A smart building monitoring management method and system for multi-service convergence

Abstract

The application is suitable for the field of intelligent building construction, and provides an intelligent building monitoring management method and system for multi-service integration, which comprises the following steps: collecting multi-dimensional data in real time and constructing a preliminary resource pool; generating a space state atlas in combination with a building digital twin model; performing multi-objective optimization deduction based on the atlas to generate an elastic collaborative resource scheduling scheme; triggering self-regulation through real-time comparison during execution; and finally arranging effective modes into standardized management processes. The system comprises multi-dimensional data acquisition, digital twin fusion analysis, elastic collaborative scheduling, self-regulation and efficiency analysis modules. The application realizes dynamic and elastic management under multi-service integration, and improves building management efficiency and resilience.

Description

Technical Field

[0001] This invention belongs to the field of smart building construction, and in particular relates to a smart building monitoring and management method and system for multi-service integration. Background Technology

[0002] As the core direction of building intelligence, smart buildings are evolving towards integrated management that combines multiple services. With the development of technologies such as the Internet of Things, digital twins, and artificial intelligence, building management has expanded from single equipment monitoring (security, fire protection) to multi-service integrated scenarios such as environmental control, personnel scheduling, and equipment collaboration. It needs to address the dynamic needs of different business scenarios such as offices, commercial spaces, and conference rooms, and realize real-time interaction between the physical and digital spaces of the building. Improving management efficiency and user experience has become the core goal of the industry.

[0003] Existing smart building monitoring and management solutions are mostly based on a single business dimension, collecting environmental parameters and equipment operation data through sensors, and performing simple alarms or scheduling in combination with preset static thresholds; some solutions introduce digital twin technology, but it is mainly used for building model visualization and does not deeply integrate multi-dimensional data for dynamic analysis; resource scheduling mostly relies on fixed strategies and lacks a mechanism for overall planning and flexible coordination of backup resources. Summary of the Invention

[0004] The purpose of this invention is to provide a smart building monitoring and management method for multi-service integration, aiming to solve the technical problems existing in the prior art as identified in the background art.

[0005] This invention is implemented as follows: a smart building monitoring and management method for multi-service integration, the method comprising:

[0006] Real-time collection of multi-dimensional data within the building's physical space, and monitoring of equipment in dormant or low-load states, as well as backup physical spaces, constitute a reserve resource pool;

[0007] Based on multi-dimensional data and combined with a pre-built digital twin model of the building, the current environmental carrying capacity threshold and equipment operating margin of each functional area are analyzed to dynamically generate a spatial status map.

[0008] Based on the spatial state map, multi-objective optimization simulation is performed to generate a flexible collaborative resource scheduling scheme;

[0009] During the execution of the elastic collaborative resource scheduling scheme, the actual operating status parameters are continuously monitored and compared in real time with the preset values ​​in the elastic collaborative resource scheduling scheme. When the actual operating status parameters are detected to exceed the preset proportion threshold of the corresponding predicted value in the elastic collaborative resource scheduling scheme, a reflexive adjustment mechanism is triggered.

[0010] After completing the reflexive adjustment cycle, collect multi-dimensional data, spatial state maps, flexible collaborative resource scheduling schemes, execution instructions and final results for the entire event cycle, analyze the execution efficiency indicators of the resource scheduling strategy, and organize the verified effective collaborative models and parameter optimization results into a standardized spatial governance process.

[0011] As a further embodiment of the present invention, the multi-dimensional data includes: current event type, personnel distribution density, personnel movement trajectory, environmental parameters of each functional area, equipment operating status, and equipment load margin.

[0012] As a further aspect of the present invention, the dynamic generation of the spatial state map specifically includes:

[0013] Based on the building's functional zones and historical operational data, dynamic upper limits for environmental parameter carrying capacity and lower limits for critical equipment operating margins are set for each functional zone.

[0014] Based on physical space topology connections and personnel movement trajectory data, a potential transmission path model of spatial pressure is established. The physical space topology connections include channel connectivity and functional area proximity. The spatial pressure includes personnel density exceeding limits and environmental parameters approaching thresholds.

[0015] Based on equipment operating margin and environmental parameters, identify equipment resources and physical space whose current operating load is below the set margin lower limit, and mark them as available resource buffers;

[0016] The threshold margin mapping results, pressure transmission path model, and resource buffer identification results are combined with the building spatial structure information of the building digital twin model and rendered into a visualized spatial state map in real time.

[0017] As a further embodiment of the present invention, the spatial state map Represented as:

[0018] ;

[0019] in:

[0020] Node set , ;

[0021] Each node This represents a functional partition. For personnel density, For environmental parameter vectors, Device operating state vector, For carrying threshold vector, This is a resource buffer value;

[0022] edge set ;

[0023] Each edge Indicates the connection relationship between nodes;

[0024] Tensor Field Each element Representing coordinates Pressure value at:

[0025] ;

[0026] Let i be the population density. For spatial coordinate vectors, Let i be the position vector of node i. The distance decay exponent, This is the export distance influence coefficient;

[0027] Function Mapping ;

[0028] Represents a three-dimensional geometric model of a building. A visual texture based on stress values ​​and resource status.

[0029] As a further aspect of the present invention, the step of performing multi-objective optimization deduction to generate a flexible collaborative resource scheduling scheme specifically includes:

[0030] Multiple optimization objectives were set, including minimizing the risk of personnel gathering, minimizing overall energy consumption, maximizing equipment lifespan, ensuring environmental comfort in key areas, and minimizing emergency response time;

[0031] Load physical space structure constraints, critical equipment capacity limit constraints, safety specification constraints, and time window constraints;

[0032] The severity of the deviation is calculated based on the degree of deviation between the actual values ​​and the thresholds of each indicator in the spatial state map;

[0033] Based on the current event type and the severity of the spatial state map, the weight coefficients of each defined optimization objective in the overall objective function are adjusted in real time.

[0034] Under all constraints, find the combination of resource scheduling strategies that makes the objective functions after weighting reach the overall optimality, and output a flexible collaborative resource scheduling scheme that includes specific equipment control instructions, resource allocation paths, execution time windows and expected effects.

[0035] As a further aspect of the present invention, the real-time adjustment of the weight coefficients of each defined optimization objective in the overall objective function specifically involves:

[0036] ;

[0037] in, Indicates the first The optimization objective is in the next moment. The updated weight coefficients, Indicates the first The optimization objective at the current moment... The weighting coefficients, Indicates the amount of weight adjustment;

[0038] ;

[0039] in, For the first Real-time deviation of each optimization objective , This is the proportionality coefficient. The integral coefficient is... These are the differential coefficients. This represents the cumulative sum of deviations from the initial time to the current time. This indicates the rate of change of the deviation.

[0040] As a further aspect of the present invention, the continuous monitoring of actual operating status parameters and the real-time comparison with preset values ​​in the elastic collaborative resource scheduling scheme specifically includes:

[0041] While executing the elastic collaborative resource scheduling scheme, the actual values ​​of indicators are continuously collected. These actual values ​​include changes in population density in the target area, actual values ​​of environmental parameters, changes in the actual load of the controlled equipment, and the progress of resource allocation.

[0042] The actual values ​​of the collected indicators are compared in real time with the corresponding preset values ​​of the indicators in the elastic collaborative resource scheduling scheme to calculate the actual deviation rate.

[0043] Set deviation alarm thresholds for different indicators. When the actual deviation rate of any indicator continuously exceeds the corresponding deviation alarm threshold for a preset time, it is determined to exceed the preset value, and the excess ratio is calculated to see if it reaches the ratio threshold.

[0044] As a further embodiment of the present invention, the triggered reflexive adjustment mechanism is specifically as follows:

[0045] For deviations exceeding the proportional threshold, based on the data type of the deviation and the excess proportion, the system automatically queries the matching reserve resource pool and sends activation instructions to the corresponding reserve resources.

[0046] Based on the current actual situation and the activated reserve resource pool resources, the secondary adjustment strategy is recalculated on the basis of the implemented elastic collaborative resource scheduling scheme, and the elastic collaborative resource scheduling scheme being executed is adjusted.

[0047] The deviation that triggers the adjustment, the adjustment measures taken, and the effect data after adjustment are packaged as execution deviation data and fed back to the multi-dimensional data collected in real time.

[0048] Another objective of this invention is to provide a smart building monitoring and management system for multi-service integration, the system comprising:

[0049] The multi-dimensional data acquisition module is used to collect multi-dimensional data in the building's physical space in real time, and to monitor devices in dormant or low-load states and backup physical spaces to form a reserve resource pool.

[0050] The digital twin model fusion analysis module is used to analyze the current environmental carrying capacity threshold and equipment operating margin of each functional area based on multi-dimensional data and a pre-built digital twin model of the building, and dynamically generate a spatial status map.

[0051] The elastic collaborative resource scheduling module is used to perform multi-objective optimization simulations based on spatial state maps and generate elastic collaborative resource scheduling schemes.

[0052] The reflexive adjustment module is used to continuously monitor the actual operating status parameters and compare them with the preset values ​​in the elastic collaborative resource scheduling scheme during the execution of the scheme. When the actual operating status parameters exceed the preset ratio threshold of the corresponding predicted value in the elastic collaborative resource scheduling scheme, the reflexive adjustment mechanism is triggered.

[0053] The performance analysis module is used to collect multi-dimensional data, spatial state maps, flexible collaborative resource scheduling schemes, execution instructions and final results throughout the entire event cycle after the reflexive adjustment cycle is completed. It analyzes the execution performance indicators of the resource scheduling strategy and organizes the verified effective collaborative models and parameter optimization results into a standardized spatial governance process.

[0054] The beneficial effects of this invention are:

[0055] This solution employs a multi-dimensional data fusion, digital twin dynamic analysis, flexible collaborative scheduling, reflexive adjustment, and standardized processes to fundamentally address the insufficient adaptability of existing technologies in multi-service convergence scenarios. By collecting multi-dimensional data in real time and constructing a reserve resource pool, it overcomes the limitations of single-service data, providing a flexible foundation for resource scheduling.

[0056] By combining the building digital twin model to generate a spatial state map, a precise mapping between physical and digital space is achieved. It can dynamically present the environmental carrying capacity, pressure transmission and resource buffer status, and solve the lag of static threshold assessment.

[0057] Multi-objective optimization simulation dynamically adjusts objective weights according to the scenario, ensuring that resource scheduling is globally optimal in dimensions such as safety, efficiency, and experience, avoiding the one-sidedness of a single objective orientation; the reflexive adjustment mechanism enhances the system's resilience to cope with emergencies through real-time deviation monitoring and activation of reserve resources, making up for the rigidity defects of fixed strategies.

[0058] This ultimately led to a standardized space governance process, enabling the accumulation and reuse of experience and driving the continuous evolution of management capabilities. The overall solution, through a closed-loop design of perception-analysis-decision-execution-optimization, achieves dynamic, efficient, and adaptive management of smart buildings in multi-business integrated scenarios, significantly improving resource utilization efficiency and system responsiveness. Attached Figure Description

[0059] Figure 1 A flowchart illustrating a smart building monitoring and management method for multi-service integration provided in an embodiment of the present invention;

[0060] Figure 2 This is a flowchart of dynamically generating a spatial state map provided in an embodiment of the present invention;

[0061] Figure 3 This is a flowchart illustrating the generation of a flexible collaborative resource scheduling scheme provided in an embodiment of the present invention;

[0062] Figure 4 A flowchart for continuously monitoring actual operating status parameters and comparing them in real time with preset values ​​in the elastic collaborative resource scheduling scheme, as provided in an embodiment of the present invention;

[0063] Figure 5 A flowchart of the reflexive regulation mechanism provided in an embodiment of the present invention;

[0064] Figure 6 This is a structural block diagram of a smart building monitoring and management system for multi-service integration provided in an embodiment of the present invention. Detailed Implementation

[0065] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention.

[0066] Figure 1 A flowchart illustrating a smart building monitoring and management method for multi-service integration provided in an embodiment of the present invention is shown below. Figure 1 As shown, the method includes:

[0067] S100 collects multi-dimensional data within the building's physical space in real time and monitors devices in dormant or low-load states and backup physical spaces to form a reserve resource pool. The multi-dimensional data includes: current event type, personnel distribution density, personnel movement trajectory, environmental parameters of each functional area, equipment operating status, and equipment load margin.

[0068] The reserve resource pool can enhance the system's ability to cope with emergencies. When there is a sudden increase in personnel or excessive equipment load, it can quickly activate backup resources to relieve pressure. It helps to improve resource utilization, manage idle resources in a coordinated manner, and avoid waste. It can also support a reflexive adjustment mechanism. When the scheduling plan deviates from its execution, it provides supplementary resources for secondary adjustments, ensuring dynamic adaptation of resources in multiple business scenarios.

[0069] S200, based on multi-dimensional data and combined with a pre-built digital twin model of the building, integrates and analyzes the current environmental carrying capacity threshold and equipment operating margin of each functional area, and dynamically generates a spatial status map.

[0070] This step deeply integrates multi-dimensional data with the building's digital twin model, transforming the complex building's operational status into a perceptible and predictable spatial status map. Based on the actual uses of each functional area of ​​the building (such as office areas, commercial areas, and leisure areas) and historical operational data, dynamic environmental parameter carrying capacity thresholds and critical equipment operational margins are set for each area. These thresholds are not fixed values ​​but are automatically adjusted according to time periods and event types to ensure that the assessment of the building's status is more in line with the actual needs of the scenario.

[0071] Based on this, a potential transmission path model of spatial pressure is constructed by combining the topological connections of physical space (such as whether passages are connected and whether functional areas are adjacent) and personnel movement trajectory data. This model can simulate how pressures such as excessive personnel density and environmental parameters approaching thresholds can spread from one area to other areas.

[0072] By analyzing equipment operating margins and environmental parameters, the system can identify devices with current loads below the set margin (devices in a low-load state) and physical spaces (temporarily idle spaces) in real time, marking them as available resource buffers. The existence of these buffers provides elasticity for subsequent resource scheduling, ensuring that backup resources can be quickly called upon in case of sudden demand.

[0073] By combining the mapping results of the aforementioned threshold margin, the pressure transmission path model, and the resource buffer identification results with the three-dimensional spatial structure information of the building's digital twin model, a visualized spatial status map is generated through real-time rendering technology. This map not only intuitively displays the current status of each area but also presents the pressure values ​​at each point in the space through tensor field calculations, allowing managers to see both the current operational status and predict potential risk diffusion trends.

[0074] This step breaks through the limitations of traditional building monitoring, which relies on single-parameter monitoring and static threshold judgment, achieving dynamic, three-dimensional, and proactive perception of building status. The dynamic threshold setting allows for assessments of environmental carrying capacity and equipment operation that better reflect real-world changes, avoiding over- or under-protection issues associated with static thresholds. The construction of a pressure transmission path model enables the system to go beyond simply presenting the current state, anticipating the spread of risks and providing the possibility for proactive intervention rather than reactive response. Real-time identification of resource buffer zones lays the foundation for subsequent flexible resource scheduling, ensuring that idle resources can be quickly activated and utilized. Furthermore, the combination of digital twin visualization transforms complex, multi-dimensional data into intuitive spatial images, allowing managers to quickly understand the overall building operation status without specialized knowledge, significantly improving decision-making efficiency and accuracy.

[0075] like Figure 2 As shown, the dynamic generation of the spatial state map specifically includes:

[0076] S210, based on the building zoning functions and historical operation data, sets dynamic environmental parameter carrying capacity upper limits and key equipment operation margin lower limits for each functional zone;

[0077] S220, Based on the physical space topology connection relationship and personnel movement trajectory data, establish a potential transmission path model of spatial pressure. The physical space topology connection relationship includes channel connectivity and functional area proximity. The spatial pressure includes personnel density exceeding the limit and environmental parameters approaching the threshold.

[0078] S230, based on the equipment operating margin and environmental parameters, identifies the equipment resources and physical space whose current operating load is lower than the set margin lower limit, and marks them as available resource buffers;

[0079] S240 combines the threshold margin mapping results, pressure transmission path model, and resource buffer identification results with the building spatial structure information of the building digital twin model and renders them in real time into a visualized spatial state map.

[0080] In this step, the spatial state map Represented as:

[0081] ;

[0082] in:

[0083] Node set , ;

[0084] Each node This represents a functional partition. For personnel density, For environmental parameter vectors, Device operating state vector, For carrying threshold vector, This is a resource buffer value;

[0085] edge set ;

[0086] Each edge Indicates the connection relationship between nodes;

[0087] Tensor Field Each element Representing coordinates Pressure value at:

[0088] ;

[0089] Let i be the population density. For spatial coordinate vectors, Let i be the position vector of node i. The distance decay exponent, This is the export distance influence coefficient;

[0090] Function Mapping ;

[0091] Represents a three-dimensional geometric model of a building. A visual texture based on stress values ​​and resource status.

[0092] S300, based on the spatial state map, performs multi-objective optimization and simulation to generate a flexible collaborative resource scheduling scheme;

[0093] This step begins by setting multi-dimensional optimization goals based on the actual operational needs of the building. These goals include dimensions such as ensuring personnel safety and experience, as well as dimensions such as efficient resource utilization: minimizing overall energy consumption and maximizing equipment lifespan. They also cover the timeliness of emergency response: minimizing emergency response time, forming a comprehensive goal system covering safety, efficiency, and experience.

[0094] Along with setting the goal, a series of constraints are loaded to ensure the feasibility of the scheduling plan. These constraints include physical space structure constraints, key equipment capability limits, safety specification constraints, and time window constraints. These constraints serve as the boundaries of the decision-making process, ensuring that the generated plan is executable in real-world scenarios.

[0095] Furthermore, to ensure that the optimization more closely reflects the actual state of the building, the severity of deviation is calculated based on the degree of deviation between the actual values ​​of each indicator in the spatial state map and the threshold. For example, if the temperature is approaching the upper limit of the carrying capacity threshold, the severity of deviation will be marked as high, while the equipment load in another area is far below the lower limit of the margin, and the degree of deviation will be low. This quantified degree of deviation will serve as the basis for subsequent adjustment of target weights.

[0096] Based on the severity of the current event type and spatial status map, the weight coefficients of each optimization objective in the overall objective function are dynamically adjusted in real time. This adjustment is not a fixed rule, but rather a kind of adaptive adjustment mechanism: the weights are dynamically corrected through real-time deviation, deviation accumulation, and deviation change rate, so that the objective function better reflects the most pressing needs. For example, when a sharp increase in population density is detected in a certain area, the weight of minimizing the risk of population gathering will be greatly increased, while the weight of minimizing overall energy consumption will be reduced accordingly; if the operation is in a stable normal state, the weights of maximizing equipment lifespan and minimizing overall energy consumption will dominate.

[0097] Under the premise of satisfying all constraints, the algorithm seeks the combination of resource scheduling strategies that achieves the overall optimality of the weighted objective functions. This process considers not only the regulation of single-type resources but also cross-resource coordination. The final output of the elastic collaborative resource scheduling scheme is not an abstract strategy but a set of instructions containing specific execution details, such as specific parameters for equipment regulation, resource allocation paths, clear execution time windows, and expected effects, ensuring that the scheme can be directly interpreted and implemented by the execution layer.

[0098] This step, through dynamic weight adjustment of multiple objectives, allows the system to flexibly switch decision-making focus based on the priorities of the actual scenario, achieving a dynamic balance between safety, efficiency, and user experience. Comprehensive constraint loading ensures the feasibility of the solution, avoiding situations where the theoretically optimal solution is impractical. The flexible and collaborative scheduling mode breaks down the silos between equipment and spatial resources, enabling cross-system resource linkage and significantly improving resource utilization efficiency. Simultaneously, the specific execution details and expected effects in the solution provide a clear benchmark for subsequent execution and monitoring, ensuring precise and controllable scheduling actions. This closed-loop design of objective-constraint-dynamic optimization-precise execution gives resource scheduling both the flexibility to handle complex scenarios and the precision required for practical implementation.

[0099] like Figure 3As shown, the process of performing multi-objective optimization simulations to generate a flexible collaborative resource scheduling scheme specifically includes:

[0100] S310 sets multiple optimization goals, including minimizing the risk of personnel gathering, minimizing overall energy consumption, maximizing equipment lifespan, ensuring environmental comfort in key areas, and minimizing emergency response time;

[0101] S320 loads physical space structure constraints, critical equipment capability upper limit constraints, safety specification constraints, and time window constraints;

[0102] S330, the severity of deviation is calculated based on the degree of deviation between the actual value and the threshold of each indicator in the spatial state map;

[0103] S340, adjusts the weight coefficients of each defined optimization objective in the overall objective function in real time according to the current event type and the severity of the spatial state graph;

[0104] S350, under all constraints, seeks the combination of resource scheduling strategies that makes the objective functions after weighting reach the overall optimality, and outputs a flexible collaborative resource scheduling scheme that includes specific equipment control instructions, resource allocation paths, execution time windows and expected effects.

[0105] In this step, the real-time adjustment of the weight coefficients of each defined optimization objective in the overall objective function specifically involves:

[0106] ;

[0107] in, Indicates the first The optimization objective is in the next moment. The updated weight coefficients, Indicates the first The optimization objective at the current moment... The weighting coefficients, Indicates the amount of weight adjustment;

[0108] ;

[0109] in, For the first Real-time deviation of each optimization objective , This is the proportionality coefficient. The integral coefficient is... These are the differential coefficients. This represents the cumulative sum of deviations from the initial time to the current time. This indicates the rate of change of the deviation.

[0110] S400: During the execution of the elastic collaborative resource scheduling scheme, the actual operating status parameters are continuously monitored and compared in real time with the preset values ​​in the elastic collaborative resource scheduling scheme. When the actual operating status parameters are detected to exceed the preset ratio threshold of the corresponding predicted value in the elastic collaborative resource scheduling scheme, a reflexive adjustment mechanism is triggered.

[0111] After the flexible collaborative resource scheduling scheme is implemented, it will enter a continuous dynamic monitoring mode to collect the actual values ​​of various indicators related to the implementation effect of the scheme in real time. These indicators include personnel dimensions: the real-time trend of personnel density in the target area, whether it is continuously rising or gradually decreasing; environmental dimensions: the actual values ​​of parameters such as temperature, humidity, and air quality; as well as equipment and resource dimensions: the actual load changes of the controlled air conditioners, the actual carrying efficiency of elevators, and the activation progress of spare space.

[0112] The system then compares the actual values ​​of the collected indicators with the corresponding preset values ​​in the elastic collaborative resource scheduling scheme in real time to calculate the actual deviation rate. When the actual deviation rate of a certain indicator continuously exceeds its corresponding alarm threshold and reaches the preset duration, the system will determine that the indicator has exceeded the predicted value in the elastic collaborative resource scheduling scheme, and further calculate whether the excess ratio has reached the preset ratio threshold. If it has, the reflexive adjustment mechanism will be formally triggered.

[0113] The activation of the reflexive adjustment mechanism signifies that the system has entered the active intervention phase: First, based on the data type and excess ratio of the deviation, the system will automatically query the reserve resource pool and quickly locate matching backup resources: If the personnel density exceeds the standard, backup passages, temporary rest areas, or idle floor spaces may be activated; if the equipment load is insufficient, backup air conditioners, elevators, and other equipment in a dormant state will be activated, and precise activation instructions will be sent to these backup resources to ensure that the resources can be put into use in the shortest possible time.

[0114] Secondly, based on the current actual operating status and the activated reserve resources, a secondary adjustment strategy will be calculated on the basis of the original flexible collaborative resource scheduling scheme. This adjustment is not a complete rejection of the original scheme, but a targeted optimization. For example, if the air conditioning in the original scheme fails to effectively reduce the temperature in a certain area, the secondary adjustment may simultaneously increase the fresh air volume in that area, turn on the auxiliary heat dissipation of the air conditioning in the adjacent area, and guide people to move to a buffer space with a suitable temperature, so as to achieve cross-resource collaborative remedy.

[0115] Finally, the data on the reasons for the adjustment (such as which indicator deviated and by how much), the specific adjustment measures taken, and the actual effects after the adjustment are packaged and fed back to the multi-dimensional data acquisition module in real time as execution deviation data. This serves as the input for the next round of data acquisition and analysis, forming a closed loop of execution-monitoring-adjustment-feedback.

[0116] Real-time monitoring and deviation comparison ensure that the system can detect deviations between the plan and reality as soon as possible, preventing small deviations from accumulating into major problems; differentiated deviation alarm thresholds make monitoring more suitable for the characteristics of different scenarios, avoiding oversensitivity leading to frequent adjustments, and also avoiding sluggishness that misses risks; the reflexive adjustment mechanism, by activating reserve resources and secondary strategy adjustments, realizes the upgrade of resource scheduling from passive response to proactive remediation, significantly improving the system's response speed and handling capabilities to emergencies; and real-time feedback of deviation data makes each adjustment an experience for optimizing the system, driving the continuous evolution of resource scheduling strategies, and ultimately enabling smart buildings to maintain an efficient, safe, and comfortable operating state in dynamic changes.

[0117] like Figure 4 As shown, the continuous monitoring of actual operating status parameters and their real-time comparison with preset values ​​in the elastic collaborative resource scheduling scheme specifically includes:

[0118] S410, while executing the elastic collaborative resource scheduling scheme, continuously collect actual values ​​of indicators, including changes in population density in the target area, actual values ​​of environmental parameters, changes in the actual load of the controlled equipment, and resource allocation progress.

[0119] S420, The actual value of the collected indicator is compared with the preset value of the corresponding indicator in the elastic collaborative resource scheduling scheme in real time to calculate the actual deviation rate;

[0120] S430: Set deviation alarm thresholds for different indicators. When the actual deviation rate of any indicator continues to exceed the corresponding deviation alarm threshold for a preset duration, it is determined to exceed the preset value, and the excess ratio is calculated to see if it reaches the ratio threshold.

[0121] like Figure 5 As shown, the trigger-reflexive adjustment mechanism is specifically as follows:

[0122] S441, for deviations exceeding the proportional threshold, automatically query the matching reserve resource pool based on the data type of the deviation and the excess ratio, and send an activation command to the corresponding reserve resource.

[0123] S442, based on the current actual state and the activated reserve resource pool resources, recalculate the secondary adjustment strategy on the basis of the implemented elastic collaborative resource scheduling scheme, and adjust the elastic collaborative resource scheduling scheme that is being executed.

[0124] S443 packages the deviation that triggers the adjustment, the adjustment measures taken, and the effect data after adjustment into execution deviation data and feeds it back to the real-time collected multi-dimensional data in real time.

[0125] After completing the reflexive adjustment cycle, S500 collects multi-dimensional data, spatial state maps, flexible collaborative resource scheduling schemes, execution instructions and final results for the entire event cycle. It analyzes the execution efficiency indicators of the resource scheduling strategy and organizes the verified effective collaborative models and parameter optimization results into a standardized spatial governance process.

[0126] After the reflexive adjustment cycle is completed, a full-cycle data collection mechanism is initiated to comprehensively capture all kinds of information related to this event: including real-time collected multi-dimensional raw data, decision outputs during the process, and the final execution results. This data is not simply piled up, but is structured and integrated according to timeline and logical relationships to form a complete event archive, providing panoramic raw materials for subsequent analysis.

[0127] Based on the collected full-cycle data, we will conduct in-depth analysis focusing on the execution performance indicators of the resource scheduling strategy. These indicators include not only intuitive outcome indicators (whether the event resolution time is within the expected range, whether the final state returns to the normal threshold), but also process indicators: response latency of resource scheduling commands, activation efficiency of the reserve resource pool, the extent to which deviations are reduced after reflexive adjustment, and resource utilization indicators: utilization rate of standby equipment, actual carrying capacity efficiency of buffer space, and the input-output ratio of energy consumption and effectiveness. Through cross-analysis of these indicators, we can accurately identify the strengths and weaknesses of this scheduling strategy.

[0128] The analysis results will then be used to extract validated collaborative patterns and parameter optimization findings, which will be organized into standardized spatial governance processes. This standardization is not a rigid, fixed process, but rather a modular solution that retains core logic and flexible space: on the one hand, validated resource scheduling combinations, parameter thresholds, and execution steps will be solidified into reusable templates; on the other hand, variable interfaces will be reserved for these templates to adapt to different scenarios, ensuring that the standardized processes can be quickly implemented and flexibly adapted to new scenarios. These will be encoded into modular instructions, which can be directly invoked and parameters fine-tuned according to the actual scale when similar events occur again.

[0129] Figure 6 This is a structural block diagram of a smart building monitoring and management system for multi-service integration, such as... Figure 6 As shown, the system includes:

[0130] The multi-dimensional data acquisition module 100 is used to collect multi-dimensional data in the physical space of the building in real time, and to monitor the equipment and backup physical space in the dormant or low-load state, forming a reserve resource pool.

[0131] The Digital Twin Model Fusion Analysis Module 200 is used to analyze the current environmental carrying capacity threshold and equipment operating margin of each functional area based on multi-dimensional data and a pre-built digital twin model of the building, and dynamically generate a spatial status map.

[0132] The elastic collaborative resource scheduling module 300 is used to perform multi-objective optimization deduction based on the spatial state map and generate an elastic collaborative resource scheduling scheme.

[0133] The reflexive adjustment module 400 is used to continuously monitor the actual operating status parameters and compare them with the preset values ​​in the elastic collaborative resource scheduling scheme in real time during the execution of the elastic collaborative resource scheduling scheme. When the actual operating status parameters are detected to exceed the preset ratio threshold of the corresponding predicted value in the elastic collaborative resource scheduling scheme, the reflexive adjustment mechanism is triggered.

[0134] The performance analysis module 500 is used to collect multi-dimensional data, spatial state maps, flexible collaborative resource scheduling schemes, execution instructions and final results of the entire event cycle after the reflexive adjustment cycle is completed. It analyzes the execution performance indicators of the resource scheduling strategy and organizes the verified effective collaborative mode and parameter optimization results into a standardized spatial governance process.

[0135] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0136] The embodiments described above are merely illustrative of several implementations of the present invention, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of the present invention. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of the present invention, and these modifications and improvements all fall within the scope of protection of the present invention. Therefore, the scope of protection of this patent should be determined by the appended claims.

[0137] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A smart building monitoring and management method for multi-service integration, characterized in that, The method includes: Real-time collection of multi-dimensional data within the building's physical space, and monitoring of equipment in dormant or low-load states, as well as backup physical spaces, constitutes a reserve resource pool; Based on multi-dimensional data and combined with a pre-built digital twin model of the building, the current environmental carrying capacity threshold and equipment operating margin of each functional area are analyzed to dynamically generate a spatial status map. Based on the spatial state map, multi-objective optimization simulation is performed to generate a flexible collaborative resource scheduling scheme; During the execution of the elastic collaborative resource scheduling scheme, the actual operating status parameters are continuously monitored and compared in real time with the preset values ​​in the elastic collaborative resource scheduling scheme. When the actual operating status parameters are detected to exceed the preset proportion threshold of the corresponding predicted value in the elastic collaborative resource scheduling scheme, a reflexive adjustment mechanism is triggered. After completing the reflexive adjustment cycle, collect multi-dimensional data, spatial state map, flexible collaborative resource scheduling scheme, execution instructions and final results for the entire event cycle, analyze the execution efficiency indicators of the resource scheduling strategy, and organize the verified effective collaborative mode and parameter optimization results into a standardized spatial governance process. Specifically, the dynamically generated spatial state map includes: Based on the building's functional zones and historical operational data, dynamic upper limits for environmental parameter carrying capacity and lower limits for critical equipment operating margins are set for each functional zone. Based on physical space topology connections and personnel movement trajectory data, a potential transmission path model of spatial pressure is established. The physical space topology connections include channel connectivity and functional area proximity. The spatial pressure includes personnel density exceeding limits and environmental parameters approaching thresholds. Based on equipment operating margin and environmental parameters, identify equipment resources and physical space whose current operating load is below the set margin lower limit, and mark them as available resource buffers; The threshold margin mapping results, pressure transmission path model, and resource buffer identification results are combined with the building space structure information of the building digital twin model and rendered into a visualized spatial state map in real time. The spatial state map Represented as: ； in: Node set , ; Each node This represents a functional partition. For personnel density, For environmental parameter vectors, Device operating state vector, For carrying threshold vector, This is a resource buffer value; edge set ; Each edge Indicates the connection relationship between nodes; Tensor Field Each element Representing coordinates Pressure value at: ； Let i be the population density. For spatial coordinate vectors, Let i be the position vector of node i. The distance decay exponent, This is the export distance influence coefficient; Function Mapping ; Represents a three-dimensional geometric model of a building. A visual texture based on stress values ​​and resource status; The process of performing multi-objective optimization deduction to generate a flexible collaborative resource scheduling scheme specifically includes: Multiple optimization objectives were set, including minimizing the risk of personnel gathering, minimizing overall energy consumption, maximizing equipment lifespan, ensuring environmental comfort in key areas, and minimizing emergency response time; Load physical space structure constraints, critical equipment capacity limit constraints, safety specification constraints, and time window constraints; The severity of the deviation is calculated based on the degree of deviation between the actual values ​​and the thresholds of each indicator in the spatial state map; Based on the current event type and the severity of the spatial state map, the weight coefficients of each defined optimization objective in the overall objective function are adjusted in real time. Under all constraints, find the combination of resource scheduling strategies that makes the objective functions after weighting reach the overall optimal value, and output a flexible collaborative resource scheduling scheme that includes specific equipment control instructions, resource allocation paths, execution time windows and expected results. The weight coefficients of each optimization objective defined in the overall objective function are adjusted in real time as follows: ； in, Indicates the first The optimization objective is in the next moment. The updated weight coefficients, Indicates the first The optimization objective at the current moment... The weighting coefficients, Indicates the amount of weight adjustment; ； in, For the first Real-time deviation of each optimization objective , This is the proportionality coefficient. The integral coefficient is... The differential coefficients are... This represents the cumulative sum of deviations from the initial time to the current time. This indicates the rate of change of the deviation.

2. The method according to claim 1, characterized in that, The multi-dimensional data includes: current event type, personnel distribution density, personnel movement trajectory, environmental parameters of each functional area, equipment operating status, and equipment load margin.

3. The method according to claim 1, characterized in that, The continuous monitoring of actual operating status parameters and their real-time comparison with preset values ​​in the elastic collaborative resource scheduling scheme specifically includes: While executing the elastic collaborative resource scheduling scheme, the actual values ​​of indicators are continuously collected. These actual values ​​include changes in population density in the target area, actual values ​​of environmental parameters, changes in the actual load of the controlled equipment, and the progress of resource allocation. The actual values ​​of the collected indicators are compared in real time with the corresponding preset values ​​of the indicators in the elastic collaborative resource scheduling scheme to calculate the actual deviation rate. Set deviation alarm thresholds for different indicators. When the actual deviation rate of any indicator continuously exceeds the corresponding deviation alarm threshold for a preset time, it is determined to exceed the preset value, and the excess ratio is calculated to see if it reaches the ratio threshold.

4. The method according to claim 3, characterized in that, The specific trigger-reflexive adjustment mechanism is as follows: For deviations exceeding the proportional threshold, based on the data type of the deviation and the excess proportion, the system automatically queries the matching reserve resource pool and sends activation instructions to the corresponding reserve resources. Based on the current actual situation and the activated reserve resource pool resources, the secondary adjustment strategy is recalculated on the basis of the implemented elastic collaborative resource scheduling scheme, and the elastic collaborative resource scheduling scheme being executed is adjusted. The deviation that triggers the adjustment, the adjustment measures taken, and the effect data after adjustment are packaged as execution deviation data and fed back to the multi-dimensional data collected in real time.

5. The method according to claim 1, characterized in that, The system for implementing the aforementioned intelligent building monitoring and management method for multi-service integration includes: The multi-dimensional data acquisition module is used to collect multi-dimensional data in the building's physical space in real time, and to monitor devices in dormant or low-load states and backup physical spaces to form a reserve resource pool. The digital twin model fusion analysis module is used to analyze the current environmental carrying capacity threshold and equipment operating margin of each functional area based on multi-dimensional data and a pre-built digital twin model of the building, and dynamically generate a spatial status map. The elastic collaborative resource scheduling module is used to perform multi-objective optimization simulations based on spatial state maps and generate elastic collaborative resource scheduling schemes. The reflexive adjustment module is used to continuously monitor the actual operating status parameters and compare them with the preset values ​​in the elastic collaborative resource scheduling scheme during the execution of the scheme. When the actual operating status parameters exceed the preset ratio threshold of the corresponding predicted value in the elastic collaborative resource scheduling scheme, the reflexive adjustment mechanism is triggered. The performance analysis module is used to collect multi-dimensional data, spatial state maps, flexible collaborative resource scheduling schemes, execution instructions and final results throughout the entire event cycle after the reflexive adjustment cycle is completed. It analyzes the execution performance indicators of the resource scheduling strategy and organizes the verified effective collaborative models and parameter optimization results into a standardized spatial governance process.

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