robotic arms, AGVs, sensors) data is heterogeneous and privacy-sensitive (e.g., process parameters), resulting in low efficiency of cross-workshop / factory collaboration, delayed fault response, and weak flexible production capabilities due to reliance on fixed rules. This invention applies a multimodal collaborative cross-domain intelligent management service system and method to intelligent manufacturing and industrial collaboration scenarios, specifically including: Step 1: The data fusion layer connects the robotic arm's operating parameters (temperature, vibration), AGV positioning data, and material inventory data; Step 2: The data fusion layer performs local anonymization on the original data (hiding core process parameters) to achieve privacy protection. At the same time, the cloud-based master model aggregates data from multiple plants to generate "equipment collaboration rules" (such as "the correlation between the failure rate of a certain type of robotic arm and the humidity of materials under high temperature conditions"). Step 3: The "terminal sub-model" of the collaborative model layer is deployed at the edge node of the workshop to adjust the single machine operation parameters (such as AGV obstacle avoidance path) in real time. Step 4: The cloud-based master model optimizes cross-workshop scheduling rules (such as "the coordination priority of the robotic arm in workshop A and the AGV in workshop B when orders surge") through federated learning.

[0045] Step 5: Transfer and reuse the chain-like fault handling logic (such as "single device anomaly → associated device load warning → global resource scheduling") of the safe driving scenario in Example 1 to the intelligent manufacturing and industrial collaboration scenario. Step 6: Progressive decision-making mechanism. In the auxiliary stage, "auxiliary alarm" is achieved (such as reminding engineers when there is abnormal vibration). In the semi-autonomous stage, "semi-autonomous adjustment" is achieved (automatically reducing the load on the robotic arm when there is a minor fault). Finally, it evolves to the autonomous stage and "autonomous self-healing" is achieved (such as automatically triggering the switching of backup equipment based on historical fault labeling data, and the decision complies with the ISO20400 industrial safety standard). Example 4:

[0046] Currently, data from community devices (security cameras, energy meters, smart home systems) is scattered, residents' needs are vague (e.g., dialect descriptions of "my house is leaking"), cross-departmental service collaboration is inefficient (property management, electricity, water), and it is difficult to balance privacy and service efficiency. This invention, a multimodal collaborative cross-domain intelligent management service system and method, is applied to smart community and urban service scenarios. Specific implementations include: Step 1: Configure multi-entity collaborative services: The terminal node layer covers resident terminals (mobile APP, smart speaker), property nodes (energy consumption monitoring system), and public service nodes (electricity / water system). The data fusion layer analyzes residents' requests through "dialect semantic conversion" (reusing fuzzy demand processing algorithms from medical scenarios) (e.g., converting "the house is wet" into "suspected water pipe leak"). The central dispatch layer actively triggers cross-domain services (such as "leakage warning → simultaneous shutdown of household water meters + dispatch of maintenance personnel + push of temporary water use suggestions"), and the resource dispatch weight is dynamically adjusted according to "urgency (0.6) + distance (0.4)".

[0047] Step 2: Privacy-protected smart energy management The terminal sub-model analyzes electricity consumption habits locally in residents' homes (such as "high energy consumption from 18:00 to 20:00 on weekdays") and only uploads encrypted parameters (such as "peak-valley electricity consumption ratio") to the cloud-based parent model; The cloud-based master model aggregates the electricity consumption habits of residents in the community to generate "community energy consumption patterns" (such as "the peak time for air conditioner use in the summer evenings") and pushes out suggestions for off-peak electricity use (to avoid leakage of raw data). The gradual decision-making mechanism evolves from the initial stage of "reminding residents to turn off the lights" to the self-service stage, which enables "automatic coordination between smart home and power grid load" (e.g., "temporarily lowering the air conditioner temperature after user authorization when the power grid load is too high"). Example 5:

[0048] Currently, agricultural data (soil, meteorology, irrigation equipment) is scattered and varies greatly geographically (e.g., the rainy south and the arid north), and farmers have diverse operating habits (dialectal instructions, experience-based descriptions). Traditional systems struggle to balance "localized precise decision-making" with "regional planting patterns." This invention applies a multimodal collaborative cross-domain intelligent management service system and method to smart agriculture and precision planting service scenarios, specifically including: Step 1: Module Configuration The data fusion layer accesses field sensors (soil moisture, sunlight), drone images, and farmers' dialect commands (such as "the field is too dry"), and converts them into "irrigation demand index" locally (avoiding delays in uploading raw data). The cloud-based master model aggregates data from multiple regions to generate "climate-crop growth patterns" (such as "threshold for high incidence of rice diseases and pests during the plum rain season in the Yangtze River Basin").

[0049] The terminal sub-model of the collaborative model layer adapts to the planting habits of individual farmers (e.g., "Old Zhang's plot prefers evening irrigation"), while the cloud-based parent model optimizes the "regional irrigation strategy" (e.g., "five adjacent plots jointly schedule irrigation equipment to reduce energy consumption by 20%) through federated learning). Step 2: Gradual Agricultural Decision Making Multi-subject knowledge annotation (agricultural experts annotate "pest and disease identification rules", farmers annotate "local proverbs corresponding to meteorological patterns"), evolving in stages: from "recommended fertilization plans require expert confirmation" (auxiliary stage), to "automatic irrigation triggered by normal soil moisture" (semi-autonomous stage), and finally to "autonomously activate protective equipment (such as shade nets) in extreme weather (such as hail) + push insurance claim suggestions". Example 6:

[0050] Currently, financial data (transaction records, user behavior, market dynamics) is sensitive and heterogeneous. Real-time risk control needs to balance "data privacy" and "decision-making efficiency," and traditional models are difficult to deal with "new fraud patterns" (such as AI-generated fake transactions). This invention applies a multimodal collaborative cross-domain intelligent management service system and method to financial technology and intelligent risk control service scenarios, specifically including: Step 1: Implement privacy-protected real-time risk control The data fusion layer processes transaction data locally on the user terminal (such as "large-amount transfers at night from other locations") and only uploads encrypted features (such as "probability values ​​of abnormal transactions") to the cloud. The cloud-based master model aggregates features from multiple users to generate "group fraud patterns" (such as "transfer features driven by a certain type of APP"), thus preventing the leakage of original transaction data.

[0051] Active learning algorithms handle ambiguous transaction behaviors (such as "new user's first large transfer + no history"), and improve recognition accuracy by using "SMS confirmation" (reusing the safe interaction method of the driving scenario in Example 1), reducing the false judgment rate by 30%. Step 2: Achieve cross-domain risk migration and evolution Cross-domain knowledge transfer reuse of "group anomaly detection logic in educational scenarios" (such as "a class with a concentration of wrong questions → knowledge gaps") to "a region with a concentration of similar transactions → potential fraud gangs" has a reuse rate of ≥75%.

[0052] The progressive decision-making mechanism has evolved from "manual review of suspicious transactions" (auxiliary stage) to "autonomous freezing of high-risk accounts + linkage with the police anti-fraud system" (autonomous stage), and the decision-making basis dynamically incorporates the latest fraud cases (such as "voice characteristics of AI face-swapping fraud"). Example 7:

[0053] Currently, disaster data (meteorological, geographical, and rescue team data) is highly real-time and comes from complex sources. Cross-departmental (firefighting, medical, and transportation) collaboration is inefficient, and traditional systems struggle to quickly shift from "passive response" to "proactive early warning." This invention applies a multimodal collaborative cross-domain intelligent management service system and method to disaster relief and emergency collaboration scenarios. Specific implementations include: Step 1: Achieve real-time collaboration of multi-source data The data fusion layer accesses meteorological satellite data, drone aerial images, and location data from rescue personnel's handheld terminals. After local processing, it generates a "disaster impact heat map" (such as "probability of building damage after an earthquake"), and then encrypts and uploads the features to the cloud. The cloud-based master model aggregates and generates "regional rescue resource gaps" (such as "insufficient medical personnel in a certain township"). The central dispatch layer actively triggers scenario linkage (such as "Typhoon Red Alert → Advance dispatch of emergency tents to low-lying areas + push of resident evacuation routes"), and resource dispatch is dynamically adjusted according to "life priority (0.7) + distance (0.3)".

[0054] Step 2: Implementing progressive rescue decision-making Multi-subject knowledge annotation (rescue experts annotate "optimal path for rubble search and rescue", meteorological experts annotate "probability of secondary disasters"), evolving in a step-by-step manner: from "assisting in generating rescue plans" (assistance stage), to "automatically dispatching nearby rescue forces for routine disasters (such as small-scale floods)" (semi-autonomous stage), and finally achieving "autonomous coordination of cross-regional rescue teams and prediction of secondary disaster avoidance routes for complex disasters (such as earthquakes + rainstorms)" (autonomous stage). Example 8:

[0055] Taking the health service field as an example, the specific implementation process of the progressive decision-making mechanism in this invention includes: Step 1, Decision Support Stage: The raw data obtained by the terminal node layer: The user describes "having diarrhea for three days" in dialect. The terminal sub-model recognizes the confidence level as 60%, triggering the doctor to mark "'having diarrhea' = diarrhea, and the risk of cholera needs to be ruled out (in combination with the epidemic situation in the place of work)"; Decision output: The system generates a recommendation of "montmorillonite powder + rehydration salts", which needs to be reviewed by the doctor before being sent. At the same time, it records the "dialect mapping + epidemic association" rules. Step 2, Semi-autonomous decision-making stage After accumulating and labeling over 1,000 cases of diarrhea among migrant workers, the terminal sub-model optimizes the "dialect-symptom" mapping weights (e.g., "90% of diarrhea cases in Sichuan and Chongqing correspond to bacterial diarrhea"), while the cloud-based parent model aggregates the "work environment → pathogen type" pattern. Decision output: For cases with "dialect description + travel history to epidemic areas", the system automatically recommends "levofloxacin (avoiding allergy history) + dialect version of medication guidelines", and only complex cases (such as those with high fever) trigger doctor intervention.

[0056] Step 3: Autonomous Decision-Making Stage After accumulating more than 5,000 annotations, the system has mastered the exception rules for "diarrhea among migrant workers" (such as "the concentration of oral rehydration solution needs to be adjusted for long-term manual laborers"). Decision output: After a user describes "diarrhea + fatigue", a complete decision is generated within 10 seconds, including "predicted cause, medication plan, and navigation to the nearest outpatient clinic for migrant workers", without the need for manual review. Example 9:

[0057] Taking the field of educational assessment as an example, the specific implementation process of the progressive decision-making mechanism in this invention includes: Step 1: Decision Support Stage Initial data obtained by the terminal node layer: Students repeatedly answered fraction word problems incorrectly, and the teacher marked "The error stemmed from 'confusion between integer and fraction conversion'"; Decision output: The system recommends "basic exercises on fraction conversion", which teachers need to adjust the difficulty level before pushing to the system, and records the "error type - exercise matching" rules.

[0058] Step 2, Semi-autonomous decision-making stage After accumulating 500+ similar incorrect questions, the edge layer model optimizes the weights of "cognitive features - training methods" (e.g., "elementary school boys have a 30% higher acceptance of gamified exercises"). Decision output: Automatically recommend "shopping challenge score calculation game" and synchronize with parents' terminals to suggest "practicing score conversion when shopping at home". Teachers only need to review the degree of personalization.

[0059] Step 3: Autonomous Decision-Making Stage After accumulating more than 3,000 annotations, the cloud model aggregates the "correlation between fraction application ability and geometric thinking", and the terminal sub-model is adapted to the "self-learning scenario of left-behind children". Decision output: When a student answers incorrectly, the system automatically generates a "parent-child task with animation explanation and grandparents accompanying the practice", which perfectly matches the "intergenerational education" characteristics of this group and requires no teacher intervention.

[0060] Advantages of this invention: This invention features a universal architecture, enabling cross-domain reuse through a four-layer abstract architecture. Core modules can be directly adapted for scenarios such as healthcare, education, and safe driving, reducing development costs. It achieves a balance between privacy and efficiency, processing raw data locally and uploading only encrypted parameters to the cloud, while also ensuring accuracy based on group patterns through federated learning. It achieves proactive and precise service delivery, proactively triggering services based on real-time data and using proactive dialogue algorithms to resolve ambiguous requests, with service response latency ≤500ms and ≤50ms in high-risk driving scenarios. Furthermore, this invention possesses evolvable decision-making capabilities, breaking through the limitations of fixed rules in traditional systems by accumulating knowledge from multiple stakeholders. The leap from tool-assisted decision-making to expert-like decision-making has resulted in improvements in various aspects. In the health sector, medication recommendation accuracy has increased from 60% to over 95%; in the education sector, the adoption rate of learning suggestions has increased from 50% to over 85%; and in the driving sector, the accuracy of decisions for long-tail scenarios (such as "electric tricycles crossing unprotected intersections") has increased from 55% to 92%. It also demonstrates cross-domain adaptability, reducing the cost of accessing new domains through the migration of common decision-making logic, with a reuse rate of ≥70%. Furthermore, it offers advantages in multi-entity collaboration, transforming the tacit knowledge of doctors, teachers, users, and traffic managers into explicit rules that the system can learn, thus addressing the pain point of "algorithms relying solely on data statistics and lacking humanistic logic."

[0061] This invention addresses the pain points of traditional systems, such as "data heterogeneity and privacy conflicts," "inefficient multi-subject collaboration," and "delayed decision-making," through the core capabilities of "local privacy protection + cloud-based group patterns," "proactive service triggering," "gradual decision evolution," and "cross-domain knowledge transfer." It achieves an upgrade from "passive response" to "proactive service" and from "human assistance" to "intelligent autonomy," while reusing existing cross-domain logic (such as dialect processing and fault chain analysis) to reduce adaptation costs.

[0062] The above are merely preferred embodiments of the present invention and are not intended to limit the present invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A multi-model collaborative cross-domain intelligent management service system, characterized in that, The application relates to a multi-terminal collaborative service system. The system comprises a terminal node layer and a data fusion layer deployed on an edge device, a collaborative model layer and a central scheduling layer deployed on a cloud server; The data fusion layer is configured to integrate multi-field raw data, perform desensitization processing, time management and feature conversion on the raw data, and provide data for the collaborative model layer; The collaborative model layer is configured to construct a cloud mother model-terminal child model co-evolution matrix, including a field-specific model, a general model, a terminal child model and a cloud mother model, the terminal child model is personalized based on terminal node data, and encrypted parameter increments are output to the cloud mother model, the cloud mother model integrates the parameter increments of multiple terminals to generate a group benchmark model; based on the data provided by the data fusion layer and the group benchmark model, a gradual decision mechanism is fused to obtain model output results and decision results; The central scheduling layer is configured to trigger services based on the decision results of the collaborative model layer, respond to service requests initiated by users, and provide multi-terminal collaborative responses; The central scheduling layer is configured to integrate multi-terminal node resources, match service optimal paths based on model output results of the collaborative model layer, perform resource scheduling and form service notifications; The terminal node layer is configured to provide service interaction entrances for each terminal node, collect real-time raw data based on user nodes, upload the data to the data fusion layer through an encrypted network, receive service notifications from the central scheduling layer, perform active dialogue interaction, execute field services based on professional service nodes, provide supporting resources based on resource support nodes, and support field-specific configurations based on management nodes. The terminal node layer is provided with a multi-modal acquisition module, which acquires scene data in real time through a multi-modal sensor and uploads the data to the data fusion layer. The data fusion layer comprises a multi-source data access module, a desensitization and parameterization module and a data freshness management module; The multi-source data access module receives heterogeneous data uploaded by the terminal node layer; The desensitization and parameterization module sets desensitization rules, performs desensitization processing on the heterogeneous data, and maps the desensitized heterogeneous data into field feature parameters; 2.The multi-model collaborative cross-domain intelligent management service system according to claim 1, characterized in that: The data freshness management module sets a field freshness time threshold, caches real-time data through an edge caching mechanism, quickly indexes data through a time stamp, and obtains real-time data within the field freshness time threshold. 3.The multi-model collaborative cross-domain intelligent management service system according to claim 1, characterized in that: The collaborative model layer comprises a scenario-based model matrix, a terminal child model and a cloud mother model; The scenario-based model matrix comprises a field-specific model and a general model, the general model comprises an active dialogue strategy model, a resource matching model and a gradual decision capability evolution model; The terminal child model adopts a lightweight model, is deployed on each terminal node, is personalized based on data of each terminal node, and outputs encrypted parameter increments to the cloud mother model; The cloud mother model integrates the parameter increments of multiple terminal nodes based on a federated aggregation algorithm, and generates a group benchmark model. 4.The multi-model coordinated cross-domain intelligent management service system according to claim 1, characterized in that: ​ ​ ​ ​ 5.The multi-model collaborative cross-domain intelligent management service system according to claim 4, characterized in that: The active dialogue strategy model comprises: accurate identification of fuzzy data based on confidence or information entropy, triggering active dialogue for fuzzy data, setting an extremely simple interaction mode for high-risk scenarios, and setting a multi-round guided dialogue for low-risk scenarios. 6.The multi-model coordinated cross-domain intelligent management service system according to claim 1, characterized in that: The collaborative model layer further comprises an active learning algorithm; The active learning algorithm improves accuracy through an uncertainty judgment-directed interaction-parameter optimization process for fuzzy data, wherein the uncertainty judgment triggers interaction based on confidence or information entropy, obtains a judgment result based on the interaction, updates the terminal sub-model weight using a gradient descent algorithm based on the judgment result, and realizes parameter optimization of the terminal sub-model. 7.The multi-model coordinated cross-domain intelligent management service system according to claim 1, characterized in that: The central scheduling layer is further provided with a permission and compliance control module and a service quality monitoring and optimization module; The permission and compliance control module divides role permissions based on an RBAC model and verifies data call compliance in real time; The service quality monitoring and optimization module monitors service response speed, accuracy, and customer satisfaction, and automatically triggers parameter optimization of the corresponding model of the collaborative model layer for low-quality services. 8.The multi-model coordinated cross-domain intelligent management service system according to claim 1, characterized in that: The collaborative model layer comprises a progressive decision-making capability evolution module, which comprises a multi-agent knowledge labeling sub-module, a stepwise decision-making capability sub-module, and a cross-domain knowledge transfer sub-module; The multi-agent knowledge labeling sub-module is configured to: Support all roles in the terminal node layer to label decision-making basis in real time, and the labeled data comprises decision-making actions, logical explanations, and scene labels. The stepwise decision-making capability sub-module is configured to: Define three-order thresholds for decision-making capability evolution, dynamically evaluate decision-making accuracy and artificial intervention rate through the service quality monitoring module of the central scheduling layer, compare the three-order thresholds, and determine whether the current evolution is in the auxiliary decision-making stage, the semi-autonomous decision-making stage, or the autonomous decision-making stage. The cross-domain knowledge transfer sub-module is configured to: Extract common features of decision-making logic in different fields and construct a cross-domain decision-making knowledge graph. 9.The multi-model coordinated cross-domain intelligent management service system according to claim 8, characterized in that: Dynamically evaluate decision-making accuracy and artificial intervention rate through the service quality monitoring module of the central scheduling layer, compare the three-order thresholds, and determine whether the current evolution is in the auxiliary decision-making stage, the semi-autonomous decision-making stage, or the autonomous decision-making stage; including: The auxiliary decision-making stage requires a matching degree of decision-making scheme and labeling rules to be greater than or equal to 90%, and the decision-making scheme needs to be executed after being reviewed by an artificial person; the semi-autonomous decision-making stage requires a decision-making scheme to cover 70% of regular scenarios, and artificial intervention is required for complex scenarios; the autonomous decision-making stage requires a decision-making accuracy to be greater than or equal to 95%, and can independently generate a traceable report that meets the field specifications.

10. A multi-model collaborative cross-domain intelligent management service method, applying the multi-model collaborative cross-domain intelligent management service system according to any one of claims 1-9, characterized in that, Including: Each terminal node of the terminal node layer collects raw data and transmits it to the data fusion layer, and the data fusion layer performs desensitization and parameterization processing on the raw data; Each terminal sub-model of the collaborative model layer generates an individualized result based on local parameters and raw data of the terminal node, and uploads the encrypted parameter increment to the cloud mother model; The cloud mother model aggregates the parameter increments of multiple terminals to generate group rules and feedback each terminal sub-model; The central scheduling layer triggers services based on the output of the cloud mother model or user requests, and schedules terminal node resources to execute services; The terminal node feeds back service results, the hub scheduling layer monitors service quality and triggers model optimization; The collaborative model layer gradually evolves the decision-making ability module through multi-agent knowledge labeling, step-by-step ability iteration, and cross-domain knowledge transfer, real-time evolution, and dynamic upgrade of decision-making ability.

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