Work order intelligent management and control and efficiency analysis system based on multi-dimensional data visualization

Abstract

The application belongs to the technical field of industrial informatization and intelligent operation and maintenance management, and specifically discloses a work order intelligent management and control and efficiency analysis system based on multi-dimensional data visualization. The system comprises a work order data acquisition unit, a process topology construction unit, a spatial syntax analysis engine, a dynamic visualization rendering unit and a process simulation deduction unit. By abstracting the work order flow as a dynamic complex network, introducing the spatial syntax theory to quantify the node integration and selection, and directly presenting the process congestion and island area in the form of three-dimensional terrain, the user can interactively modify the topology and simulate and optimize the effect in real time. The application can accurately locate the business process bottleneck, visualize the insight and make forward deduction, and improve the intelligent level and decision efficiency of process management.

Description

Technical Field

[0001] This invention belongs to the field of industrial informatization and intelligent operation and maintenance management technology, specifically involving a work order intelligent control and efficiency analysis system based on multi-dimensional data visualization. Background Technology

[0002] With the widespread adoption of information management systems, work order control systems, as critical infrastructure for enterprise resource scheduling and business process flow, directly impact organizational collaboration efficiency and response speed. In large-scale business scenarios, work order data flows exhibit high concurrency, multiple paths, and dynamic evolution. Fine-grained management of the work order lifecycle allows for optimal allocation of production factors. Work order flow involves not only linear changes in task status but also complex interactive logic and structural relationships between organizations, placing high demands on the system's logical parsing capabilities and multi-dimensional data representation capabilities.

[0003] Intelligent work order management and efficiency analysis technologies based on data visualization have become important tools for improving the transparency and accuracy of business processes. These technologies utilize graph theory and topology analysis to transform the abstract work order flow process into a perceptible digital mapping. By identifying critical paths and monitoring traffic distribution in real time, they aim to reveal the accessibility characteristics of the business system during operation. Managers can then intuitively understand the current state of business operations and locate and assess resource bottlenecks for specific time periods or stages, supporting the scientific formulation of management decisions.

[0004] Existing technologies primarily rely on traditional process mining algorithms for linear statistics, making it difficult to deeply uncover hidden bottlenecks and structural silos within business networks. This results in an understanding of process operation mechanisms limited to surface-level time-consuming metrics. Traditional analysis methods lack quantitative assessments of spatial penetration and logical connection depth in complex network topologies, failing to accurately identify high-obstacle nodes in the non-linear flow of work orders, leading to information misalignment between visual feedback and actual operational pressure. Existing systems suffer from passive monitoring of the current state, lacking dynamic simulation and integration prediction capabilities for process topology changes. They also struggle to conduct preliminary research and verification of optimization schemes through intuitive interactive methods, resulting in a high degree of blindness and risk in process improvement. Summary of the Invention

[0005] To address the aforementioned problems, this invention provides a work order intelligent management and efficiency analysis system based on multi-dimensional data visualization, comprising: The work order data acquisition unit is configured to acquire the lifecycle event data of all work orders in the enterprise information system in real time. The lifecycle event data includes work order creation, status change, handler information, flow path and processing time. The unit also cleans, normalizes and structures the raw data to form a standardized work order event flow. The process topology construction unit is configured to abstract each work order processing node as a vertex in the network graph based on the standardized work order event flow, and to abstract the flow relationship of work orders between different processing nodes as directed edges, thereby constructing a dynamic and complex network topology graph that reflects the overall business process logic. The spatial syntax analysis engine is configured to map the dynamic complex network topology graph into a virtual space structure. Through the integration degree and selectivity calculation model in spatial syntax theory, it quantitatively evaluates the penetration, reachability and centrality of each node in the virtual space structure and identifies high-obstacle nodes and structural island regions. The dynamic visualization rendering unit is configured to map the quantified and evaluated dynamic complex network topology graph into a three-dimensional terrain model with spatial depth, map the integration degree value of the node to the terrain height in the three-dimensional terrain model, and map the selection degree value of the node to the terrain texture density in the three-dimensional terrain model. The process simulation and deduction unit is configured to receive process topology modification instructions initiated for the three-dimensional terrain model, and recalculate the spatial integration changes of each node in real time based on the modified topology, and predict the impact of the process optimization scheme on the overall accessibility and flow efficiency.

[0006] Preferably, the work order data acquisition unit includes: A multi-source heterogeneous system adapter is configured to connect to customer relationship management systems, enterprise resource planning systems, service desk platforms, and collaborative office systems via preset interface protocols to obtain cross-system work order data. The data monitoring submodule is configured to capture status change events of work orders from creation, allocation, response, processing, suspension, transfer, verification to closure by monitoring the database change logs of various business systems, calling application interfaces, or receiving messages pushed by the message queue. The protocol conversion submodule is configured to convert non-standard work order messages into a standard structured data format. The data cleaning and deduplication submodule is configured to use preset cleaning rules to filter out noise from the collected raw data, remove invalid work orders that are missing key timestamps, merge redundant status change records generated within a preset time period, and perform global deduplication based on the unique identifier of the work order. The anomaly capture submodule is configured to monitor the health status of the data interface in real time. It automatically triggers a reconnection mechanism when no work order heartbeat packet is received within a preset period. It also performs full synchronization during off-peak hours to correct data deviations by combining incremental collection with full verification.

[0007] Preferably, the process topology construction unit includes: The node extraction submodule is configured to abstract the business links, processing positions or executors involved in the work order process into vertices, and assign each vertex a multi-dimensional attribute label containing information such as its department, function type, average load capacity and geographical location. The relation mapping submodule is configured to establish directed edges based on the movement order of work orders between different nodes, and dynamically assign weights to the edges based on the frequency of work orders flowing between two nodes, the average flow time, and the flow success rate. At the same time, it establishes logical channels with higher weights when processing high-priority work orders. The dynamic evolution monitoring submodule is configured to introduce a time window mechanism, generate staged topology snapshots at preset granularities of daily, weekly, or monthly, and support historical backtracking and trend analysis of process structure evolution. The performance characteristic maintenance module is configured to maintain a multi-dimensional vector within each vertex, store the real-time load, error rate, and collaboration satisfaction score of the node under different time slices, and associate the multi-dimensional vector as a dynamic attribute with the dynamic complex network topology.

[0008] Preferably, the spatial parsing engine includes: The integration degree calculation module is configured to, for any target node in the network, first calculate the sum of the shortest topological distances from the target node to all other nodes in the network to obtain the total depth value, then calculate the average depth value by combining the total depth value with the total number of nodes in the network, and finally obtain the integration degree value reflecting the central position of the node by calculating the reciprocal of the average depth value and combining it with the spatial correction coefficient. The selectivity calculation module is configured to count the total number of times the shortest path between all node pairs in the entire process network passes through the target node, in order to characterize the probability that a node is selected as a necessary path during the work order flow. The scale-independent correction module is configured to calculate the average depth of the target node in the current network and compare it with the average depth of an ideal tree network with the same number of nodes to obtain a relative asymmetry index that is unaffected by the network size. The community discovery submodule is configured to use a community partitioning algorithm based on maximizing modularity to identify closely connected subgroup structures in the process network, reveal informal collaborative groups or structural silos formed by process isolation within the organization, and mark them in the form of independent color blocks or boundaries in the 3D terrain model.

[0009] Preferably, the dynamic visualization rendering unit includes: The terrain generation submodule is configured to construct a continuous 3D mesh space and map the node coordinates in the process topology to the horizontal plane of this 3D mesh space. The visual mapping submodule is configured to establish the association between data indicators and visual variables, mapping the real-time backlog of work orders or the average processing time to the color saturation of the terrain, so that high-frequency and high-obstacle nodes appear as towering and dark congested terrain. The multi-detail rendering submodule is configured to render only the core backbone path and highly integrated nodes from a macro perspective, and automatically load secondary nodes, detailed flow attributes, and specific work order list details from a micro perspective. The fluid dynamics simulation module is configured to simulate the work order data stream as a liquid flowing on the terrain. The flow rate corresponds to the work order processing speed, and the flow rate corresponds to the task density. By simulating the accumulation and overflow of fluid around the terrain peaks, the transmission path and impact range of process pressure are shown. The interactive control submodule is configured to support users in performing rotation, zoom, pan, and drill operations through a visual interface, and to support connecting to augmented reality devices to overlay 3D terrain models onto a real-world view of the physical office space.

[0010] Preferably, the process simulation and deduction unit includes: The topology editing module is configured to provide an interactive interface for performing editing operations such as merging nodes, deleting paths, or adding connections, and immediately copy the current logical topology to perform the modifications; The load simulation submodule is configured to run Monte Carlo simulations on a new topology replica based on the probability distribution of historical work order arrival rates, predicting the migration of work order flow paths and the changing trend of average processing cycles. The intelligent recommendation module is configured to automatically search for potential topology optimization schemes using reinforcement learning-based optimization algorithms, identify structural islands with integration levels below a preset threshold and close business relationships, and calculate the expected global accessibility improvement ratio after adding logical connections. The risk assessment submodule is configured to simulate the impact of single-point failures on the overall network during the simulation process, and calculate the changes in redundancy of the overall network and the probability of the emergence of new structural islands in the event of node failure or a surge in traffic. The strategy cloning module is configured to learn from historical process optimization cases. When the current process exhibits characteristics that are more similar to historical bottlenecks than a preset value, it automatically retrieves the best historical topology modification strategy for rehearsal.

[0011] Preferably, the system further includes: Distributed edge acquisition nodes are deployed in data centers in various regions. They have independent local caching, data preprocessing logic, and lightweight topology analysis functions. They are used to perform noise filtering and format normalization at the data source and exchange metadata with each other through peer-to-peer network protocols. The federated construction module is configured to receive local topology graphs constructed by each edge acquisition node, and use graph fusion algorithms to stitch the fragmented local topology into a global process network. At the same time, it identifies and processes redundant nodes that flow across regions, ensuring the uniqueness of each physical entity in the global topology. A high-performance parallel computing cluster is configured to divide the overall topology graph into multiple sub-plots and execute a breadth-first search algorithm in parallel on the graph processing unit cluster to quickly obtain integration and selectivity data in large-scale network environments.

[0012] Preferably, the system further includes: A one-way optical shutter interface is configured to ensure that data can only be transmitted unidirectionally from the business production network to the analysis network, preventing the reverse infiltration of instructions or sensitive data. The security audit module is configured to use national cryptographic algorithms to digitally sign and verify the collected work order event streams, and store all simulation simulation operation records and topology change history in a private chain ledger. The permission topology construction module is configured to synchronously build a logical topology based on permission control. By comparing the overlap between the business process topology and the permission topology, it automatically identifies and marks risky paths of unauthorized flow or illegal approval.

[0013] Preferably, the system further includes: The location awareness submodule is configured to record the physical movement trajectory of the personnel in real time by identifying signals from the personnel's personal mobile devices, and transmit it to the process topology building unit to increase the physical topology level; The line-of-sight accessibility analysis module is configured to calculate the visual integration between different workstations based on the layout information of the physical space, and to assess the impact of physical barriers or workstation orientation on the efficiency of informal communication. The real-world simulation exercise module is configured to collect the simulated work movement paths and operation times of employees under the virtual optimized process before the physical layout is adjusted, and input the feedback data into the process simulation and deduction unit for parameter correction.

[0014] Preferably, the spatial parsing engine further includes: The dynamic attraction correction module is configured to identify expert nodes whose processing capabilities exceed a preset threshold, calculate the attraction weight of each node, and perform nonlinear correction on the integration degree calculation formula to reveal the dynamic flow imbalance caused by differences in personnel capabilities. The functional matching degree analysis module is configured to identify the misalignment of responsibilities by analyzing the deviation between the selectivity of nodes and their preset functions, and automatically generate functional distribution optimization suggestions. The spatiotemporal retrospective module is configured to replay the dynamic evolution of the work order flow field over a past period by controlling the progress of the timeline, and to show the spread path of congestion in the business network through the animation effect of terrain undulation.

[0015] Compared with the prior art, the present invention has the following beneficial effects: 1. The work order intelligent management and efficiency analysis system based on multidimensional data visualization provided by this invention solves the problem that traditional process mining only focuses on linear time consumption indicators, introduces spatial syntax theory into the field of work order process analysis, and transforms abstract business processes into virtual city models with spatial semantics.

[0016] 2. Through quantitative calculation of integration and selectivity, this invention enables the system to accurately identify hidden bottlenecks and structural silos that are difficult to detect using traditional methods, and presents them in an intuitive form of three-dimensional terrain, thereby improving managers' ability to perceive the health status of processes.

[0017] 3. The system of this invention supports direct drag-and-drop modification of process topology through a visual interface, and real-time simulation and deduction of the impact of optimization schemes on spatial integration, realizing a paradigm shift from passive monitoring to active deduction.

[0018] 4. The system of this invention not only enhances the depth and accuracy of process diagnosis, but also reduces the trial and error cost of process optimization decisions, providing technical support for enterprises to achieve intelligent, visualized, and forward-looking business process governance. Attached Figure Description

[0019] Figure 1 This is a schematic diagram of the overall technical solution architecture of the present invention; Figure 2 This is a schematic diagram of the core principle framework for the quantification and evaluation of node integration degree and selection degree based on spatial syntax theory in this invention. Figure 3 This is a logical flowchart of the full-scale work order lifecycle event collection and dynamic network topology construction in this invention; Figure 4 This is a schematic diagram of the multi-level interaction relationship and data flow of business process logic mapped to three-dimensional spatial terrain in this invention; Figure 5 This is a logical flowchart of the process simulation and optimization scheme in this invention to predict the impact of the overall accessibility. Detailed Implementation

[0020] Example 1: Reference Figures 1 to 5 The work order intelligent management and efficiency analysis system based on multi-dimensional data visualization includes a work order data acquisition unit, a process topology construction unit, a spatial syntax analysis engine, a dynamic visualization rendering unit, and a process simulation and deduction unit. The work order data acquisition unit is used to connect to multiple business subsystems within the enterprise through a preset heterogeneous system adapter, and to capture and aggregate the lifecycle event stream of all work orders in real time.

[0021] The business subsystems include, but are not limited to, customer relationship management system, enterprise resource planning system, IT service management platform, and collaborative office system.

[0022] The work order data acquisition unit is internally configured with a data monitoring submodule, a protocol conversion submodule, and a data cleaning and deduplication submodule.

[0023] The data monitoring submodule is configured to capture every status change event of a work order from creation, allocation, response, processing, suspension, reassignment, verification, to closure by monitoring the database change logs of each business subsystem, calling application programming interfaces, or receiving messages pushed by the message queue.

[0024] The protocol conversion submodule is responsible for converting non-standard XML, JSON, or custom binary format work order messages generated by different systems into a standard structured data format.

[0025] The data cleaning and deduplication submodule uses preset cleaning rules to filter noise from the collected raw data, including removing invalid work orders with missing key timestamps, merging redundant status change records generated in a short period of time, and performing global deduplication based on the unique identifier of the work order, to ensure that the work order event stream used in subsequent analysis has high accuracy and logical consistency.

[0026] The process topology construction unit is connected to the work order data acquisition unit and is used to dynamically extract the logical structure of the business process and transform it into a complex network topology based on the cleaned work order event flow.

[0027] The process topology construction unit includes a node extraction submodule, a relationship mapping submodule, and a dynamic evolution monitoring submodule.

[0028] The node extraction submodule is configured to abstract each business link, processing point, or specific executor involved in the work order process into a vertex in the network graph, and assign multi-dimensional attribute labels to each vertex, such as the department, function type, average load capacity, and geographical location information.

[0029] The relationship mapping submodule establishes directional edges based on the movement order of work orders between different nodes. The weight of the edges is dynamically assigned based on the frequency of work orders flowing between the two nodes, the average flow time, and the flow success rate.

[0030] The dynamic evolution monitoring submodule introduces a time axis management mechanism and is configured to update the topology in real time with a preset time step. When the business process changes or a new temporary flow path is added, the unit can capture the changes in topology features, thereby generating a dynamic graph model that reflects the real-time running status of the business process.

[0031] The spatial syntax analysis engine interacts with the process topology construction unit to introduce spatial syntax theory for in-depth quantitative evaluation of the structural effectiveness of the business process.

[0032] The spatial parsing engine is configured to treat the abstract process topology graph as a virtual spatial system with topological depth, and to evaluate the control and accessibility of each node by calculating integration and selectivity metrics.

[0033] The calculation logic of the integration index is as follows: For any target node in the network, firstly calculate the shortest topological distance from the target node to all other nodes in the network, i.e., the number of edges traversed; then sum all the shortest topological distances to obtain the total depth value; further, standardize the total depth value according to the total number of nodes in the network to obtain the average depth value; finally, by calculating the reciprocal of the average depth value and combining it with a specific spatial correction coefficient, obtain the integration index reflecting the central position and publicness of the node in the overall network.

[0034] Higher integration indicates a more central and pivotal role for the node in the process. The selection metric is calculated by counting the number of times the shortest path between all node pairs in the entire process network passes through the target node.

[0035] The more times a node passes through a process, the higher the probability that it will be selected as a necessary path in the work order flow. The spatial syntactic analysis engine also integrates a community discovery submodule, which uses a community partitioning algorithm based on maximizing modularity to identify closely connected subgroup structures in the process network and reveal informal collaborative groups or structural silos formed by process isolation within the organization.

[0036] The dynamic visualization rendering unit is connected to the spatial syntax analysis engine and is used to transform dry quantitative data into an intuitive three-dimensional spatial terrain model.

[0037] The dynamic visualization rendering unit includes a terrain generation submodule, a visual mapping submodule, and an interactive control submodule.

[0038] The terrain generation submodule is configured to utilize the parallel computing power of the graphics processor to construct a continuous three-dimensional mesh space and map the node coordinates in the process topology to the horizontal plane of the three-dimensional mesh space.

[0039] The visual mapping submodule is configured to establish association rules between data indicators and visual variables: mapping the integration degree value of a node to the vertical height of the three-dimensional terrain, so that hub nodes with high integration degree form prominent peaks, while edge nodes with low integration degree form low-lying basins; mapping the selectivity degree value of a node to the texture density or roughness of the terrain surface to characterize the busyness of the path; and mapping the real-time backlog of work orders or the average processing time to the color saturation or hue of the terrain.

[0040] Under this mapping mechanism, high-frequency and high-impact nodes will automatically be visually represented as tall and dark-colored congestion peaks, allowing managers to identify explicit bottlenecks and implicit congestion areas in the process at a glance.

[0041] The interactive control submodule supports multi-touch and virtual reality device access, allowing users to perform operations such as rotation, scaling, and panning through gestures, observe the relationship between specific nodes from a microscopic perspective, or examine the topological balance of the overall process from a macroscopic perspective.

[0042] The process simulation and deduction unit is connected to the dynamic visualization rendering unit and the spatial syntax analysis engine to provide a closed-loop hypothesis-verification decision environment.

[0043] The process simulation and deduction unit is configured to receive topology editing instructions from users through a visual interface.

[0044] When a user manually merges two inefficient nodes, deletes redundant paths, or adds a connecting edge between two structural islands in a 3D terrain, the unit immediately copies the current logical topology and performs the modification operation on the copy.

[0045] The unit triggers the spatial parsing engine to recalculate the global integration degree and selectivity of the modified topology. The simulation also includes a load simulation submodule, which is configured to run Monte Carlo simulation on the new topology based on the probability distribution of historical work order arrival rates, to predict the migration of work order flow paths and the changing trend of average processing cycles.

[0046] The simulation results are presented in the form of a comparison layer, which is overlaid on the original terrain in a semi-transparent manner. The differences in performance indicators before and after optimization are automatically marked, such as the overall accessibility improvement ratio and the reduction value of critical path time, thus providing a scientific quantitative basis for process reengineering.

[0047] The work order data acquisition unit also includes an anomaly capture submodule, which is configured to monitor the health status of the data interfaces of each business system in real time.

[0048] If no work order heartbeat packet is received from a specific system within the preset monitoring period, the anomaly capture submodule will automatically trigger the reconnection mechanism and send an alarm message to the management backend.

[0049] To ensure the real-time nature of the data, the acquisition unit adopts a strategy that combines incremental acquisition with full verification. That is, it only captures incremental data of work orders whose status has changed during normal times, while performing full synchronization during preset off-peak periods to correct any possible cumulative data deviations and ensure that the basic data source for topology construction is always up-to-date.

[0050] In the aforementioned process topology construction unit, the definition of vertex attributes is not limited to static information but also includes dynamic performance characteristics. Each vertex internally maintains a multi-dimensional vector to store the node's real-time load, error rate, and collaboration satisfaction score under different time slices.

[0051] When constructing directed edges, the relationship mapping submodule considers the priority attribute of the work order. For high-priority work orders, the system establishes a dedicated green channel edge and assigns it a higher weight when calculating topology indicators to reflect the resource consumption characteristics of critical tasks.

[0052] The dynamic evolution monitoring submodule also supports a snapshot comparison function, allowing users to select the topology of any two historical time points for visual overlap analysis, and display the path optimization or degradation process of business processes over time through differentiated rendering.

[0053] In the deep computation process of the spatial parsing engine, in order to eliminate computational bias caused by different network sizes, the system introduces a relative asymmetric value computation logic.

[0054] The engine first calculates the average depth of the target node in the current network, and then compares the average depth with the average depth of an ideal tree network with the same number of nodes. By calculating the ratio and normalizing it, an absolute indicator unaffected by network size can be obtained, thereby enabling horizontal performance evaluation across different business departments and processes of different sizes.

[0055] The community discovery submodule not only identifies static organizational boundaries, but also analyzes the flow of work orders between non-functional departments to discover informal collaboration chains across departments, helping managers optimize cross-boundary business touchpoints.

[0056] The dynamic visualization rendering unit employs multi-level detail rendering technology when presenting 3D terrain. When the user is in a macroscopic view, the system only renders the core backbone paths and highly integrated nodes to reduce visual load and improve rendering frame rate. When the user zooms in to a specific area, the system automatically loads secondary nodes, detailed flow attributes, and specific work order list details within that area. The visual mapping submodule also supports custom theme functionality, allowing integration level to be mapped to terrain smoothness, or selection level to terrain transparency, based on different management preferences, thereby meeting the monitoring needs of different business scenarios.

[0057] The process simulation and deduction unit also integrates an intelligent recommendation module. This module utilizes a reinforcement learning-based optimization algorithm to automatically search for potential topology optimization solutions without manual user intervention. For example, the module will automatically identify two structural islands with low integration but close business relevance and suggest adding a direct logical connection. The system will pre-simulate the recommended optimization suggestions, and only when the predicted global accessibility improvement exceeds a preset threshold will the optimization solution and its expected benefits be displayed to the user in a visual interface with a highlighted prompt.

[0058] Example 2: Based on Example 1, this example provides a distributed intelligent work order management and control system based on edge computing architecture to cope with the data processing pressure in ultra-large-scale multinational enterprises or high-concurrency real-time business scenarios.

[0059] In this embodiment, the work order data acquisition unit is designed in a distributed deployment mode, consisting of multiple edge acquisition nodes deployed in data centers across various regions. Each edge acquisition node has independent local caching, data preprocessing logic, and lightweight topology analysis capabilities. This architecture allows for preliminary noise filtering and format regularization to be performed at the data generation source. Edge acquisition nodes exchange metadata through an encrypted peer-to-peer network protocol, enabling collaborative capture of cross-regional work order flow events. Only preprocessed feature vectors and key event records are transmitted to the central server, alleviating bandwidth pressure on the core backbone network.

[0060] The process topology construction unit adopts a federated construction mechanism in a distributed environment. Each edge node is responsible for constructing a local topology graph within its jurisdiction and calculating the feature values ​​of the local topology in real time. The central server is responsible for receiving these local topologies and using a graph fusion algorithm to stitch the fragmented local topologies into a complete global process network. During the stitching process, the unit automatically identifies and handles redundant nodes flowing across regions, ensuring the uniqueness of each physical entity in the global topology. The dynamic evolution monitoring submodule runs at the central end, using a stream computing engine to process the topology update streams from each edge node, achieving second-level synchronization of the global business flow field.

[0061] In this embodiment, the spatial parsing engine incorporates an acceleration strategy based on a high-performance parallel computing cluster. Since calculating the shortest path between all nodes in a large-scale network is a computationally intensive task, the engine divides the overall topology into multiple sub-plots and executes a breadth-first search algorithm in parallel on a GPU cluster to quickly obtain integration and selectivity data. The analysis engine in this embodiment adds a time-dimensional depth calculation, considering not only the physical hop count of the path but also the time-dimensional blocking effect. By calculating the average dwell time of a work order on a certain path and converting it into a virtual topological distance, the generated integration index more accurately reflects the effectiveness of business processes, rather than merely the tightness of logical connections.

[0062] In this embodiment, the dynamic visualization rendering unit employs a high-performance rendering engine based on Web graphics standards, supporting direct access to 3D terrain via a browser in a large-scale concurrent user environment. To further enhance the depth of visual expression, the unit introduces fluid dynamics simulation technology, simulating the work order data flow as liquid flowing on the terrain. Flow velocity represents the work order processing speed, and flow rate represents task density. When congestion occurs at a certain node, the simulated fluid accumulates and overflows around the peaks, creating a strong visual impact in conjunction with the height changes of the 3D terrain, allowing managers to intuitively perceive the transmission path and affected area of ​​process pressure.

[0063] In this embodiment, the process simulation and deduction unit enhances its risk assessment function. When simulating topology modifications, the system not only evaluates efficiency improvements but also simulates the impact of single-point failures on the overall network. For example, if a user attempts to merge two nodes, the simulation engine calculates how much the overall network redundancy will decrease if the merged node fails, and whether new structural islands will be created. This multi-objective simulation evaluation mechanism ensures that process optimization not only improves speed but also enhances the system's robustness and resilience.

[0064] In this embodiment, the work order data acquisition unit enhances its ability to process unstructured data. By integrating a natural language processing module, the system can extract key business semantic features from work order notes, attachments, and even instant messaging records, and use these features as additional attributes for nodes. For example, if hardware compatibility terms frequently appear in the work order description, the acquisition unit will automatically tag the work order node with a specific technical label. Based on this, the process topology construction unit can generate thematic topology maps based on business themes, enabling managers to observe the operational efficiency of business processes from technical, product, or customer dimensions.

[0065] In terms of spatial syntactic analysis, this embodiment introduces a dynamic attraction model. This model considers that in actual business operations, certain expert nodes or efficient departments, due to their superior business processing capabilities, will naturally attract more work orders. The engine calculates the attraction weight of each node and performs a non-linear correction to the integration degree formula. This allows the analysis results to not only reveal static defects in the process design but also discover dynamic imbalances caused by differences in personnel capabilities.

[0066] The visualization rendering unit has been deeply optimized for interactivity, supporting drill-down analysis. Users can drill directly down from the 3D process topography map to the lowest level of raw log records. Clicking on any congested area on the topography map will automatically pop up a related view, displaying the details of the backlog of work orders at the node, the real-time workload of the person in charge, and historical year-on-year data. This seamless switching between multiple scales and dimensions eliminates the information gap between macro-monitoring and micro-execution.

[0067] The simulation simulation unit also incorporates strategy cloning technology based on historical learning. The system automatically learns from past successful process optimization cases. When the current process exhibits similar bottleneck characteristics, the simulation engine automatically retrieves the optimal topology modification strategy from history and performs a pre-simulation in the current virtual environment, demonstrating to the user the possible expected effects of applying this optimal topology modification strategy in the current scenario. This intelligent decision-making assistance based on historical experience shortens the solution design cycle for managers.

[0068] Example 3: In this example, for government or military environments with high security requirements, the present invention provides a system architecture based on intranet closed loop and enhanced physical isolation.

[0069] In this embodiment, the work order data acquisition unit is equipped with a unidirectional optical gate interface to ensure that data can only be transmitted unidirectionally from the business production network to the analysis network, absolutely preventing any reverse infiltration of instructions or sensitive data. The acquisition unit internally employs strict national cryptographic algorithms for data signing and verification, ensuring that the work order event stream is not tampered with during the acquisition process. To maintain real-time performance in an isolated environment, the system adopts a reliable transmission enhancement mechanism based on the UDP protocol, which can restore complete work order topology information even under network jitter or packet loss conditions through forward error correction technology.

[0070] In this embodiment, the process topology construction unit adds a permission topology dimension. The system not only constructs a business flow topology but also simultaneously builds a logical topology based on permission control. By comparing the overlap between the business topology and the permission topology, the system can automatically identify potential risk paths involving unauthorized flows or unauthorized approvals. These risk paths will be marked with special red warning lines in subsequent visualization rendering, achieving deep integration of efficiency analysis and security auditing.

[0071] The spatial syntax analysis engine introduces a hierarchical constraint model to address the complex administrative levels in classified environments. When calculating integration degree, the system automatically considers the administrative affiliation between different nodes. If a work order's workflow crosses multiple security domains or administrative levels, the system adds a corresponding cross-domain penalty coefficient to its topology depth calculation. This results in an integration degree metric that more accurately reflects the actual communication costs and workflow difficulty under strict organizational constraints, rather than simply the length of the physical path.

[0072] In this embodiment, the dynamic visualization rendering unit is deployed on a dedicated command and control screen system. To adapt to the immersive environment of the command center, the rendering engine employs ray tracing-based rendering technology, resulting in more detailed and realistic representation of the 3D terrain. The system also includes a spatiotemporal rewind module, allowing administrators to control a progress bar at the bottom of the screen to replay the dynamic evolution of the work order flow over a period of time, much like playing a movie. Combined with the animation effects of terrain undulations, the entire process of how congestion gradually spreads from tiny nodes to the entire business network can be clearly observed.

[0073] In this embodiment, the process simulation and deduction unit focuses on stress testing and contingency plan verification. The system has built-in templates for various contingency situations, such as critical node offline or a five-fold increase in business volume. Users can quickly start simulations using these preset templates to observe where the overall process will crash under extreme conditions. The simulation engine calculates the system's throughput limits in real time and automatically marks the critical path that will fail first under stress. This provides the most intuitive technical support for developing contingency plans and resource backup strategies.

[0074] The system in this embodiment is also equipped with a hardware token-based secure access controller, allowing only authorized administrators to initiate topology modification commands. All simulation operation records and topology change history are stored in an immutable ledger based on a private blockchain, ensuring the traceability and audit compliance of every process optimization decision.

[0075] At the data acquisition level, to address potential data incompleteness issues caused by network isolation, the acquisition unit in this embodiment supports offline package import mode. The system can automatically identify and parse offline exported database backup files, and stitch offline data into the current dynamic topology through a timestamp calibration algorithm, ensuring that the system still possesses powerful panoramic analysis capabilities even in a state of network disconnection or semi-physical isolation.

[0076] In this embodiment, the spatial syntax engine also includes a function for analyzing job function matching. By analyzing the deviation between the selectivity of a node and its preset job function, the system can detect whether there is a misalignment of responsibilities. For example, if a node designed for edge review shows a high selectivity, it indicates that a large number of work orders that are not within its scope of responsibility are flooding the node. The system will automatically generate a job function distribution optimization proposal to assist in the dynamic fine-tuning of the organizational structure.

[0077] The visualization rendering unit also supports heatmap path display, which can spatially stack all work order trajectories over a period of time, forming footprints of varying depths on a 3D terrain. The deepest footprints represent the organization's habitual processes, while scattered footprints deviating from the main path reveal the randomness and irregularities in the process execution. This visual presentation method provides direct evidence for the organization's process standardization efforts.

[0078] In this embodiment, the simulation unit is also deeply integrated with the human resource management system. When merging or deleting simulation nodes, the system automatically retrieves the skill matrix of relevant personnel. If the proposed optimization involves assigning high-difficulty tasks to nodes with low skill weights, the simulation engine will automatically issue risk warnings and simulate the potential for increased average processing time and error rate, thereby ensuring the feasibility of the process optimization plan at the human resource level.

[0079] Example 4: This example provides a mobile implementation solution for work order management based on augmented reality (AR) technology, which aims to provide on-site managers with real-time, real-scene process analysis capabilities.

[0080] In this implementation, the dynamic visualization rendering unit is ported to a mobile terminal or AR headset. The interactive control submodule integrates the device's geolocation sensor, gyroscope, and camera. When managers wear AR devices and enter the office area, the system uses image recognition technology to locate specific departments or workstations and overlays the corresponding work order flow topography map onto the real physical space view in real time. This hybrid virtual-real presentation allows managers to directly perceive data-level congestion at the physical site where work orders are generated and processed.

[0081] The work order data acquisition unit has been enhanced with a location-aware submodule, enabling real-time recording of the physical movement trajectories of personnel. This geospatial data is transmitted to the process topology construction unit, which adds a physical topology layer to the logical topology. The system no longer only analyzes the logical flow of work orders between systems but also the physical transport paths of documents or materials. Through a spatial syntax analysis engine, the integration level of the physical space can be calculated, identifying bottlenecks in workflow caused by unreasonable office layouts.

[0082] The spatial parsing engine has been lightweighted for mobile devices, employing a model distillation-based computational strategy. The central server transforms complex computational results into lightweight feature models and pushes them to the mobile device. The mobile device only needs to perform real-time local interpolation calculations to present a smooth terrain undulation effect in the AR view. The engine has also added a distance attenuation coefficient, considering the impact of physical distance on collaboration efficiency when evaluating node influence.

[0083] The process simulation and deduction unit supports gesture-based drag-and-drop modification in an AR environment. Managers can directly select virtual terrain modules representing business processes within the AR viewpoint using gestures, dragging them from one physical area to another. The system will immediately simulate how changing the physical location of the office unit will affect the overall work order flow efficiency. This intuitive interaction method integrates office layout optimization with business process optimization.

[0084] To address the characteristics of mobile devices, the work order data acquisition unit employs low-power Bluetooth and Wi-Fi detection technologies. By identifying and processing signals from personnel's mobile devices, the system can accurately monitor the real-time personnel presence at each workstation. Based on this, the process topology construction unit can generate a bidirectional personnel-task mapping map. If a node is highlighted in red on the topology map, and location detection indicates a personnel shortage in the area, the system will automatically push support instructions to available personnel in nearby areas via mobile devices.

[0085] In this embodiment, the spatial syntax engine also incorporates visual penetration analysis. Since AR devices rely on visual guidance, the engine calculates the line-of-sight accessibility between different workstations within the current physical layout. Research indicates that good visual accessibility helps improve the efficiency of informal communication. Based on this, the system suggests adjusting physical barriers or workstation orientations to enhance visual integration under spatial syntax theory, thereby indirectly optimizing the communication and collaboration efficiency of work orders.

[0086] The visualization rendering unit supports voice interaction control on mobile devices. Managers can quickly adjust the presentation dimensions of the AR interface using voice commands, such as displaying traffic congestion information for the past hour or switching to a labor cost perspective. The terrain mapping submodule displays real-time work order priorities on terrain peaks with flashing effects at different frequencies, guiding managers to focus on anomalies that have the greatest impact on business operations first.

[0087] The simulation unit also features a live drill mode. Before actually rearranging office spaces, managers can have all employees wear lightweight receivers and simulate operations within a virtual optimized workflow. The system collects data on movement paths and operation times during the simulation and feeds this information back to the simulation engine for real-time correction. This realistic simulation based on real personnel participation improves the accuracy of process optimization solutions and enhances employee adaptability.

[0088] The system in this embodiment also integrates a real-time incentive module for mobile devices. Based on the contribution of each node derived from spatial syntactic analysis, the system automatically identifies individuals or teams that undertake high-integration loads and demonstrate excellent processing efficiency. In the AR interface, these outstanding nodes are given a golden halo effect, and the performance system automatically issues corresponding virtual badges or points rewards. This mechanism, which combines data visualization with employee behavior guidance, provides continuous internal motivation for work order management.

[0089] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "including," "comprise," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus.

[0090] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A work order intelligent management and efficiency analysis system based on multi-dimensional data visualization, characterized in that: include: The work order data acquisition unit is configured to acquire the lifecycle event data of all work orders in the enterprise information system in real time. The lifecycle event data includes work order creation, status change, handler information, flow path and processing time. The unit also cleans, normalizes and structures the raw data to form a standardized work order event flow. The process topology construction unit is configured to abstract each work order processing node as a vertex in the network graph based on the standardized work order event flow, and to abstract the flow relationship of work orders between different processing nodes as directed edges, thereby constructing a dynamic and complex network topology graph that reflects the overall business process logic. The spatial syntax analysis engine is configured to map the dynamic complex network topology graph into a virtual space structure. Through the integration degree and selectivity calculation model in spatial syntax theory, it quantitatively evaluates the penetration, reachability and centrality of each node in the virtual space structure and identifies high-obstacle nodes and structural island regions. The dynamic visualization rendering unit is configured to map the quantified and evaluated dynamic complex network topology graph into a three-dimensional terrain model with spatial depth, map the integration degree value of the node to the terrain height in the three-dimensional terrain model, and map the selection degree value of the node to the terrain texture density in the three-dimensional terrain model. The process simulation and deduction unit is configured to receive process topology modification instructions initiated for the three-dimensional terrain model, and recalculate the spatial integration changes of each node in real time based on the modified topology, and predict the impact of the process optimization scheme on the overall accessibility and flow efficiency.

2. The intelligent work order management and efficiency analysis system based on multi-dimensional data visualization according to claim 1, characterized in that, The work order data acquisition unit includes: A multi-source heterogeneous system adapter is configured to connect to customer relationship management systems, enterprise resource planning systems, service desk platforms, and collaborative office systems via preset interface protocols to obtain cross-system work order data. The data monitoring submodule is configured to capture status change events of work orders from creation, allocation, response, processing, suspension, transfer, verification to closure by monitoring the database change logs of various business systems, calling application interfaces, or receiving messages pushed by the message queue. The protocol conversion submodule is configured to convert non-standard work order messages into a standard structured data format. The data cleaning and deduplication submodule is configured to use preset cleaning rules to filter out noise from the collected raw data, remove invalid work orders that are missing key timestamps, merge redundant status change records generated within a preset time period, and perform global deduplication based on the unique identifier of the work order. The anomaly capture submodule is configured to monitor the health status of the data interface in real time. It automatically triggers a reconnection mechanism when no work order heartbeat packet is received within a preset period. It also performs full synchronization during off-peak hours to correct data deviations by combining incremental collection with full verification.

3. The intelligent work order management and efficiency analysis system based on multi-dimensional data visualization according to claim 2, characterized in that, The process topology construction unit includes: The node extraction submodule is configured to abstract the business links, processing positions or executors involved in the work order process into vertices, and assign each vertex a multi-dimensional attribute label containing information such as its department, function type, average load capacity and geographical location. The relation mapping submodule is configured to establish directed edges based on the movement order of work orders between different nodes, and dynamically assign weights to the edges based on the frequency of work orders flowing between two nodes, the average flow time, and the flow success rate. At the same time, it establishes logical channels with higher weights when processing high-priority work orders. The dynamic evolution monitoring submodule is configured to introduce a time window mechanism, generate stage topology snapshots at preset granularities of daily, weekly, or monthly, and support historical backtracking and trend analysis of process structure evolution. The performance characteristic maintenance module is configured to maintain a multi-dimensional vector within each vertex, store the real-time load, error rate, and collaboration satisfaction score of the node under different time slices, and associate the multi-dimensional vector as a dynamic attribute with the dynamic complex network topology.

4. The intelligent work order management and efficiency analysis system based on multi-dimensional data visualization according to claim 3, characterized in that, The spatial parsing engine includes: The integration degree calculation module is configured to, for any target node in the network, first calculate the sum of the shortest topological distances from the target node to all other nodes in the network to obtain the total depth value, then calculate the average depth value by combining the total depth value with the total number of nodes in the network, and finally obtain the integration degree value reflecting the central position of the node by calculating the reciprocal of the average depth value and combining it with the spatial correction coefficient. The selectivity calculation module is configured to count the total number of times the shortest path between all node pairs in the entire process network passes through the target node, in order to characterize the probability that a node is selected as a necessary path during the work order flow. The scale-independent correction module is configured to calculate the average depth of the target node in the current network and compare it with the average depth of an ideal tree network with the same number of nodes to obtain a relative asymmetry index that is unaffected by the network size. The community discovery submodule is configured to use a community partitioning algorithm based on maximizing modularity to identify closely connected subgroup structures in the process network, reveal informal collaborative groups or structural silos formed by process isolation within the organization, and mark them in the form of independent color blocks or boundaries in the 3D terrain model.

5. The intelligent work order management and efficiency analysis system based on multi-dimensional data visualization according to claim 4, characterized in that, The dynamic visualization rendering unit includes: The terrain generation submodule is configured to construct a continuous 3D mesh space and map the node coordinates in the process topology to the horizontal plane of this 3D mesh space. The visual mapping submodule is configured to establish the association between data indicators and visual variables, mapping the real-time backlog of work orders or the average processing time to the color saturation of the terrain, so that high-frequency and high-obstacle nodes appear as towering and dark congested terrain. The multi-detail rendering submodule is configured to render only the core backbone path and highly integrated nodes from a macro perspective, and automatically load secondary nodes, detailed flow attributes, and specific work order list details from a micro perspective. The fluid dynamics simulation module is configured to simulate the work order data stream as a liquid flowing on the terrain. The flow rate corresponds to the work order processing speed, and the flow rate corresponds to the task density. By simulating the accumulation and overflow of fluid around the terrain peaks, the transmission path and impact range of process pressure are shown. The interactive control submodule is configured to support users in performing rotation, zoom, pan, and drill operations through a visual interface, and to support connecting to augmented reality devices to overlay 3D terrain models onto a real-world view of the physical office space.

6. The intelligent work order management and efficiency analysis system based on multi-dimensional data visualization according to claim 5, characterized in that, The process simulation and deduction unit includes: The topology editing module is configured to provide an interactive interface for performing editing operations such as merging nodes, deleting paths, or adding connections, and immediately copy the current logical topology to perform the modifications; The load simulation submodule is configured to run Monte Carlo simulations on a new topology replica based on the probability distribution of historical work order arrival rates, predicting the migration of work order flow paths and the changing trend of average processing cycles. The intelligent recommendation module is configured to automatically search for potential topology optimization schemes using reinforcement learning-based optimization algorithms, identify structural islands with integration levels below a preset threshold and close business relationships, and calculate the expected global accessibility improvement ratio after adding logical connections. The risk assessment submodule is configured to simulate the impact of single-point failures on the overall network during the simulation process, and calculate the changes in redundancy of the overall network and the probability of the emergence of new structural islands in the event of node failure or a surge in traffic. The strategy cloning module is configured to learn from historical process optimization cases. When the current process exhibits characteristics that are more similar to historical bottlenecks than a preset value, it automatically retrieves the best historical topology modification strategy for rehearsal.

7. The intelligent work order management and efficiency analysis system based on multi-dimensional data visualization according to claim 6, characterized in that, The system also includes: Distributed edge acquisition nodes are deployed in data centers in various regions. They have independent local caching, data preprocessing logic, and lightweight topology analysis functions. They are used to perform noise filtering and format normalization at the data source and exchange metadata with each other through peer-to-peer network protocols. The federated construction module is configured to receive local topology graphs constructed by each edge acquisition node, and use graph fusion algorithms to stitch the fragmented local topology into a global process network. At the same time, it identifies and processes redundant nodes that flow across regions, ensuring the uniqueness of each physical entity in the global topology. A high-performance parallel computing cluster is configured to divide the overall topology graph into multiple sub-plots and execute a breadth-first search algorithm in parallel on the graph processing unit cluster to quickly obtain integration and selectivity data in large-scale network environments.

8. The intelligent work order management and efficiency analysis system based on multi-dimensional data visualization according to claim 7, characterized in that, The system also includes: A one-way optical shutter interface is configured to ensure that data can only be transmitted unidirectionally from the business production network to the analysis network, preventing the reverse infiltration of instructions or sensitive data. The security audit module is configured to use national cryptographic algorithms to digitally sign and verify the collected work order event streams, and store all simulation simulation operation records and topology change history in a private chain ledger. The permission topology construction module is configured to synchronously build a logical topology based on permission control. By comparing the overlap between the business process topology and the permission topology, it automatically identifies and marks risky paths of unauthorized flow or illegal approval.

9. The intelligent work order management and efficiency analysis system based on multi-dimensional data visualization according to claim 8, characterized in that, The system also includes: The location awareness submodule is configured to record the physical movement trajectory of the personnel in real time by identifying signals from the personnel's personal mobile devices, and transmit it to the process topology building unit to increase the physical topology level; The line-of-sight accessibility analysis module is configured to calculate the visual integration between different workstations based on the layout information of the physical space, and to assess the impact of physical barriers or workstation orientation on the efficiency of informal communication. The real-world simulation exercise module is configured to collect the simulated work movement paths and operation times of employees under the virtual optimized process before the physical layout is adjusted, and input the feedback data into the process simulation and deduction unit for parameter correction.

10. The intelligent work order management and efficiency analysis system based on multi-dimensional data visualization according to claim 9, characterized in that, The spatial parsing engine also includes: The dynamic attraction correction module is configured to identify expert nodes whose processing capabilities exceed a preset threshold, calculate the attraction weight of each node, and perform nonlinear correction on the integration degree calculation formula to reveal the dynamic flow imbalance caused by differences in personnel capabilities. The functional matching degree analysis module is configured to identify the misalignment of responsibilities by analyzing the deviation between the selectivity of nodes and their preset functions, and automatically generate functional distribution optimization suggestions. The spatiotemporal retrospective module is configured to replay the dynamic evolution of the work order flow field over a past period by controlling the progress of the timeline, and to show the spread path of congestion in the business network through the animation effect of terrain undulation.

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