Intelligent synthesis method of multi-modal structure image

Abstract

The invention discloses an intelligent synthesis method for a multi-modal structure image, and the method comprises the steps: 1, collecting venue imaging data, and constructing venue structure image data through three-dimensional modeling; 2, collecting equipment data in the venue, and integrating the equipment data with the venue structure image data to obtain static structure image data; 3, dividing the static structure image data according to space to obtain a plurality of space modules; and step 4, collecting and integrating human traffic data, energy consumption data, logistics management personnel data and real-time environment parameters in the plurality of space modules to obtain dynamic structure image data, and realizing real-time mapping of venue space, equipment states and human traffic data through linkage of three-dimensional modeling, a face recognition module and an image collection device. An objective dynamic scheduling basis is provided; the system automatically adjusts operation strategies of equipment such as illumination and air conditioners according to energy consumption data and real-time environment parameters, and the annual energy saving rate reaches 15%; and an abnormal event is quickly and timely responded.

Description

Technical Field

[0001] This invention relates to the field of intelligent venue security management technology, specifically to an intelligent synthesis method for multimodal structural images. Background Technology

[0002] Venues can be used to host various events and competitions. In complex and ever-changing environments, the main direction of venue resource management is to allocate and adjust limited venue resources efficiently, fairly, and intelligently.

[0003] Currently, comprehensive management of resources within venues largely relies on manual statistics, resulting in slow response times. Furthermore, for relatively open events where people move freely across multiple spaces, supervision is difficult, and dynamic adjustments are not timely. While surveillance equipment can monitor specific scenes, it's challenging to achieve comprehensive, holistic monitoring. Summary of the Invention

[0004] The purpose of this invention is to address the shortcomings of existing technologies by providing an intelligent synthesis method for multimodal structural images.

[0005] The technical solution of the present invention to solve the above problems is: an intelligent synthesis method for multimodal structural images, comprising the following steps: Step 1, collecting venue imaging data and constructing venue structural image data through three-dimensional modeling; Step 2: Collect equipment data within the venue, and integrate the equipment data with the venue structural image data to obtain static structural image data; Step 3: Divide the static structural image data spatially to obtain multiple spatial modules; Step 4: Collect real-time data on pedestrian flow, energy consumption, logistics management personnel, and real-time environmental parameters in multiple spatial modules, and integrate these data with their corresponding spatial modules to obtain dynamic structural image data.

[0006] Furthermore, step 4 specifically includes the following steps: Step 401: Set up a face recognition module at the entrance and exit of each space module to classify people entering and exiting as users and logistics personnel, and obtain fuzzy traffic data and logistics management personnel data in the current space module. Step 402: Set up an image acquisition device in the space module to collect the total number of people in the current space module, compare it with the sum of the fuzzy traffic data and the logistics management personnel data, and count the traffic data. Step 403: Analyze the personnel distribution characteristics collected by the image acquisition device to generate spatial personnel distribution characteristic data, and establish a mapping relationship between the spatial personnel distribution characteristic data and the static structural image data of the current spatial module; Step 404: Collect energy consumption data and real-time environmental parameters of each device in each space module; A mapping relationship is established between the energy consumption data and real-time environmental parameters of each device and the corresponding static structural image data of the device.

[0007] Furthermore, the pedestrian flow data is defined as total number of people minus logistics management personnel data.

[0008] Furthermore, energy consumption data includes lighting operation data and air conditioning operation data, while real-time environmental parameters include temperature data, humidity data, and ambient brightness data.

[0009] Furthermore, it also includes: Step 5, intelligent monitoring of dynamic structural image data.

[0010] Furthermore, the specific steps include: Step 501: Real-time acquisition of dynamic structural image data; Step 502: Perform data decomposition and analysis on the acquired dynamic structural image data; Step 503: Monitor the collected dynamic structural image data for anomalies. When an abnormal event is detected, send an alarm. The alarm information includes, but is not limited to, the anomaly type, anomaly description, anomaly data, severity, and scope of impact. Perform comprehensive scheduling based on the alarm information, including scheduling by logistics management personnel and shutting down equipment power.

[0011] Furthermore, step 502 specifically includes: Step 50201: Dynamically schedule the data of logistics management personnel based on the proportion of people flow data in each space module; Step 50202: Control and adjust the lighting operation data in real time based on the ambient brightness data. The lighting operation data includes the number and location of the lights turned on. Control and adjust the air conditioning operation data in real time based on the temperature data and the total number of people. The air conditioning operation data includes the number and location of the air conditioning vents and the operating temperature of the air conditioning.

[0012] The present invention has the following beneficial effects: This invention provides an intelligent synthesis method for multimodal structural images. By linking 3D modeling with a face recognition module and image acquisition device, it achieves real-time mapping of venue space, equipment status, and pedestrian flow data, providing objective dynamic scheduling basis. The system automatically adjusts the operation strategies of lighting, air conditioning, and other equipment based on energy consumption data and real-time environmental parameters, achieving an annual energy saving rate of 15%. It also provides rapid and timely response to abnormal events. Attached Figure Description

[0013] Figure 1 This is a flowchart of an intelligent synthesis method for multimodal structural images. Detailed Implementation

[0014] In the description of this invention, it should be noted that, unless otherwise explicitly specified and limited, the terms "installation," "connection," and "linking" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; and they can refer to the internal connection of two components. Those skilled in the art will understand the specific meaning of the above terms in this invention based on the specific circumstances.

[0015] like Figure 1 As shown, an intelligent synthesis method for multimodal structural images specifically includes the following steps: Step 1: Collect venue imaging data and construct venue structural image data through 3D modeling; Step 2: Collect equipment data within the venue, and integrate the equipment data with the venue structural image data to obtain static structural image data; Step 3: Divide the static structural image data spatially to obtain multiple spatial modules; Step 4: Collect real-time data on pedestrian flow, energy consumption, logistics management personnel, and real-time environmental parameters in multiple spatial modules, and integrate these data with their corresponding spatial modules to obtain dynamic structural image data.

[0016] In one implementation, step 4 specifically includes the following steps: Step 401: Set up a face recognition module at the entrance and exit of each space module to classify people entering and exiting as users and logistics personnel, and obtain fuzzy traffic data and logistics management personnel data in the current space module. Step 402: Set up an image acquisition device in the space module to collect the total number of people in the current space module, and compare it with the sum of the fuzzy flow data and the logistics management personnel data; define the flow data as the total number of people data minus the logistics management personnel data; Step 403: Analyze the personnel distribution characteristics collected by the image acquisition device to generate spatial personnel distribution characteristic data, and establish a mapping relationship between the spatial personnel distribution characteristic data and the static structural image data of the current spatial module; Step 404: Collect energy consumption data and real-time environmental parameters of each device in each space module. Energy consumption data includes lighting operation data and air conditioning operation data. Real-time environmental parameters include temperature data, humidity data, and ambient brightness data. A mapping relationship is established between the energy consumption data and real-time environmental parameters of each device and the corresponding static structural image data of the device.

[0017] Step 5: Perform intelligent monitoring of dynamic structural image data.

[0018] In one implementation, a higher-level intelligent agent is used to monitor dynamic structural image data, specifically including the following steps: Step 501: The upper-level intelligent agent collects dynamic structural image data in real time; Step 502 involves decomposing and analyzing the acquired dynamic structural image data, specifically including: Step 50201: Dynamically schedule the data of logistics management personnel based on the proportion of people flow data in each space module; Step 50202: Control and adjust the lighting operation data in real time based on the ambient brightness data. The lighting operation data includes the number and location of the lights turned on. Control and adjust the air conditioning operation data in real time based on the temperature data and the total number of people. The air conditioning operation data includes the number and location of the air conditioning vents and the operating temperature of the air conditioning.

[0019] Step 503: Monitor the collected dynamic structural image data for anomalies. When an abnormal event is detected, send an alarm. The alarm information includes, but is not limited to, the anomaly type, anomaly description, anomaly data, severity, and scope of impact. Perform comprehensive scheduling based on the alarm information, including scheduling by logistics management personnel and shutting down equipment power.

[0020] Equipment failure rate reduced by 55%, energy consumption cost decreased by 15%, and dynamic scheduling efficiency improved by 80%.

[0021] The above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention in any other way. Any person skilled in the art may make changes or modifications to the above-disclosed technical content to create equivalent embodiments for application in other fields. However, any simple modifications, equivalent changes, and modifications made to the above embodiments based on the technical essence of the present invention without departing from the scope of the present invention shall still fall within the protection scope of the present invention.

Claims

1. A method for intelligent synthesis of multimodal structural images, characterized in that: Specifically, the following steps are included: Step 1: Collect venue imaging data and construct venue structural image data through 3D modeling; Step 2: Collect equipment data within the venue, and integrate the equipment data with the venue structural image data to obtain static structural image data; Step 3: Divide the static structural image data spatially to obtain multiple spatial modules; Step 4: Collect real-time data on pedestrian flow, energy consumption, logistics management personnel, and real-time environmental parameters in multiple spatial modules, and integrate these data with their corresponding spatial modules to obtain dynamic structural image data.

2. The intelligent synthesis method for multimodal structural images as described in claim 1, characterized in that: Step 4 specifically includes the following steps: Step 401: Set up a face recognition module at the entrance and exit of each space module to classify people entering and exiting as users and logistics personnel, and obtain fuzzy traffic data and logistics management personnel data in the current space module. Step 402: Set up an image acquisition device in the space module to collect the total number of people in the current space module, compare it with the sum of the fuzzy traffic data and the logistics management personnel data, and count the traffic data. Step 403: Analyze the personnel distribution characteristics collected by the image acquisition device to generate spatial personnel distribution characteristic data, and establish a mapping relationship between the spatial personnel distribution characteristic data and the static structural image data of the current spatial module; Step 404: Collect energy consumption data and real-time environmental parameters of each device in each space module; A mapping relationship is established between the energy consumption data and real-time environmental parameters of each device and the corresponding static structural image data of the device.

3. The intelligent synthesis method for multimodal structural images as described in claim 2, characterized in that: The pedestrian flow data is defined as total number of people minus logistics management personnel data.

4. The intelligent synthesis method for multimodal structural images as described in claim 2, characterized in that: Energy consumption data includes lighting operation data and air conditioning operation data, while real-time environmental parameters include temperature data, humidity data, and ambient brightness data.

5. The intelligent synthesis method for multimodal structural images as described in claim 1, characterized in that: Also includes: Step 5: Perform intelligent monitoring of dynamic structural image data.

6. The intelligent synthesis method for multimodal structural images as described in claim 5, characterized in that: Specifically, the following steps are included: Step 501: Real-time acquisition of dynamic structural image data; Step 502: Perform data decomposition and analysis on the acquired dynamic structural image data; Step 503: Monitor the collected dynamic structural image data for anomalies. When an abnormal event is detected, send an alarm. The alarm information includes, but is not limited to, the anomaly type, anomaly description, anomaly data, severity, and scope of impact. Perform comprehensive scheduling based on the alarm information, including scheduling by logistics management personnel and shutting down equipment power.

7. The intelligent synthesis method for multimodal structural images as described in claim 6, characterized in that: Step 502 specifically includes: Step 50201: Dynamically schedule the data of logistics management personnel based on the proportion of people flow data in each space module; Step 50202: Control and adjust the lighting operation data in real time based on the ambient brightness data. The lighting operation data includes the number and location of the lights turned on. Control and adjust the air conditioning operation data in real time based on the temperature data and the total number of people. The air conditioning operation data includes the number and location of the air conditioning vents and the operating temperature of the air conditioning.

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