Intelligent control data linkage control method and system for digital intelligent exhibition hall

Abstract

The invention provides a digital intelligent exhibition hall intelligent control data linkage control method and system, and relates to the technical field of intelligent exhibition halls, and the method comprises the steps: 1, collecting people flow density data in real time at an exhibition hall entrance gate position, and inputting the people flow density data into an edge calculation node; the edge computing node fuses temperature and humidity acquisition terminal data and an air conditioning equipment protocol, dynamically computes a thermal load compensation value based on the flow density, and generates an environment adjusting instruction; and step 2, inputting the people flow density data into a dynamic analysis unit, forming a projection curtain reference point based on the optical characteristics of the holographic projection curtain, and generating a projection area illumination basic parameter by combining the illumination intensity of the projection curtain reference point. According to the invention, multi-system cooperation is driven by people flow data, precise adaptation and dynamic adjustment of environment, illumination and navigation are realized, and the intelligent level, the operation efficiency and the safety and energy saving performance of the exhibition hall are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of smart exhibition halls, and in particular to a method and system for intelligent data linkage control of an indexed intelligent exhibition hall. Background Art

[0002] The current intelligent control system of digital exhibition halls faces some problems and challenges in practical application: Data islands and independent control modes are relatively common. Subsystems in the exhibition hall, such as access control / gates, environmental sensors, air conditioning, lighting, projection equipment, electronic display cabinet locks, service / security robots, etc., are mostly deployed and operated independently, making it difficult to effectively communicate data between systems.

[0003] At present, some studies have attempted to simply link pedestrian flow with some data such as air conditioning, or to consider static obstacles in robot path planning. However, there is still room for improvement in deep integration and closed-loop linkage control. For example, there is a lack of a mechanism to build a collaborative grid based on multiple spatial reference points to achieve precise control. Summary of the Invention

[0004] The technical problem to be solved by the present invention is to provide a method and system for intelligent data linkage control of a digital exhibition hall, so as to comprehensively improve the intelligence level of the exhibition hall.

[0005] In order to solve the above technical problems, the technical solutions of the present invention are as follows: In the first aspect, a method for intelligent data linkage control of a digital exhibition hall is provided, the method comprising: Step 1: Collect crowd density data in real time at the exhibition hall entrance gate and input the crowd density data into the edge computing node; the edge computing node integrates the temperature and humidity collection terminal data with the air conditioning equipment protocol, dynamically calculates the heat load compensation value based on the crowd density, and generates environmental adjustment instructions; Step 2: Input the crowd density data into the dynamic analysis unit, form a projection screen reference point based on the optical characteristics of the holographic projection screen, and generate the basic lighting parameters of the projection area in combination with the light intensity of the projection screen reference point; Step 3: Using the core showcase electronic lock position as the showcase positioning point, combining the entrance gate coordinates with the curtain reference point coordinates, and applying the spatial area division method to generate a spatial collaborative grid; Step 4: Generate a lighting compensation coefficient based on the thermal value of the grid unit flow, dynamically scale the lighting parameter intensity, and generate a robot navigation base path based on the status of the showcase electronic lock; In step 5, the navigation base path and the display cabinet boundary coordinates are input into the line segment intersection detection algorithm, and a collision avoidance instruction is output. The collision avoidance instruction and the lighting compensation coefficient are fed back to the air conditioning equipment, triggering adaptive adjustment of the ventilation intensity based on the heat load increment and the grid traffic density.

[0006] Secondly, the digital exhibition hall intelligent data linkage control system includes: The instruction generation module is used to collect real-time crowd density data at the exhibition hall entrance gate and input the crowd density data into the edge computing node. The edge computing node integrates the temperature and humidity collection terminal data with the air conditioning equipment protocol, dynamically calculates the heat load compensation value based on the crowd density, and generates environmental adjustment instructions. A parameter generation module is used to input crowd density data into the dynamic analysis unit, form a projection screen reference point based on the optical characteristics of the holographic projection screen, and generate basic lighting parameters of the projection area based on the light intensity of the projection screen reference point; The grid generation module is used to generate a spatial collaborative grid using the core showcase electronic lock position as the showcase positioning point, combining the entrance gate coordinates with the curtain reference point coordinates, and applying the spatial area division method; The path generation module is used to generate lighting compensation coefficients based on the thermal value of the grid unit flow, dynamically scale the lighting parameter intensity, and generate the robot navigation base path based on the status of the display cabinet electronic lock; The adaptive adjustment module is used to input the navigation base path and the display cabinet boundary coordinates into the line segment intersection detection algorithm to output collision avoidance instructions; the collision avoidance instructions and lighting compensation coefficient are fed back to the air conditioning equipment, triggering adaptive adjustment of the ventilation intensity based on the heat load increment and grid traffic density.

[0007] According to a third aspect, a computing device includes: one or more processors; The storage device is used to store one or more programs, and when the one or more programs are executed by the one or more processors, the one or more processors implement the method.

[0008] In a fourth aspect, a computer-readable storage medium stores a program, which implements the method when executed by a processor.

[0009] The above solution of the present invention includes at least the following beneficial effects: By dynamically calculating heat load compensation based on crowd density, the air conditioning system is linked to achieve adaptive temperature and humidity adjustments. This not only maintains a comfortable environment through heat load compensation when crowds are dense, but also avoids ineffective energy consumption, achieving a balance between energy conservation and comfort. Combining the optical properties of the holographic projection screen with light intensity, adaptive lighting parameters are generated based on crowd density, improving the clarity and viewing experience of the holographic projection. Through spatial collaborative grid division, exhibition halls are managed regionally according to equipment distribution and spatial coordinates, providing a precise spatial benchmark for lighting, navigation, and other controls, making various adjustments more targeted.

[0010] The lighting intensity is dynamically adjusted based on the thermal value of the grid flow of people, and the robot navigation base path is generated in combination with the status of the display cabinet and collision avoidance is performed to ensure safe and efficient robot inspections. At the same time, the lighting and robot activities are coordinated to avoid waste of resources. The navigation, lighting and other data are fed back to the air-conditioning system to achieve a deep linkage between heat load and air supply intensity, thereby improving the overall intelligence level and operating efficiency of the exhibition hall. BRIEF DESCRIPTION OF THE DRAWINGS

[0011] Figure 1 It is a flow chart of the intelligent data linkage control method for a digital exhibition hall provided by an embodiment of the present invention.

[0012] Figure 2 It is a schematic diagram of the digital exhibition hall intelligent control data linkage control system provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0013] Exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. Although exemplary embodiments of the present disclosure are shown in the accompanying drawings, it should be understood that the present disclosure can be implemented in various forms and should not be limited by the embodiments set forth herein. Rather, these embodiments are provided to enable a more thorough understanding of the present disclosure and to fully convey the scope of the present disclosure to those skilled in the art.

[0014] like Figure 1 As shown, an embodiment of the present invention proposes a method for intelligent data linkage control of a digital exhibition hall, which includes the following steps: Step 1: Collect crowd density data in real time at the exhibition hall entrance gate and input the crowd density data into the edge computing node; the edge computing node integrates the temperature and humidity collection terminal data with the air conditioning equipment protocol, dynamically calculates the heat load compensation value based on the crowd density, and generates environmental adjustment instructions; Step 2: Input the crowd density data into the dynamic analysis unit, form a projection screen reference point based on the optical characteristics of the holographic projection screen, and generate the basic lighting parameters of the projection area in combination with the light intensity of the projection screen reference point; Step 3: Using the core showcase electronic lock position as the showcase positioning point, combining the entrance gate coordinates with the curtain reference point coordinates, and applying the spatial area division method to generate a spatial collaborative grid; Step 4: Generate a lighting compensation coefficient based on the thermal value of the grid unit flow, dynamically scale the lighting parameter intensity, and generate a robot navigation base path based on the status of the showcase electronic lock; In step 5, the navigation base path and the display cabinet boundary coordinates are input into the line segment intersection detection algorithm, and a collision avoidance instruction is output. The collision avoidance instruction and the lighting compensation coefficient are fed back to the air conditioning equipment, triggering adaptive adjustment of the ventilation intensity based on the heat load increment and the grid traffic density.

[0015] In this embodiment of the present invention, heat load compensation values ​​are dynamically calculated based on crowd density, and air conditioning equipment is linked to achieve adaptive temperature and humidity adjustment. This not only maintains a comfortable environment through heat load compensation when crowds are dense, but also avoids ineffective energy consumption, achieving a balance between energy saving and comfort. Combining the optical properties of the holographic projection screen with light intensity, adaptive basic lighting parameters are generated based on crowd density, improving the clarity and viewing experience of the holographic projection. Through spatial collaborative grid division, exhibition halls are regionalized according to equipment distribution and spatial coordinates, providing a precise spatial benchmark for lighting, navigation, and other controls, making various adjustments more targeted.

[0016] The lighting intensity is dynamically adjusted based on the thermal value of the grid flow of people, and the robot navigation base path is generated in combination with the status of the display cabinet and collision avoidance is performed to ensure safe and efficient robot inspections. At the same time, the lighting and robot activities are coordinated to avoid waste of resources. The navigation, lighting and other data are fed back to the air-conditioning system to achieve a deep linkage between heat load and air supply intensity, thereby improving the overall intelligence level and operating efficiency of the exhibition hall.

[0017] In a preferred embodiment of the present invention, step 1 above collects crowd density data in real time at the exhibition hall entrance gate and inputs the crowd density data into an edge computing node; the edge computing node integrates the temperature and humidity collection terminal data with the air conditioning equipment protocol, dynamically calculates the heat load compensation value based on the crowd density, and generates environmental adjustment instructions, which may include: Step 100, collecting crowd density data in real time through the infrared counting sensor of the entrance gate; Step 101: The edge computing node receives crowd density data and temperature and humidity data uploaded by the temperature and humidity acquisition terminal, associates the temperature and humidity control parameters defined in the air conditioning equipment protocol, and matches the corresponding heat load compensation coefficient according to the crowd density value interval; Step 102 : Calculate the temperature and humidity compensation values ​​of the target space based on the heat load compensation coefficient, and generate an environmental adjustment instruction including the compensated target temperature value and target humidity value.

[0018] In this embodiment of the present invention, the entrance gate's infrared counting sensor features symmetrically mounted infrared transmitters and receivers on either side of the gate's passageway, forming multiple intersecting infrared sensing lines. As visitors pass through the gate, their bodies block the infrared sensing lines. The sensor determines the direction of traffic flow based on the number and sequence of blocked lines: if the line outside the exhibition hall is blocked first, followed by the line inside, it is considered an "entry"; otherwise, it is considered an "exit." The sensor aggregates the blockage signals every 0.5 seconds, recording "entry" counts as positive and "exit" counts as negative, cumulatively calculating the net traffic flow per unit time (e.g., 1 minute). At the same time, to avoid repeated counting caused by the same visitor repeatedly entering and exiting, the sensor will filter out repeated occlusion signals in a short period of time through the occlusion duration (usually set at an interval threshold of 0.5-2 seconds) - if the sensing line at the same position is blocked again within the threshold time, it is determined that the same person has not completely passed through and will not be counted repeatedly. Finally, the sensor will add the net flow rate per unit time to the current cumulative number of people in the exhibition hall, update and output the "current exhibition hall crowd density data" in real time, and the data format is "number of people / total exhibition hall area" (such as "30 people / 1000 square meters"), and send it to the edge computing node via wired transmission (such as Ethernet).

[0019] In step 101, after the edge computing node is started, it continuously receives real-time crowd density data from infrared counting sensors. It also receives real-time data uploaded by temperature and humidity collection terminals via wireless communication modules (such as LoRa). Temperature data is accurate to 0.1°C (e.g., "25.3°C") and humidity data is accurate to 1% (e.g., "55%"). Next, the edge computing node invokes the pre-stored air conditioning equipment protocol and extracts the basic control parameters defined in the protocol: these include the exhibition hall's baseline temperature range (e.g., "24-26°C"), the baseline humidity range (e.g., "50%-60%"), the air conditioning operating power standards at different temperatures and humidities, and the parameter adjustment accuracy specified in the protocol (e.g., temperature adjustment steps of 0.5°C and humidity of 5%). The node then divides the crowd density data into three preset intervals: "low density (≤5 people / 100 square meters)," "medium density (6-15 people / 100 square meters)," and "high density (≥16 people / 100 square meters)." The basis for classification is the heat dissipation of the human body per unit time (about 100-150 watts / hour for an adult at rest), combined with the ventilation efficiency of the exhibition hall - the more people there are, the greater the total amount of heat dissipated by the human body, and the stronger the heat load compensation required.

[0020] Based on the division results, the node matches the corresponding coefficient from the pre-stored "Crowd Density-Heat Load Compensation Coefficient Comparison Table": for example, the low-density area corresponds to a compensation coefficient of "1.0" (no additional compensation required), the medium-density area corresponds to "1.2" (20% heat load compensation required), and the high-density area corresponds to "1.5" (50% heat load compensation required). The setting of the compensation coefficient needs to be combined with the historical operation data of the exhibition hall to ensure that it matches the environmental impact caused by the actual heat dissipation of the human body.

[0021] In step 102 , the edge computing node first calculates the compensation value based on the reference temperature and humidity in the air-conditioning equipment protocol and the heat load compensation coefficient matched in step 101 .

[0022] Temperature compensation: If the current real-time temperature is within the reference range (e.g., 25°C) and is in the medium-density range (compensation coefficient 1.2), the value "reference temperature - (compensation coefficient - 1.0) × reference adjustment step × 2" is calculated to lower the target temperature to offset the temperature rise caused by human body heat dissipation.

[0023] Regarding humidity compensation: Human respiration increases ambient humidity, and the compensation logic is the opposite of temperature. For example, if the current humidity is 55% (within the baseline range), in the medium-density range, the calculation is "baseline humidity + (compensation coefficient - 1.0) × base humidity step size"—a base step size of 5%. The compensated target humidity is 55% + (0.2 × 5%) = 56%. In the high-density range, the compensation is 55% + (0.5 × 5%) = 57.5%. This slight increase in target humidity prevents excessive dehumidification and drying caused by the air conditioner. Finally, the node integrates the compensated target temperature (e.g., 24.5°C) and target humidity (e.g., 57.5%) into a standardized environmental adjustment command. This command includes the device address (specifying the air conditioner unit to be adjusted), parameter type (temperature / humidity), target value, and adjustment time limit (e.g., "reach target value within 10 minutes"). This command is sent to the air conditioning control system via the communication interface specified by the air conditioner equipment protocol (e.g., RS485 bus), triggering the device's operating status adjustment.

[0024] Direction recognition and repeated counting filtering by infrared counting sensors ensures real-time and accurate crowd data, avoiding inaccurate environmental adjustments due to data errors. Combined with crowd density interval matching compensation coefficients, heat load calculations are aligned with the human body's actual cooling needs, avoiding "fixed parameter adjustment" that can lead to overcooling / overheating, overdrying / overhumidifying the environment, thereby enhancing audience comfort. Linking air conditioning equipment protocol parameters ensures that adjustment instructions comply with equipment operating specifications, avoiding equipment failures caused by parameter conflicts. Clear adjustment steps and time limits ensure stable air conditioning operation. Dynamic adjustment of target temperature and humidity based on actual crowd flow avoids inefficient energy consumption when there are no people or few people (e.g., enhanced adjustment during high density, maintaining the baseline during low density), achieving a balance between energy conservation and comfort.

[0025] In a preferred embodiment of the present invention, the above step 2, inputting the crowd density data into the dynamic analysis unit, forming a projection screen reference point based on the optical characteristics of the holographic projection screen, and generating the projection area lighting basic parameters in combination with the illumination intensity of the projection screen reference point, may include: Step 200, using the optical center coordinates of the holographic projection screen as the projection screen reference point; Step 201, collecting the ambient light intensity value at the reference point of the projection screen in real time through the light sensor; In step 202, the dynamic analysis unit receives the crowd density data and the ambient light intensity value, matches the basic brightness parameters required for the projection area according to the preset crowd density-light intensity classification mapping relationship, and outputs a lighting control instruction containing the basic brightness parameters, specifically including: Compare the crowd density data with the preset crowd density threshold to generate the crowd density influencing factor; calculate the difference between the ambient light intensity value and the preset benchmark illumination value to generate the light compensation factor; According to the weighted sum of the crowd density influencing factor and the illumination compensation factor, the preset brightness parameter mapping table is indexed to obtain the basic brightness parameter value of the projection area, and a lighting control instruction containing the basic brightness parameter value is generated.

[0026] In an embodiment of the present invention, the physical installation boundaries of the holographic projection screen are determined by measuring the horizontal distance between the left and right edges of the screen (i.e., the screen width), and the vertical distance between the top and bottom edges (i.e., the screen height). For example, if the screen is 6 meters wide and 4 meters high, the horizontal position of its physical center is "left edge + 3 meters" and the vertical position is "bottom edge + 2 meters." Based on the exhibition hall's preset spatial coordinate system (with the floor in the lower left corner of the exhibition hall as the origin, the horizontal rightward direction is the X-axis, and the vertical upward direction is the Y-axis), the position of the screen's physical center is converted into coordinate values. Assuming that the X-axis coordinate of the left edge of the screen is 5 meters from the origin, and the Y-axis coordinate of the bottom edge is 1 meter from the origin, then the X-coordinate of the screen's optical center point is 5 meters + 3 meters = 8 meters, and the Y-coordinate is 1 meter + 2 meters = 3 meters. The final reference point coordinates are determined to be (8, 3), which is marked as the core monitoring point of the projection area.

[0027] In step 201, a high-precision light sensor (with an accuracy of 1 lux) is installed at the projection screen reference point (coordinates 8, 3) determined in step 200. The sensor's sensing surface faces the screen surface (maintaining a distance of 30 cm to avoid blocking the projection light). The sensor collects ambient light intensity data every 2 seconds, covering natural light (such as sunlight from windows), basic exhibition hall lighting (such as overhead spotlights), and light from other devices (such as visitors' mobile phone flashlights). The collected data is automatically converted to a standardized value (in lux). For example, natural light on a cloudy day may be 300 lux, direct sunlight on a sunny day at noon may reach 1500 lux, and basic exhibition hall lighting alone may be 500 lux. This data is transmitted in real time to the dynamic analysis unit via a wireless transmission module (such as WiFi). The transmission is accompanied by a collection timestamp (accurate to the second) and a sensor number (to prevent data confusion from multiple devices), ensuring that the dynamic analysis unit can obtain real-time light changes at the reference point.

[0028] Step 202, the process of generating the crowd density influencing factor: The dynamic analysis unit pre-stores three sets of crowd density thresholds: low threshold (≤10 people / 100 square meters), medium threshold (11-30 people / 100 square meters), and high threshold (≥31 people / 100 square meters). After receiving real-time crowd density data, it compares it with the thresholds: If the crowd density is 8 people / 100 square meters (below the lower threshold), it is judged as "sparse crowd flow" and a crowd density impact factor of 0.9 is generated (indicating that the projection brightness needs to be appropriately reduced to avoid excessive glare); If the crowd density is 20 people / 100 square meters (at the middle threshold), it is judged as "moderate crowd flow" and the impact factor is 1.0 (maintaining the baseline brightness); If the crowd density is 35 people / 100 square meters (higher than the upper threshold), it is judged as "densely populated" and an impact factor of 1.2 is generated (brightness needs to be increased to offset the light attenuation caused by crowd obstruction).

[0029] Generate lighting compensation factors: The dynamic analysis unit presets a reference illuminance value for the projection area (set according to the optimal display effect of the holographic projection, usually 500 lux). The difference between the real-time light intensity value collected in step 201 and the reference illuminance value is calculated: If the real-time light intensity is 600 lux (100 lux higher than the baseline), it means that the ambient light is too strong. The compensation factor is calculated as "1 - (difference / baseline value) = 1 - (100 / 500) = 0.8" (the projection brightness needs to be reduced to balance the strong light); If the real-time light intensity is 400 lux (100 lux lower than the baseline), it means that the ambient light is weak. The compensation factor is calculated as "1 + (absolute value of difference / baseline value) = 1 + (100 / 500) = 1.2" (the projection brightness needs to be increased to ensure clarity); If the real-time light intensity is equal to 500 lux, the compensation factor is 1.0 (no compensation required).

[0030] Calculate basic brightness parameters and generate instructions: The dynamic analysis unit performs a weighted calculation based on the crowd density factor and the lighting compensation factor (preset weighting of 60% for the crowd factor and 40% for the lighting factor). For example, if the crowd density factor is 1.2 (dense crowds) and the lighting compensation factor is 1.2 (low ambient light), the weighted sum is 1.2 × 60% + 1.2 × 40% = 1.2. The unit then indexes the preset "weighted sum-brightness parameter mapping table" (which specifies a weighted sum of 0.8 corresponding to 2000 lumens, 1.0 to 3000 lumens, and 1.2 to 4000 lumens), obtaining a base brightness parameter value of 4000 lumens. Finally, the dynamic analysis unit generates a lighting control command containing the following information: target area (the entire projection screen), base brightness parameter (4000 lumens), adjustment time (brightness switching within 10 seconds), and execution priority (higher than the fixed mode of the base lighting). This command is then sent via the wireless control bus to the lighting control system in the projection area, triggering brightness adjustment.

[0031] Using the screen's optical center as a reference point, the system ensures a strong correlation between light acquisition and the core projection area, preventing brightness parameter distortion caused by misaligned acquisition points. High-frequency (2-second / time) light intensity acquisition allows for rapid response to ambient light changes (such as cloud cover blocking sunlight or exhibition hall lights turning on and off), ensuring timely brightness adjustment. Through a weighted calculation of the crowd density factor (matching the light requirements of the audience) and the light compensation factor (balancing ambient light interference), the projection brightness adapts to the crowd size (brighter in dense crowds for improved visibility) while also offsetting ambient light fluctuations (lower in strong light and higher in weak light). Instructions clearly define the target area, parameter values, and timeliness, ensuring precise execution of the lighting system, avoiding adjustment delays or parameter deviations, and improving system linkage efficiency.

[0032] In a preferred embodiment of the present invention, step 3, using the core showcase electronic lock position as the showcase positioning point, combining the entrance gate coordinates and the curtain reference point coordinates, and applying a spatial region division method to generate a spatial collaborative grid, may include: Step 300: Obtain the coordinates of the entrance gate as a first spatial reference point, the coordinates of the optical center of the holographic projection screen as a second spatial reference point, and the coordinates of the physical position of the core display cabinet electronic lock as a third spatial reference point, and establish a rectangular coordinate system for the exhibition hall based on these three spatial reference points. Step 301: Based on the exhibition hall's spatial rectangular coordinate system, locate and collect the physical location coordinates of all display cabinets and security equipment to generate a spatial equipment coordinate set; Step 302: Input the device spatial coordinate set into a preset neighbor assignment rule, calculate the straight-line distance between each device coordinate and each reference point, assign the device to the corresponding reference point with the smallest straight-line distance, and output a reference point-device assignment relationship table; Step 303 , based on the reference point-device allocation relationship table, automatically generate space separation boundary lines between adjacent reference points according to the device distribution density; Step 304: divide the space into grid units covering the entire exhibition hall according to the spatial separation boundary line, and mark each grid unit with the associated reference point number and the type of equipment contained therein.

[0033] In this embodiment of the present invention, the physical coordinates of three spatial reference points are obtained: First spatial reference point (entrance gate): Measure the center point of the entrance gate, using the lower left corner of the exhibition hall floor as a temporary origin (for ease of measurement). Use a laser rangefinder to measure the horizontal distance between the gate center point and the temporary origin to be 8 meters (along the long axis of the exhibition hall) and 2 meters (along the short axis). The recorded coordinates are (8, 2).

[0034] The second spatial reference point (optical center of the holographic projection screen): uses the coordinates of the optical center of the screen determined in step 200, assuming it is (15, 5) (i.e., 15 meters horizontally and 5 meters vertically from the temporary origin).

[0035] Third spatial reference point (core display cabinet electronic lock): Find the installation location of the core display cabinet's electronic lock (usually in the middle of the right side of the display cabinet front), measure its horizontal distance to the temporary origin to be 10 meters and the vertical distance to be 8 meters, and record the coordinates as (10, 8).

[0036] A spatial rectangular coordinate system is established based on these three reference points: Take the first reference point (8, 2) as the origin of the coordinate system (the adjusted origin), and set the direction of the line from the first reference point to the second reference point (15, 5) as the positive direction of the X-axis (calculated along the line, each meter corresponds to 1 coordinate unit); The direction perpendicular to the X-axis and pointing into the exhibition hall is defined as the positive Y-axis. Ensure that the coordinates of the three reference points within this coordinate system are: the first reference point (0, 0), the second reference point (assuming the straight-line distance between the two points is 7.6 meters, so the coordinates are (7.6, 0)), and the third reference point (calculated based on its relative position to the origin, ultimately determined to be (3.2, 5.8)). Once the coordinate system is established, all subsequent device coordinates and grid divisions are based on this system to ensure uniformity and accuracy in spatial positioning.

[0037] In step 301, based on the rectangular coordinate system established in step 300, technicians collect data on the location of all showcases (including non-core showcases) and security equipment (such as cameras and infrared alarms) in the exhibition hall: Display Case Coordinate Collection: For each display case, select the lower left corner of its front face (close to the ground) as the location point. Use a laser rangefinder to measure the X and Y axis values ​​of this point in the coordinate system. For example, if the location point of a typical display case is 3 meters on the X axis and 2 meters on the Y axis from the coordinate system origin, the coordinates are recorded as (3, 2) and the device type is marked as "Display Case - Cultural Relics."

[0038] Security device coordinate collection: For cameras, the projection position of the lens center (perpendicular to the ground) is used as the coordinate point; for infrared alarms, the perpendicular to the ground of the mounting base is used as the coordinate point. For example, the projection coordinates of a camera are (12, 4), and the device type is labeled "Security - Camera."

[0039] The coordinate data of all devices is summarized into a "spatial device coordinate set" in the format of "device unique ID + (X coordinate, Y coordinate) + device type", such as "ZG-002 + (5, 3) + display cabinet - technology" and "AF-005 + (9, 6) + security - alarm", ensuring that each device has a unique and clear location record in the coordinate system.

[0040] Step 302: Input the spatial device coordinate set generated in step 301 into the system and perform device attribution classification according to the preset neighbor allocation rule: Distance calculation logic: For each device's coordinates (X, Y), calculate the straight-line distance from each device to the three spatial reference points. For example, the coordinates of a display cabinet are (5, 3): Distance to the first reference point (0, 0): By measuring the difference between the two points on the X and Y axes (5-0=5, 3-0=3), according to the straight-line distance logic (i.e., "the hypotenuse of the right triangle formed by the horizontal difference and the vertical difference"), it is determined that the distance is less than the distance to the second and third reference points (assuming the second reference point is (7.6, 0) and the third reference point is (3.2, 5.8), the horizontal difference from the display cabinet to the second reference point is 2.6, and the vertical difference is 3, so the distance is greater; the horizontal difference from the third reference point is 1.8, and the vertical difference is 2.8, so the distance is also greater).

[0041] Attribution determination: After comparing the three distances, the showcase is assigned to the first reference point with the smallest distance.

[0042] Generate a relationship table: The system automatically generates a "reference point-device assignment relationship table" containing columns such as device ID, device coordinates, associated reference point number (1 / 2 / 3), and distance value (minimum distance). For example, "ZG-002+(5,3)+1+6.5 meters" clearly identifies each device's location.

[0043] In step 303, based on the relationship table in step 302, the system analyzes the density of device distribution and automatically generates spatial separation boundary lines between adjacent reference points: Density analysis: Counts the coordinate distribution range of the devices at each benchmark. For example, the devices at the first benchmark (No. 1) are concentrated between 0-6 meters on the X axis and 0-4 meters on the Y axis; the devices at the second benchmark (No. 2) are concentrated between 6-12 meters on the X axis and 0-3 meters on the Y axis; and the devices at the third benchmark (No. 3) are concentrated between 2-8 meters on the X axis and 4-8 meters on the Y axis.

[0044] Boundary Line Delineation: For adjacent benchmarks (e.g., No. 1 and No. 2), the boundary is determined by taking the midpoint between the two equipment distribution ranges. Since the maximum X-axis distance of No. 1 is 6 meters, and the minimum X-axis distance of No. 2 is 6 meters, the vertical boundary is drawn at X=6 meters. For No. 1 and No. 3, since the maximum Y-axis distance of No. 1 is 4 meters, and the minimum Y-axis distance of No. 3 is 4 meters, the horizontal boundary is drawn at Y=4 meters.

[0045] Boundary line verification: Ensure that all devices are within the boundary line of their respective reference points (for example, all devices No. 1 are within the area where X < 6 meters and Y < 4 meters). If there are devices that cross the boundary, fine-tune the boundary line position (for example, ± 0.5 meters) to ultimately form a closed boundary line network covering the entire exhibition hall.

[0046] Step 304: Based on the spatial separation boundary lines generated in step 303, the system automatically divides the space into grid cells covering the entire exhibition hall: Grid size setting: Dynamically adjust the grid size based on equipment density. Equipment-dense areas (such as display cabinets) use a small grid of 0.5m x 0.5m, while equipment-sparse areas (such as aisles) use a large grid of 1m x 1m. For example, the display cabinet-dense area at reference point 1 would have a grid interval of 0.5m on the X axis and 0.5m on the Y axis.

[0047] Grid attribute annotation: Each grid cell is annotated with three key pieces of information: ① The associated reference point number (e.g., "No. 1"); ② The device type (e.g., "3 showcases, 1 camera"); and ③ The coordinates of the grid center point (for later positioning). For example, a grid center point with coordinates (2.5, 1.5) would be annotated as "Reference point No. 1 + 2 showcases + no security equipment."

[0048] Full coverage verification: Ensure that all grids are seamlessly connected, with no overlapping or missing areas (including wall corners, channel corners, etc.), ultimately forming a complete "spatial collaborative grid".

[0049] A unified rectangular coordinate system is established using three core reference points, eliminating discrepancies in coordinate systems for different device locations. The spatial device coordinate set comprehensively covers display cabinets and security equipment. Incorporating neighbor assignment rules clarifies device ownership, making each device's "responsible reference point" clearly traceable and laying the foundation for targeted control (such as lighting adjustment in a specific reference point area). Boundary lines and grids generated based on device distribution density ensure clear separation of adjacent areas.

[0050] In a preferred embodiment of the present invention, the above step 4, generating a lighting compensation coefficient based on the thermal value of the grid unit flow, dynamically scaling the lighting parameter intensity, and generating a robot navigation base path based on the state of the electronic lock of the showcase, may include: Step 400: Collect the heat value data of the crowd flow of each grid unit in real time, and determine the lighting compensation coefficient based on the heat value data of the crowd flow and the preset projection brightness classification threshold; Step 401 dynamically adjusts the intensity level of the basic lighting parameters of the projection area based on the lighting compensation coefficient, and obtains the switch status signal of the core display cabinet electronic lock in real time. When the electronic lock status signal is on, a navigation base path coordinate sequence for the robot inspection is generated with the physical position coordinates of the electronic lock as the starting point.

[0051] In this embodiment of the present invention, infrared thermal imaging sensors deployed in each grid cell (one per grid, with a sampling frequency of 5 seconds per time) collect crowd flow thermal value data in real time. The crowd flow thermal value is expressed using an index value of 0-100: 0 means no one in the grid, 30-50 means 3-5 people are staying, and 80-100 means a dense gathering of more than 8 people (the higher the value, the denser the crowd flow). For example, if the thermal values ​​collected three times in a row for a grid cell are 65, 68, and 70, respectively, the system takes the average value of 67 as the current effective thermal value of the grid. The system calls the preset "projection brightness grading threshold table", which divides the thermal value into 5 levels and corresponding compensation coefficients: Thermal value 0-20 (sparse crowd): compensation coefficient 0.8 (reduce lighting intensity to avoid energy waste); 21-40 (less traffic): compensation coefficient 0.9; 41-60 (moderate traffic): compensation coefficient 1.0 (maintain basic brightness); 61-80 (dense crowds): compensation factor 1.2 (enhanced brightness and improved visibility); 81-100 (dense crowds): compensation factor 1.5 (significantly enhances brightness to meet occlusion and viewing needs).

[0052] Compare the effective thermal value of the grid unit with the threshold: if the thermal value of a grid is 67, which falls between 61 and 80, the corresponding lighting compensation coefficient is 1.2; for a projection area containing multiple grids, the system takes the average of the compensation coefficients of each grid as the final lighting compensation coefficient of the area (for example, a projection area contains 3 grids, with compensation coefficients of 1.2, 1.0, and 1.2 respectively, and the average is 1.13, which is rounded to 1.1).

[0053] In step 401, the system first retrieves the basic lighting parameters for the projection area generated in step 202 (e.g., a basic brightness of 3000 lumens). Combined with the lighting compensation factor determined in step 400 (e.g., 1.1), it calculates the actual adjusted lighting intensity: multiply the basic parameter value by the compensation factor (i.e., 3000 lumens × 1.1 = 3300 lumens). The system also sets intensity level limits: a minimum of no less than 70% of the basic parameter (to avoid excessive darkness) and a maximum of no more than 180% of the basic parameter (to avoid damage to exhibits due to excessive brightness). For example, if the compensation factor is 1.5, the basic parameter is 3000 lumens, which will be adjusted to 4500 lumens. However, since the upper limit of 180% is 5400 lumens, the final setting is 4500 lumens. If the compensation factor is 0.8, the adjusted setting is 2400 lumens, which is higher than the 70% upper limit of 2100 lumens, so the setting is 2400 lumens. The adjusted parameters are transmitted in real time via wireless signals to the projection area lighting controller, triggering brightness adjustment. The entire process is completed within 10 seconds.

[0054] Generate robot navigation base path: The system receives status signals from the electronic locks of core display cases in real time (transmitted via a low-power Bluetooth module, with the signal updated every second): when the electronic lock is closed, the signal is "0"; when it is open (e.g., when a staff member opens the display case to replace or inspect an exhibit), the signal is "1." When a "1" signal is detected, the system immediately retrieves the physical location coordinates of the electronic lock of the display case (from the spatial rectangular coordinate system established in step 300, such as (10, 8)) and sets it as the starting point for the robot inspection. Next, based on the principle of "covering all core display cases and passing through high-value display cases," the system selects the target display cases that need to be inspected (e.g., numbered ZG-01, ZG-03, and ZG-05) and obtains the coordinates of the electronic locks of these display cases (e.g., (12, 9), (8, 10), and (15, 7)). Subsequently, the coordinate sequence of the navigation base path is generated in the order of "starting point → nearest target display case → next nearest target display case → end point (the exhibition hall robot charging station, coordinates (5, 2))." For example, starting from (10, 8), passing through (12, 9) → (8, 10) → (15, 7) → (5, 2), the path between each coordinate point is a straight line, and the final output coordinate sequence is "(10, 8) → (12, 9) → (8, 10) → (15, 7) → (5, 2)", which serves as the basic route for the robot's initial navigation.

[0055] Compensation coefficients are generated based on real-time grid-based crowd thermal data, dynamically matching lighting intensity to traffic density in different areas (brighter in densely populated areas, dimmed in sparser areas). This ensures a pleasant viewing experience while reducing inefficient energy consumption, achieving "on-demand lighting." Intensity level limits prevent lighting parameters from exceeding equipment capacity or impacting exhibit protection (e.g., excessive brightness damaging photosensitive exhibits), while rapid response (adjustment completed within 10 seconds) ensures environmental adaptability. Navigation base paths are generated only when electronic locks are unlocked, allowing robots to focus inspections on "displays requiring attention" (i.e., open states that may pose security risks), reducing ineffective inspections and improving security efficiency. Coordinate sequences are generated in an optimal order, shortening navigation distances and saving time and costs. Both the lighting compensation coefficient and robot navigation are based on grid cell data, strengthening the linkage between traffic distribution, lighting system, and security robots, and evolving intelligent exhibition hall control from single-device adjustments to a coordinated response across multiple systems.

[0056] In a preferred embodiment of the present invention, step 5, inputting the navigation base path and the display case boundary coordinates into a line segment intersection detection algorithm to output a collision avoidance instruction; feeding the collision avoidance instruction and the lighting compensation coefficient back to the air conditioning equipment to trigger adaptive adjustment of the ventilation intensity based on the heat load increment and the grid traffic density, may include: Step 500 , performing line segment spatial relationship detection on the navigation base path coordinate sequence and the showcase boundary coordinates, and identifying the coordinates of the intersection points of the navigation base path line segments and the showcase boundary line segments; Step 501: Reconstruct the path segment by segment based on the intersection point, and generate a collision avoidance instruction including an avoidance offset distance; Step 502: Transmit the path offset parameters and lighting compensation coefficients in the collision avoidance instruction to the air conditioning equipment control unit, and analyze the crowd density distribution data of each grid cell in the spatial collaborative grid in real time; Step 503: Calculate the heat load increment value grid by grid based on the crowd density data and the preset heat load comparison table, and aggregate the heat load increment values ​​of all grids to generate the total air supply intensity adjustment value; Step 504: outputting an air supply equipment control instruction based on the total air supply intensity adjustment amount to achieve continuous adaptive adjustment of the air supply fan speed.

[0057] In an embodiment of the present invention, the navigation base path consists of a coordinate sequence generated in step 401, such as "(10, 8) → (12, 9) → (8, 10)", where every two adjacent coordinates form a straight line segment (e.g., line segment 1: (10, 8) to (12, 9); line segment 2: (12, 9) to (8, 10)). The display case boundary coordinates are generated based on the display case positioning points collected in step 301. Each display case is a rectangular structure. Starting from the positioning point (the lower left corner of the front of the display case), four boundary line segments are generated based on the actual dimensions of the display case (e.g., 1.2 meters in length and 0.6 meters in width): left side (positioning point to positioning point + height), right side (positioning point + length to positioning point + length + height), top side (positioning point + length to positioning point), and bottom side (positioning point + height to positioning point + length + height). For example, the boundary line segments of a display case are (5, 6) → (5, 6.6) (left side), (6.2, 6) → (6.2, 6.6) (right side), etc. The system performs spatial relationship detection on each navigation base path segment and the four boundary line segments of all display cases one by one: For line segment 1 (10, 8) → (12, 9) and the right edge of a display case (6.2, 6) → (6.2, 6.6), first determine whether the extension directions of the two line segments may intersect (for example, if the path line segment extends to the upper right and the display case boundary line segment extends vertically upward, then the direction of intersection is excluded); For line segments that may intersect in different directions (such as the path segment (12, 9) → (8, 10) and the top edge of a display cabinet (7, 9.5) → (9, 9.5)), the presence of an intersection is determined by comparing the relative positions of the segment endpoints (for example, the starting point of the path segment (12, 9) is on the right side of the top edge of the display cabinet, and the end point (8, 10) is on the left side of the top edge of the display cabinet); finally, the coordinates of the intersection point are identified. For example, the intersection point of the above-mentioned intersecting line segments is (8.5, 9.5), and this point is recorded as the collision risk point.

[0058] In step 501, for the intersection point identified in step 500 (e.g., (8.5, 9.5)), the system reconstructs the original navigation base path in segments: The original path segment is (12, 9) → (8, 10), and the intersection point (8.5, 9.5) divides it into two segments: (12, 9) → (8.5, 9.5) and (8.5, 9.5) → (8, 10); Using the intersection point as a reference, set an avoidance offset distance (preset to 0.5 meters based on the showcase size to ensure a safe gap between the robot and the showcase) in the direction away from the showcase (for example, to the outside of the showcase, parallel to the showcase boundary). The reconstructed path detours at the intersection point: for example, from (8.5, 9.5), it is offset 0.5 meters to the outside of the display cabinet to generate a new intermediate point (8.5, 10), which is then connected to the original end point (8, 10). The new path segment becomes (12, 9) → (8.5, 9.5) → (8.5, 10) → (8, 10); The generated collision avoidance instructions include: the coordinates of the original path segment points (8.5, 9.5), the offset direction (outside the showcase), the offset distance (0.5 meters), the reconstructed path coordinate sequence (including the newly added intermediate points), and the execution priority (higher than the original navigation base path).

[0059] In step 502, the collision avoidance instruction generated in step 501 (including the offset distance of 0.5 meters, the reconstructed path coordinates, etc.) and the lighting compensation coefficient determined in step 400 (e.g., 1.2) are packaged and transmitted to the air conditioning equipment control unit via the industrial Ethernet within the exhibition hall: The transmission data format is "command type + parameter value + timestamp", for example, "collision avoidance-offset distance: 0.5 meters; lighting compensation coefficient: 1.2; time: 10:05:30". After receiving the data, the air-conditioning control unit first analyzes the crowd density distribution data of the spatial collaborative grid: the real-time crowd count of each grid unit is retrieved from the grid management system (for example, there are 5 people in grid (2, 3) and 2 people in grid (5, 4)), and the grid area is associated (for example, the area of ​​a 0.5 meter × 0.5 meter grid is 0.25 square meters). The "crowd density" (number of people / area) of each grid is calculated. For example, the density of grid (2, 3) is 5 people / 0.25 square meters = 20 people / square meter.

[0060] In step 503, the air conditioning control unit calls a preset "people density-heat load increment comparison table" which sets the corresponding heat load increment (unit: watts / square meter) according to the people density range: Density ≤ 5 people / m2: increment 50 watts / m2 (low load, less heat dissipation from human body); 6-15 people / m2: increment of 100 watts / m2 (medium load); Density ≥ 16 people / m2: Increment 150 watts / m2 (high load, concentrated heat dissipation from human bodies).

[0061] Calculate the heat load increment on a grid-by-grid basis: The density of grid (2, 3) is 20 people / m2 (≥16), corresponding to an increment of 150 watts / m2. The grid area is 0.25 m2, and the grid increment is 150×0.25=37.5 watts. The density of the grid (5, 4) is 2 people / m2 (≤5), corresponding to an increment of 50 watts / m2. For an area of ​​1 m2 (channel grid), the increment is 50×1=50 watts.

[0062] Aggregate the heat load increments for all grids to obtain the total heat load increment (for example, the total heat load increment for all grids is 1200 watts). Then, based on the "total heat load increment - air supply intensity adjustment amount mapping" (assuming a 100-watt increment corresponds to a 10% increase in air supply intensity), calculate the total air supply intensity adjustment amount: 1200 watts corresponds to a 120% adjustment amount (i.e., a 20% increase over the base air supply intensity).

[0063] In step 504, the air conditioning control unit converts the total air supply intensity adjustment amount (e.g., 120%) calculated in step 503 into a blower speed control instruction: The basic speed of the blower is 1500 rpm (corresponding to the basic air supply intensity), and the adjustment amount is 120%, that is, the target speed is: basic speed × adjustment amount. The instruction includes the speed change rate (such as increasing by 300 rpm to avoid airflow impact caused by sudden speed changes) and duration (until the grid traffic density decreases, triggering a decrease in the adjustment amount). The control unit sends instructions to the blower and monitors the speed feedback in real time (such as the sensor detects that the current speed is 1700 rpm). If the target is not reached, it continues to fine-tune until it stabilizes at 1800 rpm. When the traffic density of a certain grid decreases (such as the number of people in grid (2, 3) decreases to 2 people), steps 502-504 are repeated to dynamically reduce the total adjustment amount (such as to 90%), and the blower speed is then reduced to 1350 rpm, realizing continuous adaptive adjustment.

[0064] Line segment intersection detection identifies collision risk points between the path and display cases, and offset distances are used to reconstruct the path. This ensures the robot maintains a safe distance from the display cases during navigation, preventing equipment collisions or damage to exhibits, and improving security and operational safety. Heat load increments are calculated cell by cell based on grid foot traffic density, allowing air supply intensity adjustments to directly match the actual demand for stronger air supply in crowded areas. This avoids localized overcooling / overheating caused by crude global adjustments and improves environmental comfort. Collision avoidance commands (reflecting localized heat dissipation caused by robot activity) and lighting compensation coefficients (reflecting heat dissipation from lighting equipment) are fed back to the air conditioning system, allowing heat load calculations to account for both foot traffic and equipment heat sources, resulting in higher regulation accuracy. Continuous speed adjustment (rather than step-by-step on / off) avoids sudden changes in air supply intensity, leading to airflow noise or temperature fluctuations. The system also responds to foot traffic in real time, ensuring a dynamic balance in the exhibition hall environment, enhancing visitor experience and energy efficiency.

[0065] like Figure 2 As shown, the embodiment of the present invention also provides a digital exhibition hall intelligent control data linkage control system, including: The instruction generation module is used to collect real-time crowd density data at the exhibition hall entrance gate and input the crowd density data into the edge computing node. The edge computing node integrates the temperature and humidity collection terminal data with the air conditioning equipment protocol, dynamically calculates the heat load compensation value based on the crowd density, and generates environmental adjustment instructions. A parameter generation module is used to input crowd density data into the dynamic analysis unit, form a projection screen reference point based on the optical characteristics of the holographic projection screen, and generate basic lighting parameters of the projection area based on the light intensity of the projection screen reference point; The grid generation module is used to generate a spatial collaborative grid using the core showcase electronic lock position as the showcase positioning point, combining the entrance gate coordinates with the curtain reference point coordinates, and applying the spatial area division method; The path generation module is used to generate lighting compensation coefficients based on the thermal value of the grid unit flow, dynamically scale the lighting parameter intensity, and generate the robot navigation base path based on the status of the display cabinet electronic lock; The adaptive adjustment module is used to input the navigation base path and the display cabinet boundary coordinates into the line segment intersection detection algorithm to output collision avoidance instructions; the collision avoidance instructions and lighting compensation coefficient are fed back to the air conditioning equipment, triggering adaptive adjustment of the ventilation intensity based on the heat load increment and grid traffic density.

[0066] It should be noted that this system is a system corresponding to the above method, and all implementation methods in the above method embodiment are applicable to this embodiment and can achieve the same technical effects.

[0067] An embodiment of the present invention further provides a computing device comprising: a processor and a memory storing a computer program, wherein the computer program, when executed by the processor, performs the above-described method. All implementations in the above-described method embodiments are applicable to this embodiment and can achieve the same technical effects.

[0068] The embodiment of the present invention further provides a computer-readable storage medium storing instructions, which, when executed on a computer, causes the computer to execute the above-described method. All implementations in the above-described method embodiment are applicable to this embodiment and can achieve the same technical effects.

[0069] The above is a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles of the present invention. These improvements and modifications should also be regarded as within the scope of protection of the present invention.

Claims

1. The method for intelligent data linkage control of digital exhibition hall is characterized by: The method comprises: Step 1: Collect crowd density data in real time at the exhibition hall entrance gate and input the crowd density data into the edge computing node; the edge computing node integrates the temperature and humidity collection terminal data with the air conditioning equipment protocol, dynamically calculates the heat load compensation value based on the crowd density, and generates environmental adjustment instructions; Step 2: Input the crowd density data into the dynamic analysis unit, form a projection screen reference point based on the optical characteristics of the holographic projection screen, and generate the basic lighting parameters of the projection area in combination with the light intensity of the projection screen reference point; Step 3: Using the core showcase electronic lock position as the showcase positioning point, combining the entrance gate coordinates with the curtain reference point coordinates, and applying the spatial area division method to generate a spatial collaborative grid; Step 4: Generate a lighting compensation coefficient based on the thermal value of the grid unit flow, dynamically scale the lighting parameter intensity, and generate a robot navigation base path based on the status of the showcase electronic lock; In step 5, the navigation base path and the display cabinet boundary coordinates are input into the line segment intersection detection algorithm, and a collision avoidance instruction is output. The collision avoidance instruction and the lighting compensation coefficient are fed back to the air conditioning equipment, triggering adaptive adjustment of the ventilation intensity based on the heat load increment and the grid traffic density.

2. The method for intelligent data linkage control of a digital exhibition hall according to claim 1 is characterized in that: Collect crowd density data in real time at the exhibition hall entrance gate and input it into the edge computing node; The edge computing node integrates the temperature and humidity collection terminal data with the air conditioning equipment protocol, dynamically calculates the heat load compensation value based on the crowd density, and generates environmental adjustment instructions, including: Collect crowd density data in real time through the infrared counting sensor of the entrance gate; The edge computing node receives crowd density data and temperature and humidity data uploaded by the temperature and humidity acquisition terminal, associates it with the temperature and humidity control parameters defined in the air-conditioning equipment protocol, and matches the corresponding heat load compensation coefficient according to the crowd density value range; The temperature and humidity compensation values ​​of the target space are calculated based on the heat load compensation coefficient, and an environmental adjustment instruction including the compensated target temperature value and target humidity value is generated.

3. The method for intelligent data linkage control of a digital exhibition hall according to claim 2 is characterized in that: The crowd density data is input into the dynamic analysis unit. Based on the optical characteristics of the holographic projection screen, a projection screen reference point is formed. Combined with the light intensity of the projection screen reference point, the basic lighting parameters of the projection area are generated, including: The optical center coordinates of the holographic projection screen are used as the reference point of the projection screen; The ambient light intensity value at the reference point of the projection screen is collected in real time through the light sensor; The dynamic analysis unit receives crowd density data and ambient light intensity values, matches the basic brightness parameters required for the projection area according to the preset crowd density-light intensity grading mapping relationship, and outputs lighting control instructions containing the basic brightness parameters.

4. The method for intelligent data linkage control of a digital exhibition hall according to claim 3 is characterized in that: The dynamic analysis unit receives crowd density data and ambient light intensity values, matches the basic brightness parameters required for the projection area based on the preset crowd density-light intensity classification mapping relationship, and outputs lighting control instructions containing the basic brightness parameters, including: Compare the crowd density data with the preset crowd density threshold to generate the crowd density influencing factor; calculate the difference between the ambient light intensity value and the preset benchmark illumination value to generate the light compensation factor; According to the weighted sum of the crowd density influencing factor and the illumination compensation factor, the preset brightness parameter mapping table is indexed to obtain the basic brightness parameter value of the projection area, and a lighting control instruction containing the basic brightness parameter value is generated.

5. The method for intelligent data linkage control of a digital exhibition hall according to claim 4 is characterized in that: Taking the core showcase electronic lock position as the showcase positioning point, combined with the entrance gate coordinates and the curtain reference point coordinates, the spatial area division method is applied to generate a spatial collaborative grid, including: Obtain the entrance gate position coordinates as the first spatial reference point, the holographic projection screen optical center coordinates as the second spatial reference point, and the core display cabinet electronic lock physical position coordinates as the third spatial reference point. Establish the exhibition hall spatial rectangular coordinate system based on these three spatial reference points. Based on the exhibition hall's rectangular coordinate system, locate and collect the physical location coordinates of all display cabinets and security equipment to generate a spatial equipment coordinate set; Input the device spatial coordinate set into the preset neighbor allocation rule, calculate the straight-line distance between each device coordinate and each reference point, assign the device to the corresponding reference point with the smallest straight-line distance, and output the reference point-device allocation relationship table; Based on the reference point-equipment allocation relationship table, the spatial separation boundary lines between adjacent reference points are automatically generated according to the density of equipment distribution; According to the spatial separation boundary line, grid units covering the entire exhibition hall are formed, and each grid unit is marked with the associated reference point number and the type of equipment contained.

6. The method for intelligent data linkage control of a digital exhibition hall according to claim 5 is characterized in that: Generate lighting compensation coefficients based on the thermal value of the grid cell flow, dynamically scale lighting parameter intensity, and generate robot navigation base paths based on the status of the showcase electronic lock, including: Collect the thermal value data of the crowd flow of each grid unit in real time, and determine the lighting compensation coefficient based on the thermal value data of the crowd flow and the preset projection brightness classification threshold; The intensity level of the basic lighting parameters of the projection area is dynamically adjusted based on the lighting compensation coefficient, and the switch status signal of the core display cabinet electronic lock is obtained in real time. When the electronic lock status signal is on, the navigation base path coordinate sequence of the robot inspection is generated with the physical position coordinates of the electronic lock as the starting point.

7. The method for intelligent data linkage control of a digital exhibition hall according to claim 6 is characterized in that: The navigation base path and the display cabinet boundary coordinates are input into the line segment intersection detection algorithm, which outputs collision avoidance instructions. The collision avoidance instructions and lighting compensation coefficients are fed back to the air conditioning equipment, triggering adaptive adjustment of the ventilation intensity based on the heat load increment and grid traffic density, including: Perform line segment spatial relationship detection on the navigation base path coordinate sequence and the showcase boundary coordinates, and identify the intersection coordinates of the navigation base path line segment and the showcase boundary line segment; The path is segmented and reconstructed based on the intersection point, generating collision avoidance instructions including avoidance offset distances; The path offset parameters and lighting compensation coefficients in the collision avoidance instructions are transmitted to the air conditioning equipment control unit, and the pedestrian density distribution data of each grid cell in the spatial collaborative grid is analyzed in real time; According to the crowd density data and the preset heat load comparison table, the heat load increment value is calculated grid by grid, and the heat load increment values ​​of all grids are aggregated to generate the total air supply intensity adjustment value; The control instructions of the air supply equipment are output based on the total air supply intensity adjustment amount to realize continuous adaptive adjustment of the air supply fan speed.

8. A digital exhibition hall intelligent data linkage control system, which implements the method according to any one of claims 1 to 7, characterized in that: include: The command generation module is used to collect real-time crowd density data at the exhibition hall entrance gate and input the crowd density data into the edge computing node; The edge computing node integrates the temperature and humidity collection terminal data with the air conditioning equipment protocol, dynamically calculates the heat load compensation value based on the crowd density, and generates the environmental adjustment instruction; A parameter generation module is used to input crowd density data into the dynamic analysis unit, form a projection screen reference point based on the optical characteristics of the holographic projection screen, and generate basic lighting parameters of the projection area based on the light intensity of the projection screen reference point; The grid generation module is used to generate a spatial collaborative grid using the core showcase electronic lock position as the showcase positioning point, combining the entrance gate coordinates with the curtain reference point coordinates, and applying the spatial area division method; The path generation module is used to generate lighting compensation coefficients based on the thermal value of the grid unit flow, dynamically scale the lighting parameter intensity, and generate the robot navigation base path based on the status of the display cabinet electronic lock; The adaptive adjustment module is used to input the navigation base path and the display cabinet boundary coordinates into the line segment intersection detection algorithm to output collision avoidance instructions; the collision avoidance instructions and lighting compensation coefficient are fed back to the air conditioning equipment, triggering adaptive adjustment of the ventilation intensity based on the heat load increment and grid traffic density.

9. A computing device, characterized in that include: one or more processors; A storage device for storing one or more programs, wherein when the one or more programs are executed by the one or more processors, the one or more processors implement the method according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that The computer-readable storage medium stores a program, which, when executed by a processor, implements the method according to any one of claims 1 to 7.

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