Water work order agent based on large model technology

Abstract

The invention relates to the technical field of intelligent water affairs and industrial artificial intelligence, in particular to a water affairs work order agent based on a large model technology, which comprises a process form configuration analysis module used for analyzing work order platform configuration in real time and generating a natural language business rule; the multi-modal semantic analysis module is used for calling an AI large model to analyze multi-modal input such as voices, texts and images on the basis of the rule, and automatically generating structured work order data; and the intelligent recommendation and operation module analyzes personnel data through load factors and a fuzzy logic algorithm, generates an order receiving personnel recommendation list and executes order dispatching. According to the invention, the dynamic fusion of the work order configuration and the AI capability is realized, and the adaptive capability of the system to the service scene is improved; manual filling dependence is reduced through multi-modal semantic analysis, and work order creation efficiency is improved; an intelligent algorithm is used to optimize the order dispatching decision, and the automation and precision level of the whole work order processing process is significantly improved.

Description

Technical Field

[0001] This invention relates to the fields of smart water management and industrial artificial intelligence technology, specifically to a water management work order intelligent agent based on large model technology. Background Technology

[0002] With the increasing demands for operational efficiency and service refinement in industries such as water utilities, work order management systems have become a core tool supporting daily operations and emergency response. (Refer to...) Figure 1 The typical workflow of existing technologies in the market is as follows: preset workflow form template → user manually selects template → fills out form → manual work order assignment → workflow progression → workflow completion → query statistical data. It is evident that current mainstream work order platforms have formed a full-process support capability covering work order configuration, creation, assignment, processing, and analysis. The system typically designs the work order structure based on preset form templates. Users can manually select a template, fill in form information, and submit the work order. Subsequently, a manual work order assignment mechanism drives the workflow step-by-step until the work order is closed. At the data application level, the system generally provides statistical query and visualization functions for basic indicators such as work order quantity and completion rate, forming a certain business data review capability.

[0003] However, when dealing with the diverse tasks, complex field environments, and high-frequency scheduling needs of the water industry, existing work order systems still face the following significant challenges in terms of intelligent support: 1) The system struggles to deeply integrate work order configuration with AI capabilities, resulting in a disconnect between field rules, business logic, and intelligent recommendations, which limits the system's ability to adapt to dynamic business scenarios. 2) The work order creation and dispatch process still relies heavily on human experience and lacks an automated support mechanism based on semantic understanding of multimodal data (such as voice, text, and images) and multidimensional decision factors; 3) When faced with massive amounts of work order data, the system's analysis capabilities are mostly limited to surface statistics, and it is not yet able to effectively carry out in-depth insights and strategy generation (such as work order priority prediction, anomaly root cause location, etc.), which restricts the further improvement of management optimization and decision-making accuracy.

[0004] The above reasons result in the overall low efficiency of existing technologies when adapting to complex business scenarios such as water affairs, making it difficult to meet the needs of modern work order operations that require high intelligence, adaptability, and deep insight. Summary of the Invention

[0005] To address the above technical problems, this invention provides a technical solution for a waterworks work order intelligent agent based on large model technology.

[0006] The technical problem solved by this invention can be achieved by the following technical solutions: A waterworks work order intelligent agent based on large model technology includes: The process form configuration parsing module connects to the configuration interface of the work order platform and is used to obtain and parse the form configuration data of the work order platform in real time to obtain natural language business rules. The multimodal semantic parsing module is connected to the process form configuration parsing module. It is used to receive multimodal data input by the user and, based on the natural language business rules, call the AI ​​big model to perform semantic parsing and information extraction on the multimodal data to generate work order data. The intelligent recommendation and operation module, connected to the multimodal semantic parsing module, is used to analyze the order-taking personnel data based on the work order data, using a load factor and fuzzy logic algorithm to generate a recommended list of order-taking personnel, and to dispatch work orders based on the user's confirmation of the recommended list of order-taking personnel.

[0007] Preferably, the process form configuration parsing module uses a configuration-semantic mapping engine to convert the form configuration data of the work order platform into natural language business rules that the AI ​​big model can understand in real time, so that the recognition and recommendation of the AI ​​big model are updated synchronously with the form configuration of the work order platform.

[0008] Preferably, the form configuration data includes field definitions, value range constraints, and business logic.

[0009] Preferably, the multimodal data includes at least one of voice data, text data, or image data.

[0010] Preferably, the multimodal semantic parsing module includes: The semantic understanding unit is used to perform deep semantic analysis on the multimodal data through the AI ​​big model to generate work order related information, which includes an initial event description, address elements, and user intent. The terminology mapping unit, connected to the semantic understanding unit, is used to optimize word segmentation and map professional terms to the work order-related information based on the water industry knowledge platform. The context completion unit, connected to the term mapping unit, is used to perform semantic completion and association reasoning on the mapped work order-related information through context completion logic to generate standardized work order field information, which includes event type, responsible entity, detailed address, and priority. The form filling unit, connected to the context completion unit, is used to fill the standardized work order field information into the corresponding fields of the work order table to generate the work order data.

[0011] Preferably, the intelligent recommendation and operation module includes: The factor fuzzification unit is used to transform the factors influencing the dispatch decision into a fuzzy set, wherein the factors influencing the dispatch decision include working hours, stability maintenance period, work order urgency, and work order expected duration. The load calculation unit, connected to the factor fuzzification unit, is used to perform parallel reasoning and quantitative analysis on the fuzzy set based on a predefined fuzzy rule base to generate load factors in multiple dimensions. The dispatch scoring calculation unit is connected to the load calculation unit and is used to calculate and sort dispatch scores based on the load factors of the multiple dimensions to obtain a recommended list of order takers. The dispatch execution unit is connected to the dispatch scoring calculation unit. It is used to receive the user's dispatch confirmation instruction, execute the work order dispatch operation based on the dispatch confirmation instruction, and synchronize the dispatch result to the relevant order recipient.

[0012] Preferably, in the load calculation unit, the multiple load factors include personnel performance load factor, geographical location load factor, and work saturation load factor.

[0013] Preferably, in the dispatch scoring calculation unit, the specific calculation formula for the dispatch score is as follows: , in, To score the dispatch, For the i-th load factor, This is the score value corresponding to the i-th load factor.

[0014] Preferably, the intelligent recommendation and operation module further includes a voice command execution unit, which is connected to the dispatch execution unit and is used to respond to the user's voice commands and, after real-time verification of the user's operation permissions, execute the withdrawal, resubmission, suspension, and scrapping operations of the work order.

[0015] Preferably, it further includes a multi-view analysis module, the multi-view analysis module comprising: The individual perspective analysis unit is used to prioritize the pending work orders of the personnel in charge of the orders and generate individual performance comparison data including completion rate and overtime rate. The management perspective analysis unit is used to statistically analyze team work order data and generate team performance reports that include team KPI scores, work order type distribution, and high-incidence area information. The causal inference unit, connected to the individual perspective analysis unit and the management perspective analysis unit, is used to locate the root cause of abnormal work orders based on the individual performance comparison data and the team performance report, and generate a decision analysis report containing actionable optimization suggestions.

[0016] Beneficial effects: This invention acquires and parses form configuration data from the work order platform in real time, transforming field rules and business logic into natural language business rules. This achieves deep integration of work order configuration and AI capabilities, enhancing the system's adaptive adjustment capabilities to dynamic business scenarios. Simultaneously, by employing multimodal data semantic parsing and information extraction based on natural language business rules, work order data is automatically generated, effectively reducing reliance on manual experience in the work order creation process and improving the automation level of multimodal task processing. Furthermore, by analyzing work order personnel data and generating a recommendation list through load factors and fuzzy logic algorithms, the work order dispatch mechanism is optimized, significantly improving the intelligence and efficiency of the dispatch process. Attached Figure Description

[0017] Figure 1 This is a schematic diagram of an existing work order management system; Figure 2 This is a schematic diagram illustrating the supporting relationship between the work order intelligent agent and the AI ​​module of the present invention; Figure 3 This is a module dependency diagram of the work order intelligent agent and the normal operation of the work order system in this invention; Figure 4 This is a schematic diagram of the multimodal semantic parsing module of the present invention; Figure 5 This is a flowchart of the work order creation process of the present invention; Figure 6 This is a schematic diagram of the intelligent recommendation and operation module of the present invention; Figure 7 This is the logic diagram of the intelligent order dispatch algorithm of the present invention; Figure 8 This is a schematic diagram of the multi-view analysis module of the present invention. Detailed Implementation

[0018] 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, and 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.

[0019] It should be noted that, unless otherwise specified, the embodiments and features described in the present invention can be combined with each other.

[0020] The present invention will be further described below with reference to the accompanying drawings and specific embodiments, but this is not intended to limit the scope of the invention.

[0021] Reference Figure 2 and Figure 3 This invention provides a waterworks work order intelligent agent based on large model technology, comprising: The process form configuration parsing module 100 connects to the configuration interface of the work order platform and is used to obtain and parse the form configuration data of the work order platform in real time to obtain natural language business rules. The multimodal semantic parsing module 200 is connected to the process form configuration parsing module 100. It is used to receive multimodal data input by the user and, based on the natural language business rules, call the AI ​​big model to perform semantic parsing and information extraction on the multimodal data to generate work order data. The multimodal data includes at least one of voice data, text data, or image data; The intelligent recommendation and operation module 300 is connected to the multimodal semantic parsing module 200. It is used to analyze the order-taking personnel data based on the work order data, using a load factor and a fuzzy logic algorithm to generate a recommended list of order-taking personnel, and to dispatch work orders based on the user's confirmation of the recommended list of order-taking personnel.

[0022] Specifically, in this embodiment of the invention, addressing the problem that existing systems struggle to deeply integrate work order configuration with AI capabilities and that work order creation and dispatch rely on human experience, this solution achieves seamless integration of work order configuration with AI capabilities and automates work order creation by real-time parsing of the work order platform's form configuration and converting it into natural language rules, and by calling a large AI model to perform semantic analysis on multimodal inputs. This effectively avoids the disconnect between field rules, business logic, and intelligent recommendations. Simultaneously, by employing load factors in conjunction with fuzzy logic algorithms to analyze order-receiving personnel data, intelligent and precise dispatch decisions are achieved, avoiding the reliance on experience and efficiency bottlenecks inherent in purely manual dispatch.

[0023] Specifically, in practical applications, the process form configuration parsing module 100 continuously monitors configuration changes on the work order platform. When the field rules or business processes of the form template are adjusted, the process form configuration parsing module 100 can instantly capture and parse these structured configuration data, transforming them into natural language business rules that the AI ​​big model can directly understand and use. This ensures that the AI ​​capabilities can dynamically adapt to changes in business scenarios, laying the foundation for subsequent intelligent processing. Next, the multimodal semantic parsing module 200 utilizes these rules to accurately call the AI ​​big model for content understanding and key information extraction when users initiate work order requests through various means such as voice, text, or images. It automatically fills in and generates standardized work order data, significantly reducing the tedium and errors of manual form filling. Subsequently, the intelligent recommendation and operation module 300 comprehensively considers the characteristics of the current work order, the real-time workload of the order taker, skill matching degree, and other multi-dimensional decision factors. It uses fuzzy logic algorithms for intelligent analysis and sorting to generate an optimal list of recommended order takers. After user confirmation, the assignment is automatically completed, thereby improving the accuracy of order assignment while ensuring the fairness and efficiency of personnel scheduling.

[0024] Overall, referring to Figure 3 The process form configuration parsing module 100 replaces the traditional preset process menu module that relies on manual pre-definition and is fixed, realizing real-time dynamic synchronization and seamless integration between work order configuration information and AI intelligence; the multimodal semantic parsing module 200 replaces the original cumbersome creation process that requires users to manually select templates and fill in forms item by item, realizing automatic extraction and intelligent filling of work order information based on natural interaction; the intelligent recommendation and operation module 300 replaces the dispatch mode that relies purely on the dispatcher's personal experience, realizing accurate dispatch decisions based on multi-dimensional factor intelligent analysis and optimized recommendations.

[0025] Specifically, refer to Figure 2The AI ​​big model mentioned is the Hetu AI Big Model, which serves as the core AI capability foundation. Through the collaborative work of a series of functional modules, it provides powerful, reliable, and continuously evolving intelligent support for the entire work order intelligent agent. These modules include, but are not limited to: ensuring the basic operation and data supply of the model through modules such as display words, API management (Application Programming Interface Management), and dataset management; achieving model optimization, deployment, and efficient computation through modules such as model quantization, model management, and model fine-tuning; enhancing the model's ability to reason about complex problems and apply knowledge through modules such as thought chains, agent invocation, and retrieval-augmented generation (RAG); and continuously optimizing model performance through modules such as model evaluation and user feedback, thereby providing powerful, flexible, and evolvable intelligent analysis support for subsequent multimodal semantic parsing and intelligent analysis.

[0026] In a preferred embodiment of the present invention, the process form configuration parsing module 100 converts the form configuration data of the work order platform into natural language business rules that the AI ​​big model can understand in real time through a configuration-semantic mapping engine, so that the recognition and recommendation of the AI ​​big model are updated synchronously with the form configuration of the work order platform.

[0027] Specifically, considering that AI capabilities and business configurations are often disconnected in existing technologies, which requires manual adjustment or retraining of AI models after changes in form rules and makes dynamic adaptation impossible, this invention proposes to design a dedicated configuration-semantic mapping engine to actively connect to the work order platform configuration interface and translate the technical configurations output by the platform (such as form definitions in JSON and XML formats) into natural language descriptions that can be directly processed by the Hetu AI large model in real time and automatically.

[0028] This approach enables the AI ​​big data model to instantly understand and follow new rules when the fields, constraints, and other information of business forms change, without the need for time-consuming and complex manual model retraining or code modification. This achieves seamless and dynamic integration of AI intelligence and business processes, significantly improving the system's adaptability and overall operational efficiency in the face of frequent changes in business rules.

[0029] More specifically, in this embodiment of the invention, the form configuration data specifically includes: Field definitions, including field names, data types (text, numeric, date, etc.) and their required / optional status; Value range constraints include the range of values ​​for numeric fields, the formatting specifications for text fields (such as phone numbers and addresses), and the list of possible values ​​for enumeration fields; Business logic includes dependencies between fields (e.g., when field A is "urgent", field B must be filled in) and dynamic display logic.

[0030] Specifically, the following concrete examples illustrate the conversion process of the configuration-semantic mapping engine: For value range constraints, if a field named "stress value" is configured as numeric and its value range is 0-1, the mapping engine will generate a natural language rule similar to "the field 'stress value' must be numeric and its value should be between 0 and 1".

[0031] For business logic and field expansion, such as adding a "map layer" field in the process configuration, the mapping engine will automatically parse this change and generate a natural language instruction such as "When creating a work order, map layer information must be provided", ensuring that the AI ​​can automatically recognize and request this information in subsequent work order creation.

[0032] In addition, the form configuration data also includes key information such as process node definitions (e.g., the steps of the work order flow, the operation permissions and fillable fields of each step), and data validation rules (e.g., cross-field logical validation). This information is also converted into natural language by the mapping engine to ensure that the large model has an accurate understanding of the complete context of the entire work order process, thereby supporting end-to-end intelligent interaction and decision-making.

[0033] As a preferred embodiment of the present invention, refer to Figure 4 The multimodal semantic parsing module 200 includes: The semantic understanding unit 210 is used to perform deep semantic parsing on the multimodal data through the AI ​​big model to generate work order related information, which includes an initial event description, address elements, and user intent. The terminology mapping unit 220, connected to the semantic understanding unit 210, is used to optimize word segmentation and professional terminology mapping of the work order-related information based on the water industry knowledge platform. The context completion unit 230, connected to the term mapping unit 220, is used to perform semantic completion and association reasoning on the mapped work order related information through context completion logic to generate standardized work order field information, which includes event type, responsible entity, detailed address and priority, etc. The form filling unit 240, connected to the context completion unit 230, is used to fill the standardized work order field information into the corresponding fields of the work order table to generate the work order data.

[0034] Specifically, considering that traditional work order creation relies heavily on users manually selecting templates and filling in each item, which is difficult and inefficient for non-professional users, especially when dealing with multimodal information such as voice and images, this invention constructs an intelligent parsing pipeline that integrates semantic understanding, terminology mapping, context completion, and automatic filling, thereby achieving end-to-end automatic conversion from user's original input to standardized work order data.

[0035] Specifically, the collaborative workflow of the multimodal semantic parsing module 200 is as follows: First, the semantic understanding unit 210 serves as the information entry point, relying on the multimodal fusion analysis capabilities of the Hetu AI big model to directly receive and parse the original information submitted by users through a mixture of voice, text, or images.

[0036] For example, when a user uploads a picture showing a broken water pipe and a voice description saying "The leak here is serious", the semantic understanding unit 210 can process visual and auditory signals simultaneously and accurately extract core event features such as "broken water pipe" and "serious leak" as well as preliminary location information such as "XX section of road".

[0037] Secondly, based on this, the terminology mapping unit 220 performs semantic refinement specifically for the professional characteristics of the water industry. It optimizes and professionally calibrates the text parsed from upstream by accessing the water industry knowledge platform.

[0038] For example, the word "leak" as described in user speech is accurately mapped to and converted into the standard event type "pipe leak" defined within the work order system, thereby ensuring the consistency between natural language and business terminology and laying the foundation for accurate matching in subsequent processes.

[0039] Next, the context completion unit 230 plays a core role in intelligent reasoning. Based on the structured information, it combines built-in completion rules with external systems (such as GIS geographic information systems) to perform correlation analysis and logical reasoning.

[0040] For example, when the context completion unit 230 receives the event type "pipeline leak" and the rough address "XX Garden Community", it will automatically trigger the address resolution service to convert the text address into precise latitude and longitude coordinates, and further determine the branch company responsible for the area (i.e. the responsible entity). At the same time, based on the severity of the event (such as keywords "serious" or "pipe burst"), it will automatically determine the priority of the work order as "urgent".

[0041] Finally, the form filling unit 240 is responsible for automatically and accurately filling the standardized and enriched field information (such as event type, precise location, responsible party, priority, etc.) generated by the aforementioned units into the corresponding form fields of the work order system, generating a structured work order data with complete information and standardized format, and completing the conversion from the user's original input to a work order that the system can directly process.

[0042] Overall, referring to Figure 5 The work order creation process shown above involves the four units working together to complete the key processing steps from "language input" to "semantic parsing" and then to "field supplementation," providing a complete and accurate work order data foundation for the subsequent "confirmation and submission" action.

[0043] Through this series of operations, the present invention completely liberates users from the tedious and professional task of filling out forms, allowing them to report in the most natural way. This not only greatly reduces the threshold and time cost of creating work orders (from several minutes of manual operation to seconds of automatic completion), but also significantly improves the information quality, standardization, and processing direction of the created work orders through accurate terminology mapping and in-depth context completion, laying a solid data foundation for subsequent intelligent dispatching and efficient processing.

[0044] More specifically, in the embodiments of the present invention, reference is made to... Figure 2 The water industry knowledge platform is a structured and dynamically updated water industry knowledge base. Its core components include modules such as graph display, knowledge services, ontology management, Q&A, knowledge annotation, and model management, which together form a professional cognitive hub that supports intelligent agents in deeply understanding water business.

[0045] The water industry knowledge platform works in collaboration with the Hetu AI big data model to provide core support for the multimodal semantic parsing module 200 in the following ways: The ontology management module constructs and maintains a conceptual system of standards in the water industry (such as the hierarchical relationship of "equipment-pipeline-valve" and the attribute definition of "pipe burst" events), providing a standardized semantic framework for AI to understand professional terms.

[0046] The knowledge service module, based on this framework, provides real-time terminology matching and concept mapping services. When the Hetu AI big model initially parses the word "leak" in the user's spoken language, it will call this service to accurately map it to the standard event type "pipe leak" defined in the ontology.

[0047] The knowledge annotation and Q&A module continuously provides high-quality industry corpora and question-and-answer pairs during the model training and optimization phases, which are used to train and fine-tune the Hetu AI large model, enabling it to master professional expressions and problem-solving patterns in the water affairs field.

[0048] The graph display module presents complex business relationship networks (such as asset locations and pipeline topologies) in a visual form. This structured knowledge can be retrieved and utilized by the Hetu AI large model through technologies such as RAG, thereby enabling more accurate association reasoning during the context completion stage (such as automatically inferring the upstream valve that should be closed based on the location of the burst pipe).

[0049] The model management module ensures the effective integration and continuous iteration of water resources knowledge features with the capabilities of the Hetu large model, guaranteeing the accuracy and timeliness of the entire cognitive center.

[0050] Through this collaborative mechanism, the static industry knowledge of the water industry knowledge platform is transformed into dynamic cognitive capabilities that AI can understand and apply. This ensures that the Hetu AI big model can not only "understand" the user's natural language, but also "understand" the professional water industry connotations behind it, thereby achieving a leap from general intelligence to industry-specific expertise.

[0051] As a preferred embodiment of the present invention, refer to Figure 6 The intelligent recommendation and operation module 300 includes: The factor fuzzification unit 310 is used to convert the factors influencing the order dispatch decision into a fuzzy set. The load calculation unit 320 is connected to the factor fuzzification unit 310 and is used to perform parallel reasoning and quantitative analysis on the fuzzy set based on a predefined fuzzy rule base to generate load factors in multiple dimensions. The dispatch scoring calculation unit 330 is connected to the load calculation unit 320 and is used to calculate and sort the dispatch scores based on the load factors of the multiple dimensions to obtain a recommended list of order takers. The dispatch execution unit 340 is connected to the dispatch scoring calculation unit 330. It is used to receive the user's dispatch confirmation instruction, execute the work order dispatch operation based on the dispatch confirmation instruction, and synchronize the dispatch result to the relevant order recipient.

[0052] Specifically, considering that the dispatch decision process involves both discrete conditional factors with clear states (such as "whether it is a working period" and "whether it is in a special guarantee period") and continuous task characteristic factors with varying degrees (such as "order urgency" and "estimated task duration"), traditional dispatch decision-making is difficult to quantify the fuzziness in these dispatch decision factors and cannot adapt to the dynamically changing external environment. Based on this, in this embodiment of the invention, dispatch decision influencing factors such as "whether it is a working period", "whether it is a stability maintenance period", "expected duration of the order task" and "order task urgency" are transformed into a fuzzy set that the system can process through the factor fuzzification unit 310, thereby realizing the quantitative processing of qualitative experience.

[0053] The fuzzy sets corresponding to each order dispatch decision influencing factor are as follows: Working hours Working hours (1), non-working hours (0); During the period of maintaining stability : Stable period (1), unstable period (0); Work order urgency : Low (1), Medium (2), High (3); Expected duration of work order : Short (1), Medium (2), Long (3).

[0054] In this way, qualitative decision factors that originally relied on human experience are transformed into fuzzy sets with clear numerical representations, providing standardized input for subsequent intelligent reasoning based on fuzzy rules. For example, when the system recognizes the combination of "non-working time" (0) and "high urgency" (3), it will automatically trigger a specific adjustment strategy for the load factor through predefined fuzzy rules, thereby achieving the intelligent effect of dynamically optimizing the order dispatch decision according to different business scenarios.

[0055] Next, considering that the core objective of dispatching decisions is to assign the right task to the right person at the right time, and that achieving this objective requires simultaneously considering three key dimensions: task completion quality, response timeliness, and resource utilization efficiency, based on this, and referring to... Figure 7 In this embodiment of the invention, based on the order dispatch decision influencing factors, according to a predefined business rule base (e.g., "if it is during working hours and the work order is highly urgent, then increase the load factor related to the recent performance evaluation of the personnel and the load factor of the personnel's region"), the fuzzy set is activated by multiple rules and fuzzy reasoning to generate load factors of three core dimensions: personnel performance, geographical location and work saturation.

[0056] The specific formulas for calculating these load factors are as follows: Personnel performance load factor , Geographical load factor , Work saturation load factor .

[0057] Furthermore, after calculating these load factors, the corresponding score value for each load factor is obtained, i.e.: Corresponding to personnel performance load factor Performance rating The higher the value, the stronger the ability and reliability of the personnel to complete the task, and the system will give priority to recommending them to accept orders. Corresponding to geographic location load factor Timeliness rating The higher the value, the lower the time cost for personnel to arrive at the scene, and the system will prioritize recommending personnel who are closer to the site to accept the order; Corresponding to the workload saturation factor Load adaptation score The higher the value, the lighter the current workload of the personnel and the better the responsiveness. The system will prioritize recommending relatively idle personnel to accept orders.

[0058] Then, the dispatch score of the order taker n is calculated by the dispatch score calculation unit 330, and all order takers are sorted based on the dispatch score to generate a recommendation list; the dispatch score calculation formula is as follows: , in, To score the dispatch, For the i-th load factor, This is the score value corresponding to the i-th load factor.

[0059] Finally, after receiving the user's confirmation instruction, the dispatch execution unit 340 dispatches the work order and simultaneously synchronizes the dispatch result to the mobile terminal of the relevant personnel receiving the order in real time, forming a complete intelligent dispatch closed loop.

[0060] In this way, the system can generate intuitive recommendation information, such as "Mr. Zhang scores 9.8 points, distance 6817 meters", and implement dispatch strategies based on multi-dimensional intelligent analysis, such as "calculating user load capacity based on historical data of work order users", "prioritizing dispatch to idle personnel when the number of work orders in the current area surges", and "prioritizing dispatching work orders to nearby staff".

[0061] As a preferred embodiment of the present invention, refer to Figure 6 The intelligent recommendation and operation module 300 also includes a voice command execution unit 350, which is connected to the dispatch execution unit 340. It is used to respond to the user's voice commands and, after verifying the user's operation permissions in real time, execute the withdrawal, resubmission, suspension and scrapping operations of the work order.

[0062] Specifically, in this embodiment of the invention, the voice command execution unit 350 integrates voice recognition and permission verification mechanisms, enabling dispatchers to quickly execute various work order operation commands using natural speech. When a user issues a voice command such as "suspend this work order," the system first performs real-time identity verification using voiceprint recognition and a role permission database. If the verification is successful, the corresponding operation is executed immediately; if permissions are insufficient, a prompt message is provided through voice synthesis technology, such as "You do not have suspension permission, please contact the administrator."

[0063] This interaction method significantly improves the operational efficiency and user experience of work order scheduling, while ensuring the security of system operations.

[0064] As a preferred embodiment of the present invention, refer to Figure 8 The water industry work order intelligent agent also includes a multi-view analysis module 400, which includes: The individual perspective analysis unit 410 is used to prioritize the pending work orders of the personnel in charge of the order and generate individual performance comparison data including completion rate and overtime rate. The management perspective analysis unit 420 is used to statistically analyze team work order data and generate a team performance report that includes team KPI scores, work order type distribution, and high-incidence area information. The causal inference unit 430, connected to the personal perspective analysis unit 410 and the management perspective analysis unit 420, is used to locate the root cause of abnormal work orders through a causal inference model based on the personal performance comparison data and the team performance report, and generate a decision analysis report containing executable optimization suggestions.

[0065] Specifically, in this embodiment of the invention, the multi-perspective analysis module 400 constructs a dual-perspective analysis system of individuals and management, and combines it with causal inference technology to achieve a comprehensive insight from micro-level individual effectiveness to macro-level team performance.

[0066] Correspondingly, the individual perspective analysis unit 410 not only dynamically prioritizes pending work orders for each order taker, but also helps individuals conduct performance self-checks and work planning by generating a trend chart comparing completion rates and overtime rates for "this month vs. last month." The management perspective analysis unit 420 integrates data at the team level, providing managers with an intuitive grasp of the overall operational situation by quantifying KPI scores (such as a completion rate of 85.37% and an anomaly rate of 12.20%), visualizing the distribution of work order types (such as 100% of GIS maintenance work orders), and locating high-incidence areas (such as 25 concentrated orders in a region). As the core of intelligent analysis, the causal inference unit 430, based on the above data, uses a causal inference model to deeply explore the root causes behind abnormal patterns (for example, identifying the key issue that "70% of overtime work orders are due to insufficient parts"), and automatically generates concrete and actionable optimization suggestions (such as "suggesting that commonly used maintenance parts be allocated to the region in advance"). This transforms data insights into precise management actions, significantly improving the scientific and forward-looking nature of water affairs operation and maintenance management.

[0067] In summary, this invention forms a complete intelligent management solution for waterworks work orders by constructing a unified intelligent hub to achieve dynamic business adaptation, innovating work order processing workflows to achieve full-process automation, and mining the deep value of data to achieve intelligent decision support.

[0068] Compared with the prior art, the present invention has the following outstanding advantages: It achieves dynamic and seamless integration of AI capabilities and business rules: Through an innovative "configuration-semantic mapping engine," it completely solves the problem of the disconnect between AI and business configuration. When the form configuration and business rules of the work order platform change, the AI ​​big data model can understand and update its recommendation and operation logic in real time, eliminating the need for time-consuming manual model retraining and significantly improving the system's adaptability to complex and ever-changing business scenarios.

[0069] It achieves intelligent and automated operation of the entire process from work order creation to dispatch: by leveraging the multimodal semantic parsing capabilities of large models, it frees users from tedious manual filling and template selection, and supports the efficient creation of standardized work orders through natural methods such as voice, text, and images; at the same time, it introduces multi-dimensional load factors based on fuzzy logic for intelligent work order dispatch decision-making, which greatly reduces the reliance on human experience and significantly improves the efficiency and accuracy of the entire work order processing chain.

[0070] It has achieved a data value enhancement from superficial statistics to in-depth insights: through multi-perspective analysis and causal inference models, the system can penetrate the surface of massive work order data, automatically locate the root causes of anomalies, predict work order priorities, and generate decision analysis reports with actionable suggestions. This provides unprecedented data-driven decision support for management optimization and effectively promotes the transformation of water operations from a passive response to a proactive management model.

[0071] The solution has achieved quantifiable and outstanding operational results: It has brought significant improvements in efficiency, accuracy and management optimization. For example, it has reduced the time for creating work orders from 5 minutes to less than 1 minute, reduced the time for dispatching orders by 60%, and reduced the time for generating analysis reports from 2 hours to 5 minutes, which fully demonstrates its great value and technological advancement in actual business scenarios.

[0072] The following two specific embodiments illustrate the practical application process and technical effects of the present invention: Example 1: Intelligent creation and dispatch of water outage work orders in residential communities 1) The on-site maintenance personnel reported via voice using a mobile terminal APP that "the entire Garden Community is without water and needs emergency handling," and simultaneously uploaded a photo showing the abnormality of the water supply valve.

[0073] 2) The system processes this information in real time through a multimodal semantic parsing module: First, identify the critical event of "water outage" and accurately classify it into the standard work order type of "difficult water use → no water in the entire community"; Secondly, by combining the "garden community" mentioned in the voice with the automatic location of the GIS system, the work order address was accurately matched and completed. At the same time, the responsible company "water affairs" was automatically associated according to the jurisdiction rules, and the work order priority was determined to be "urgent" based on keywords such as "emergency handling". Ultimately, the system automatically generated a standardized work order title: "AI_Patrol Report_No Water in the Entire Community_Water Affairs".

[0074] 3) In the intelligent order dispatching process, the system evaluated and ranked the candidate order takers in multiple dimensions based on load factors and fuzzy logic algorithms. Among them, "Master Zhang" was recommended to the top by the system because of his historical completion rate of 98%, even though he had a lot of pending work orders (208) and was far away from the site (6817 meters).

[0075] 4) The maintenance personnel click "OK" to dispatch the work order. The system automatically dispatches the work order (No.: 202508110***5) to Mr. Zhang and sends an SMS notification at the same time. The work order status is updated to the work order platform in real time, thus completing the fully automated process from reporting to dispatch.

[0076] Example 2: Intelligent Analysis and Root Cause Localization for Management Decision Support 1) Department leaders issue instructions in natural language through the PC management platform: "Analyze the department's work orders for this month."

[0077] 2) The system responds to this command through the multi-view analysis module 400: First, the team's work order data for this month was analyzed from multiple dimensions using the management perspective analysis unit 420. It was found that 45 work orders were completed, of which 100% were GIS maintenance work orders. The heat map was used to identify the high-incidence area as X region X area (25 orders in total). At the same time, the performance of each team member is evaluated simultaneously through the individual perspective analysis unit 410. For example, a member has a completion rate of 85.37% and an error rate of 12.20%, ranking first in the team. Then, based on the above data, the causal inference unit 430 uses a causal inference model to deeply mine the abnormal work orders and locates the root cause of the three overdue work orders as "insufficient parts".

[0078] 3) The system automatically generates decision analysis reports that include data visualization charts and text descriptions, and provides actionable optimization suggestions, such as: "Prioritize the stockpiling of repair parts for the XX area and strengthen the review mechanism for abnormal work orders by administrators."

[0079] As can be seen from the above two typical embodiments, the present invention achieves: In the work order creation process, unstructured on-site information is transformed into standardized work order data through multimodal semantic understanding, which solves the efficiency bottleneck of traditional manual form filling. In the work order dispatching process, by introducing multi-dimensional load factors and fuzzy logic algorithms, intelligent work order dispatching decisions that balance efficiency and fairness are achieved. In the management analysis phase, through multi-perspective data analysis and causal inference, massive amounts of work order data are transformed into decision support information with practical and actionable value, effectively improving the level of refinement and intelligence in water operations management.

[0080] The above description is merely a preferred embodiment of the present invention and does not limit the implementation and protection scope of the present invention. Those skilled in the art should realize that any equivalent substitutions and obvious changes made based on the description and illustrations of the present invention should be included within the protection scope of the present invention.

Claims

1. A waterworks work order intelligent agent based on large model technology, characterized in that, include: The process form configuration parsing module connects to the configuration interface of the work order platform and is used to obtain and parse the form configuration data of the work order platform in real time to obtain natural language business rules. The multimodal semantic parsing module is connected to the process form configuration parsing module. It is used to receive multimodal data input by the user and, based on the natural language business rules, call the AI ​​big model to perform semantic parsing and information extraction on the multimodal data to generate work order data. The intelligent recommendation and operation module, connected to the multimodal semantic parsing module, is used to analyze the order-taking personnel data based on the work order data, using a load factor and fuzzy logic algorithm to generate a recommended list of order-taking personnel, and to dispatch work orders based on the user's confirmation of the recommended list of order-taking personnel.

2. The waterworks work order intelligent agent based on large model technology according to claim 1, characterized in that, The process form configuration parsing module uses a configuration-semantic mapping engine to convert the form configuration data of the work order platform into natural language business rules that the AI ​​big model can understand in real time, so that the recognition and recommendation of the AI ​​big model are updated synchronously with the form configuration of the work order platform.

3. The waterworks work order intelligent agent based on large model technology according to claim 2, characterized in that, The form configuration data includes field definitions, value range constraints, and business logic.

4. The waterworks work order intelligent agent based on large model technology according to claim 1, characterized in that, The multimodal data includes at least one of voice data, text data, or image data.

5. The waterworks work order intelligent agent based on large model technology according to claim 4, characterized in that, The multimodal semantic parsing module includes: The semantic understanding unit is used to perform deep semantic analysis on the multimodal data through the AI ​​big model to generate work order related information, which includes an initial event description, address elements, and user intent. The terminology mapping unit, connected to the semantic understanding unit, is used to optimize word segmentation and map professional terms to the work order-related information based on the water industry knowledge platform. The context completion unit, connected to the term mapping unit, is used to perform semantic completion and association reasoning on the mapped work order-related information through context completion logic to generate standardized work order field information, which includes event type, responsible entity, detailed address, and priority. The form filling unit, connected to the context completion unit, is used to fill the standardized work order field information into the corresponding fields of the work order table to generate the work order data.

6. The waterworks work order intelligent agent based on large model technology according to claim 1, characterized in that, The intelligent recommendation and operation module includes: The factor fuzzification unit is used to transform the factors influencing the dispatch decision into a fuzzy set, wherein the factors influencing the dispatch decision include working hours, stability maintenance period, work order urgency, and work order expected duration. The load calculation unit, connected to the factor fuzzification unit, is used to perform parallel reasoning and quantitative analysis on the fuzzy set based on a predefined fuzzy rule base to generate load factors in multiple dimensions. The dispatch scoring calculation unit is connected to the load calculation unit and is used to calculate and sort dispatch scores based on the load factors of the multiple dimensions to obtain a recommended list of order takers. The dispatch execution unit is connected to the dispatch scoring calculation unit. It is used to receive the user's dispatch confirmation instruction, execute the work order dispatch operation based on the dispatch confirmation instruction, and synchronize the dispatch result to the relevant order recipient.

7. A waterworks work order intelligent agent based on large model technology according to claim 6, characterized in that, In the load calculation unit, the multiple load factors include personnel performance load factor, geographical location load factor, and work saturation load factor.

8. A waterworks work order intelligent agent based on large model technology according to claim 6, characterized in that, In the dispatch scoring calculation unit, the specific calculation formula for the dispatch score is as follows: ， in, For the overall evaluation of order dispatch, For the i-th load factor, This is the score value corresponding to the i-th load factor.

9. A waterworks work order intelligent agent based on large model technology according to claim 6, characterized in that, The intelligent recommendation and operation module also includes a voice command execution unit, which is connected to the dispatch execution unit and is used to respond to the user's voice commands. After verifying the user's operation permissions in real time, the voice command execution unit performs the withdrawal, resubmission, suspension and scrapping operations of the work order.

10. A waterworks work order intelligent agent based on large model technology according to claim 1, characterized in that, It also includes a multi-view analysis module, which includes: The individual perspective analysis unit is used to prioritize the pending work orders of the personnel in charge of the orders and generate individual performance comparison data including completion rate and overtime rate. The management perspective analysis unit is used to statistically analyze team work order data and generate team performance reports that include team KPI scores, work order type distribution, and high-incidence area information. The causal inference unit, connected to the individual perspective analysis unit and the management perspective analysis unit, is used to locate the root cause of abnormal work orders based on the individual performance comparison data and the team performance report, and generate a decision analysis report containing actionable optimization suggestions.

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