Digital exhibition hall intelligent control data linkage control method and system

Abstract

The application provides a digital exhibition hall intelligent control data linkage control method and system, relates to the technical field of intelligent exhibition hall, and the method comprises the following steps: step 1, collecting people flow density data in real time at the entrance gate position of the exhibition hall, and inputting the people flow density data into an edge computing node; the edge computing node fuses temperature and humidity collection terminal data and air conditioning equipment protocols, dynamically calculates a heat load compensation value based on the people flow density, and generates an environment adjusting instruction; 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 projection area illumination basic parameters combined with the illumination intensity of the projection curtain reference point. Through the people flow data driving multi-system cooperation, the application realizes accurate adaptation and dynamic adjustment of the environment, illumination and navigation, and improves the intelligent level, operation efficiency and safety and energy saving property of the exhibition hall.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of intelligent exhibition hall, in particular to an index intelligent exhibition hall intelligent control data linkage control method and system. BACKGROUND

[0002] The intelligent control system of the current digital intelligent exhibition hall faces some problems and challenges in actual application:

[0003] Data island phenomenon and independent control mode are more common. The access control / gate machine, environmental sensor, air conditioner, lighting, projection equipment, electronic showcase lock, service / security robot and other subsystems in the exhibition hall are mostly independently deployed and operated, and the data between systems is difficult to realize effective interconnection.

[0004] At present, some researches have tried to simply link some data such as human flow and air conditioner, or consider static obstacles in robot path planning, but there is still room for improvement in deep integration and closed-loop linkage control, for example, there is a lack of mechanism for constructing a collaborative grid based on multiple space reference points to realize precise regulation and control. SUMMARY

[0005] The technical problem to be solved by the present application is to provide a digital intelligent exhibition hall intelligent control data linkage control method and system to comprehensively improve the intelligent level of the exhibition hall.

[0006] To solve the above technical problems, the technical scheme of the present application is as follows:

[0007] In a first aspect, a digital intelligent exhibition hall intelligent control data linkage control method, the method comprising:

[0008] Step 1: Real-time collection of human flow density data at the entrance gate position of the exhibition hall, and input of the human flow density data into an edge computing node; the edge computing node fuses temperature and humidity collection terminal data and air conditioner equipment protocol, dynamically calculates a heat load compensation value based on human flow density, and generates an environment adjustment instruction;

[0009] Step 2: Input of the human flow density data into a dynamic analysis unit, formation of a projection curtain reference point based on the optical properties of the holographic projection curtain, and generation of projection area lighting basic parameters combined with the illumination intensity of the projection curtain reference point;

[0010] Step 3: Taking 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;

[0011] Step 4: Generation of an illumination compensation coefficient according to the grid unit human flow heat value, dynamic scaling of the illumination parameter intensity, and generation of a robot navigation base path based on the showcase electronic lock state;

[0012] Step 5, the navigation base path is intersected with the showcase boundary coordinate input 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, and the air conditioning equipment is adaptively adjusted based on the heat load increment and the grid passenger flow density.

[0013] In a second aspect, a smart control data linkage control system for a digital exhibition hall includes:

[0014] An instruction generation module is configured to collect passenger flow density data in real time at an entrance gate of the exhibition hall, and input the passenger flow density data into an edge computing node; the edge computing node fuses temperature and humidity collection terminal data and air conditioning equipment protocols, dynamically calculates a heat load compensation value based on the passenger flow density, and generates an environment adjustment instruction;

[0015] A parameter generation module is configured to input the passenger flow density data into a dynamic analysis unit, form a projection curtain reference point based on the optical properties of the holographic projection curtain, and generate lighting basic parameters for a projection area in combination with the light intensity of the projection curtain reference point;

[0016] A grid generation module is configured to take a core showcase electronic lock position as a showcase positioning point, combine entrance gate coordinates and curtain reference point coordinates, and generate a spatial coordination grid by applying a spatial region division method;

[0017] A path generation module is configured to generate a lighting compensation coefficient based on the passenger flow heat value of a grid unit, dynamically scale lighting parameter intensity, and generate a robot navigation base path based on the state of the showcase electronic lock;

[0018] An adaptive adjustment module is configured to output a collision avoidance instruction by intersecting the navigation base path with the showcase boundary coordinate input line segment intersection detection algorithm; the collision avoidance instruction and the lighting compensation coefficient are fed back to the air conditioning equipment, and the air conditioning equipment is adaptively adjusted based on the heat load increment and the grid passenger flow density.

[0019] In a third aspect, a computing device includes:

[0020] One or more processors;

[0021] A storage device is configured to store one or more programs, when the one or more programs are executed by the one or more processors, so that the one or more processors implement the method.

[0022] In a fourth aspect, a computer readable storage medium stores a program, which is executed by a processor to implement the method.

[0023] The above-mentioned scheme of the present application at least has the following beneficial effects:

[0024] The heat load compensation value is dynamically calculated through the human flow density, the air conditioning equipment is linked to realize the self-adaptive adjustment of temperature and humidity, the comfortable environment can be maintained through the heat load compensation when the human flow is dense, the invalid energy consumption can be avoided, the balance of energy saving and comfort is realized, the lighting basic parameters are generated according to the human flow density in combination with the optical characteristics and the illumination intensity of the holographic projection curtain, and the definition and the viewing experience of the holographic projection are improved. The exhibition hall is regionalized managed according to the equipment distribution and the spatial coordinates through the spatial cooperative grid division, accurate spatial benchmarks are provided for the control of lighting, navigation and the like, the adjustment is more targeted.

[0025] The lighting intensity is dynamically adjusted based on the grid human flow heat value, the robot navigation base path is generated combined with the state of the showcase and collision avoidance is conducted, the safety and efficiency of the robot inspection are ensured, the lighting and the robot activity are coordinated, resource waste is avoided, the data of navigation and lighting are fed back to the air conditioning system, the heat load and the air supply intensity are deeply linked, and the overall intelligent level and operation efficiency of the exhibition hall are improved. BRIEF DESCRIPTION OF DRAWINGS

[0026] Figure 1 is a flowchart of the intelligent exhibition hall intelligent control data linkage control method provided by the embodiment of the present application.

[0027] Figure 2 is a schematic diagram of the intelligent exhibition hall intelligent control data linkage control system provided by the embodiment of the present application. DETAILED DESCRIPTION

[0028] Exemplary embodiments of the present disclosure will be described in greater detail below with reference to the accompanying drawings. Although exemplary embodiments of the present disclosure are shown in the 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 so that the present disclosure can be more thoroughly understood and the scope of the present disclosure can be accurately conveyed to those skilled in the art.

[0029] As Figure 1 shown, the embodiment of the present application proposes an intelligent exhibition hall intelligent control data linkage control method, which comprises the following steps:

[0030] Step 1, real-time collection of human flow density data at the entrance gate position of the exhibition hall, and input of the human flow density data into an edge computing node; the edge computing node fuses temperature and humidity collection terminal data and air conditioning equipment protocols, dynamically calculates a heat load compensation value based on the human flow density, and generates an environment adjustment instruction;

[0031] Step 2, input of the human flow density data into a dynamic analysis unit, formation of a projection curtain reference point based on the optical characteristics of the holographic projection curtain, and generation of projection area lighting basic parameters combined with the illumination intensity of the projection curtain reference point;

[0032] Step 3, taking the core showcase electronic lock position as the showcase positioning point, combining the entrance gate machine coordinates and the curtain reference point coordinates, and applying a space region division method to generate a space coordination grid;

[0033] Step 4, generating a lighting compensation coefficient according to the grid unit people flow heat value, dynamically scaling the lighting parameter intensity, and generating a robot navigation base path based on the showcase electronic lock state;

[0034] Step 5, inputting the navigation base path and the showcase boundary coordinates into a line segment intersection detection algorithm to output collision avoidance instructions; feeding the collision avoidance instructions and the lighting compensation coefficient to the air conditioning equipment to send the wind intensity adaptive adjustment based on the heat load increment and the grid people flow density.

[0035] In the embodiment of the application, the heat load compensation value is dynamically calculated by the people flow density, and the air conditioning equipment is linked to realize adaptive adjustment of temperature and humidity, which can maintain a comfortable environment by heat load compensation when the people flow is dense, and can avoid invalid energy consumption, realizing the balance between energy saving and comfort; combined with the optical characteristics of the holographic projection curtain and the illumination intensity, the lighting basic parameters are generated according to the people flow density to improve the clarity and viewing experience of the holographic projection. Through space coordination grid division, the exhibition hall is regionalized managed according to the equipment distribution and space coordinates, providing accurate space reference for lighting, navigation and other control, so that each adjustment is more targeted.

[0036] Based on the grid people flow heat value, the lighting intensity is dynamically adjusted, the robot navigation base path is generated combined with the showcase state and the collision avoidance is performed, ensuring the safety and efficiency of the robot inspection, and at the same time, the lighting and the robot activity form a synergy to avoid resource waste. The navigation, lighting and other data are fed back to the air conditioning system to realize the deep linkage of heat load and air supply intensity, and improve the overall intelligent level and operation efficiency of the exhibition hall.

[0037] In a preferred embodiment of the application, the above step 1, the people flow density data is collected in real time at the entrance gate machine position of the exhibition hall, and the people flow density data is input into the edge computing node; the edge computing node fuses the temperature and humidity collection terminal data and the air conditioning equipment protocol, dynamically calculates the heat load compensation value based on the people flow density, and generates environment adjustment instructions, which can include:

[0038] Step 100, collecting people flow density data in real time through the infrared counting sensor of the entrance gate machine;

[0039] Step 101, the edge computing node receives the people flow density data and the temperature and humidity data uploaded by the temperature and humidity collection 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 people flow density value interval;

[0040] Step 102, calculate the temperature and humidity compensation value of the target space based on the heat load compensation coefficient, and generate an environment adjustment instruction containing the compensated target temperature value and target humidity value.

[0041] In the embodiment of the present application, the infrared counting sensor of the entrance gate is symmetrically installed with infrared transmitting ends and receiving ends on both sides of the gate passage, forming multiple groups of intersecting infrared sensing lines. When the audience passes through the gate, the body will block the infrared sensing lines, and the sensor will determine the flow direction by the number and order of the blocked sensing lines. If the sensing lines close to the exhibition hall outside are blocked first and then the sensing lines close to the exhibition hall inside are blocked, it is determined as "entering"; otherwise, it is determined as "leaving". The sensor collects the blocking signals every 0.5 seconds, and records the "entering" times as positive values and the "leaving" times as negative values, and accumulatively calculates the net flow in a unit time (such as 1 minute). At the same time, in order to avoid repeated counting caused by the same audience repeatedly entering and leaving, the sensor will filter the repeated blocking signals in a short time through the blocking duration (usually set an interval threshold of 0.5-2 seconds) - if the same position sensing line is blocked again within the threshold time, it is determined that the same person has not completely passed through, and the counting is not repeated. Finally, the sensor adds the net flow in a unit time to the current cumulative number of people in the exhibition hall, updates and outputs the "current exhibition hall flow density data" in real time, and the data form is "number / total area of exhibition hall" (such as "30 people / 1000 square meters"), and is sent to the edge computing node through wired transmission (such as Ethernet).

[0042] Step 101, after the edge computing node is started, it will continuously receive the real-time flow density data sent by the infrared counting sensor, and at the same time receive the real-time data uploaded by the temperature and humidity collection terminal through the wireless communication module (such as LoRa) - the temperature data is accurate to 0.1℃ (such as "25.3℃"), and the humidity data is accurate to 1% (such as "55%"). Then, the edge computing node calls the pre-stored air conditioning equipment protocol, extracts the basic control parameters defined in the protocol, including the exhibition hall reference temperature range (such as "24-26℃"), the reference humidity range (such as "50%-60%"), the air conditioning running power standard under different temperature and humidity, and the parameter adjustment accuracy specified in the protocol (such as temperature adjustment step is 0.5℃, humidity is 5%). Subsequently, the node divides the flow density data into intervals: usually three intervals are preset, such as "low density (≤5 people / 100 square meters)", "medium density (6-15 people / 100 square meters)", and "high density (≥16 people / 100 square meters)". The division is based on the heat dissipation of the human body per unit time (about 100-150 watts / hour for an adult in a resting state), combined with the ventilation efficiency of the exhibition hall - the more people, the greater the total heat dissipation of the human body, the stronger the heat load compensation required.

[0043] Based on the division result, the node matches the corresponding coefficient from the pre-stored "people flow density-thermal load compensation coefficient table": for example, the low-density interval corresponds to the compensation coefficient "1.0" (no additional compensation is required), the medium-density corresponds to "1.2" (20% of the thermal load needs to be compensated), and the high-density corresponds to "1.5" (50% of the thermal load needs to be compensated). 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 actual environmental impact of human body heat dissipation.

[0044] In step 102, the edge computing node first takes the reference temperature and humidity in the air conditioning equipment protocol as the basis value, and calculates the compensation value based on the thermal load compensation coefficient matched in step 101.

[0045] Temperature compensation: If the current real-time temperature is within the reference range (e.g. 25°C), and it is in the medium-density interval (compensation coefficient 1.2), then calculate "reference temperature - (compensation coefficient - 1.0) x reference adjustment step size x 2" to offset the temperature rise caused by human body heat dissipation by lowering the target temperature.

[0046] Humidity compensation: Human respiration increases environmental humidity, and the compensation logic is opposite to that of temperature. If the current humidity is 55% (within the reference range), and it is in the medium-density interval, then calculate "reference humidity + (compensation coefficient - 1.0) x reference humidity step size" - the reference step size is 5%. The compensated target humidity is 55% + (0.2 x 5%) = 56% in the medium-density interval, and 55% + (0.5 x 5%) = 57.5% in the high-density interval. By slightly increasing the target humidity, it avoids excessive dehumidification of the air conditioner leading to dry environment. Finally, the node integrates the compensated target temperature (e.g. 24.5°C) and target humidity (e.g. 57.5%) into standardized environmental adjustment instructions, which include device address (specifying the air conditioning unit to be adjusted), parameter type (temperature / humidity), target value, and adjustment time limit (e.g. "reach target value within 10 minutes"). The instructions are sent to the air conditioning control system through the communication interface (e.g. RS485 bus) specified in the air conditioning equipment protocol, triggering the device to adjust its operating state.

[0047] Through the direction recognition and repeated counting filtering of the infrared counting sensor, it ensures that the people flow data is real-time and accurate, avoiding environmental adjustment errors caused by data errors; by matching the compensation coefficient with the people flow density interval, it links the thermal load calculation with the actual human body heat dissipation demand, avoiding environmental overcooling / overheating, over-drying / over-humidification caused by "fixed parameter adjustment", and improving the audience's comfort level. By associating with the air conditioning equipment protocol parameters, it ensures that the adjustment instructions meet the device operation specifications, avoiding device failures caused by parameter conflicts, and by specifying the adjustment step size and time limit, it ensures the stability of the air conditioner operation; by adjusting the target temperature and humidity based on the actual people flow, it avoids unnecessary energy consumption when there is no one or few people (e.g. increasing adjustment in high-density and maintaining the reference in low-density), achieving the balance between energy saving and comfort.

[0048] In a preferred embodiment of the present application, the step 2 of inputting the human flow density data into the dynamic analysis unit, forming the projection screen reference point based on the optical characteristics of the holographic projection screen, and generating the illumination basis parameter of the projection area in combination with the illumination intensity of the projection screen reference point can include:

[0049] Step 200, taking the optical center point coordinate of the holographic projection screen as the projection screen reference point;

[0050] Step 201, collecting the environmental illumination intensity value at the projection screen reference point in real time through the illumination sensor;

[0051] Step 202, the dynamic analysis unit receives the human flow density data and the environmental illumination intensity value, matches the required basic brightness parameter of the projection area according to the preset human flow density-illumination intensity classification mapping relationship, and outputs the illumination control instruction containing the basic brightness parameter, specifically including:

[0052] Comparing the human flow density data with the preset human flow density threshold value to generate a human flow density influence factor; and performing difference calculation on the environmental illumination intensity value and the preset reference illuminance value to generate an illumination compensation factor;

[0053] According to the weighted sum of the human flow density influence factor and the illumination compensation factor, indexing the preset brightness parameter mapping table to obtain the basic brightness parameter value of the projection area, and generating the illumination control instruction containing the basic brightness parameter value.

[0054] In the embodiment of the present application, the physical installation boundary of the holographic projection screen is determined: the horizontal distance between the left edge and the right edge of the screen (i.e. the width of the screen) and the vertical distance between the upper edge and the lower edge (i.e. the height of the screen) are measured. 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 "lower edge + 2 meters". Based on the preset spatial coordinate system of the exhibition hall (taking the lower left corner of the exhibition hall as the origin, the horizontal right as the X axis, and the vertical up as the Y axis), the position of the physical center of the screen is converted into coordinate values; assuming that the left edge of the screen is 5 meters away from the origin X axis coordinate, and the lower edge is 1 meter away from the origin Y axis coordinate, then the X coordinate of the optical center point of the screen is 5 meters + 3 meters = 8 meters, and the Y coordinate is 1 meter + 2 meters = 3 meters, and finally the reference point coordinate is determined as (8, 3), which is marked as the core monitoring point of the projection area.

[0055] Step 201, at the projection curtain reference point (coordinates 8, 3) determined in step 200, install a high-precision light sensor (precision up to 1 lux), with the sensing surface facing the curtain surface (30 cm away from the curtain to avoid blocking the projection light). The sensor collects environmental light intensity data every 2 seconds, covering natural light (such as sunlight through the window), exhibition hall basic lighting (such as overhead spotlights), and other devices (such as audience cell phone flash). The collected data is automatically converted into standardized values (in lux), for example: under cloudy natural light, it may be 300 lux, under direct sunlight at noon on a sunny day, it may reach 1500 lux, and when the exhibition hall basic lighting is turned on alone, it may be 500 lux. The data is sent in real time to the dynamic analysis unit through a wireless transmission module (such as WiFi), with a timestamp (accurate to the second) and sensor number (to avoid data confusion from multiple devices) attached to ensure that the dynamic analysis unit can obtain real-time light changes at the reference point.

[0056] Step 202, the generation process of the crowd density influence factor:

[0057] 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 is compared with the thresholds:

[0058] If the crowd density is 8 people / 100 square meters (below the low threshold), it is determined to be "sparse crowd", and the crowd density influence factor 0.9 is generated (indicating that the projection brightness needs to be appropriately reduced to avoid strong and dazzling light);

[0059] If the crowd density is 20 people / 100 square meters (at the medium threshold), it is determined to be "moderate crowd", and the influence factor 1.0 is generated (maintaining the baseline brightness);

[0060] If the crowd density is 35 people / 100 square meters (above the high threshold), it is determined to be "dense crowd", and the influence factor 1.2 is generated (the brightness needs to be enhanced to offset the light attenuation caused by the crowd blocking).

[0061] Generate light compensation factor:

[0062] The dynamic analysis unit pre-sets the baseline illuminance value of the projection area (according to the best display effect of holographic projection, usually 500 lux). The real-time light intensity value collected in step 201 is calculated by difference with the baseline illuminance value:

[0063] If the real-time light intensity is 600 lux (100 lux higher than the reference), it indicates that the ambient light is strong, and the compensation factor is calculated as "1 - (difference / reference) = 1 - (100 / 500) = 0.8" (the projection brightness needs to be reduced to balance the strong light);

[0064] If the real-time light intensity is 400 lux (100 lux lower than the reference), it indicates that the ambient light is weak, and the compensation factor is calculated as "1 + (absolute value of difference / reference) = 1 + (100 / 500) = 1.2" (the projection brightness needs to be enhanced to ensure clarity);

[0065] If the real-time light intensity is equal to 500 lux, the compensation factor is 1.0 (no compensation is needed).

[0066] Calculate the basic brightness parameter and generate instructions:

[0067] The dynamic analysis unit performs weighted calculation on the crowd density influence factor and the light compensation factor (preset crowd factor weight 60%, light factor weight 40%). For example: when the crowd influence factor is 1.2 (crowded) and the light compensation factor is 1.2 (weak ambient light), the weighted sum is 1.2 x 60% + 1.2 x 40% = 1.2; refer to the "weighted sum-brightness parameter mapping table" (in the table, 0.8 corresponds to 2000 lumens, 1.0 corresponds to 3000 lumens, and 1.2 corresponds to 4000 lumens), and the basic brightness parameter value is 4000 lumens. Finally, the dynamic analysis unit generates lighting control instructions containing the following information: target area (projection screen domain), basic brightness parameter (4000 lumens), adjustment time (complete brightness switching within 10 seconds), execution priority (higher than the fixed mode of basic lighting), and sends it to the lighting control system of the projection area through the wireless control bus, triggering brightness adjustment.

[0068] Taking the optical center point of the screen as the reference point, it ensures strong correlation between light collection and projection core area, avoids brightness parameter distortion caused by deviation of collection points, and collects light intensity at a high frequency (2 seconds / time), which can quickly respond to changes in ambient light (such as cloud cover blocking sunlight, exhibition hall light switching), ensuring the timeliness of brightness adjustment. Through the weighted calculation of the crowd density influence factor (matching the demand of audience number for light) and the light compensation factor (balancing the disturbance of ambient light), the projection brightness can adapt to the number of people (more brightness in crowded areas to improve visibility) and offset the fluctuation of ambient light (reduce in strong light and enhance in weak light). The instructions contain clear target area, parameter value and time limit, ensuring the precision of lighting system execution, avoiding adjustment delay or parameter deviation, and improving the efficiency of system linkage.

[0069] In a preferred embodiment of the present application, the above-mentioned step 3, taking the core showcase electronic lock position as the showcase positioning point, combining the entrance gate coordinate and the curtain reference point coordinate, and applying the space region division method to generate a space collaborative grid, can include:

[0070] Step 300, obtaining the entrance gate position coordinate as the first space reference point, the holographic projection curtain optical center coordinate as the second space reference point, and the core showcase electronic lock physical position coordinate as the third space reference point, and establishing a showroom space rectangular coordinate system based on the three space reference points;

[0071] Step 301, based on the showroom space rectangular coordinate system, positioning and collecting the physical position coordinates of all showcases and security devices to generate a space device coordinate set;

[0072] Step 302, inputting the device space coordinate set into a preset near neighbor allocation rule, calculating the straight line distance between each device coordinate and each reference point, allocating the device to the corresponding reference point with the minimum straight line distance, and outputting a reference point-device allocation relationship table;

[0073] Step 303, based on the reference point-device allocation relationship table, automatically generating a space separation boundary line between adjacent reference points according to the device distribution density;

[0074] Step 304, dividing to form a grid unit covering the entire showroom according to the space separation boundary line, and labeling the associated reference point number and the contained device type for each grid unit.

[0075] In an embodiment of the present application, the physical coordinates of the three space reference points are obtained:

[0076] The first space reference point (entrance gate): measure the center point position of the entrance gate, taking the lower left corner of the showroom floor as a temporary origin point (for easy measurement), and use a laser range finder to measure the horizontal distance of the gate center point from the temporary origin point as 8 meters (along the long axis direction of the showroom), and the vertical distance as 2 meters (along the short axis direction), and record the coordinates as (8, 2).

[0077] The second space reference point (holographic projection curtain optical center): use the curtain optical center coordinate determined in step 200, which is assumed to be (15, 5) (i.e., 15 meters horizontally and 5 meters vertically from the temporary origin point).

[0078] The third space reference point (core showcase electronic lock): find the electronic lock installation position of the core showcase (usually on the right side of the front of the showcase), measure its horizontal distance from the temporary origin point as 10 meters and its vertical distance as 8 meters, and record the coordinates as (10, 8).

[0079] Establish a space rectangular coordinate system based on these three reference points:

[0080] Take the first reference point (8, 2) as the origin of the coordinate system (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, 1 coordinate unit per meter).

[0081] The direction perpendicular to the X-axis and pointing to the interior of the exhibition hall is set as the positive direction of the Y-axis, ensuring that the coordinates of the three reference points in the 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, the coordinates are (7.6, 0)), and the third reference point (according to the relative position to the origin, finally determined as (3.2, 5.8)). After establishing the coordinate system, all subsequent device coordinates and grid division are based on this system to ensure the unity and accuracy of spatial positioning.

[0082] Step 301, based on the rectangular coordinate system established in step 300, the technician locates and collects all showcases (including non-core showcases) and security devices (such as cameras and infrared alarms) in the exhibition hall:

[0083] Showcase coordinate collection: For each showcase, select the lower left corner of its front face (near the ground) as the positioning point, and measure the X-axis and Y-axis values of this point in the coordinate system using a laser range finder. For example, a certain ordinary showcase positioning point is 3 meters away from the origin of the coordinate system on the X-axis and 2 meters on the Y-axis, recording the coordinates as (3, 2), and labeling the device type as "Showcase - Cultural Relics".

[0084] Security device coordinate collection: For cameras, take the projection position (foot of the vertical line on the ground) of the lens center point as the coordinate point; for infrared alarms, take the foot of the vertical line on the ground of the installation base as the coordinate point. For example, the projection point coordinates of a certain camera are (12, 4), and the device type is labeled as "Security - Camera".

[0085] All device coordinate data is collected into a "spatial device coordinate set" with the format "device unique ID + (X coordinate, Y coordinate) + device type", such as "ZG-002 + (5, 3) + Showcase - Technology" and "AF-005 + (9, 6) + Security - Alarm", ensuring that each device has a unique and clear location record in the coordinate system.

[0086] Step 302, input the spatial device coordinate set generated in step 301 into the system, and perform device attribution division according to the preset near neighbor allocation rules:

[0087] Distance calculation logic: For each device's coordinate (X, Y), calculate the straight-line distance to the three spatial reference points, respectively. For example, a certain showcase coordinate is (5, 3):

[0088] Distance to the first reference point (0, 0): By measuring the difference in 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 and vertical differences"), it is determined that the distance is less than that to the second and third reference points (assuming the second reference point (7.6, 0) and the third reference point (3.2, 5.8), the horizontal difference to the second reference point is 2.6, and the vertical difference is 3, so the distance is greater; the horizontal difference to the third reference point is 1.8, and the vertical difference is 2.8, so the distance is also greater).

[0089] Homologous determination: After comparing the three distances, the showcase is assigned to the first reference point with the smallest distance.

[0090] Generation of relationship table: The system automatically outputs a "reference point-equipment allocation relationship table" containing columns: equipment ID, equipment coordinates, reference point number (1 / 2 / 3), and distance value (minimum distance). For example, "ZG-002 + (5, 3) + 1 + 6.5 meters", clearly indicating the ownership of each device.

[0091] Step 303: Based on the relationship table in step 302, the system analyzes the equipment distribution density and automatically generates spatial separation boundary lines between adjacent reference points:

[0092] Density analysis: Count the coordinate distribution range of the equipment belonging to each reference point. For example, the equipment of the first reference point (No. 1) is mainly concentrated in the X axis 0-6 meters and the Y axis 0-4 meters; the equipment of the second reference point (No. 2) is concentrated in the X axis 6-12 meters and the Y axis 0-3 meters; the equipment of the third reference point (No. 3) is concentrated in the X axis 2-8 meters and the Y axis 4-8 meters.

[0093] Boundary line demarcation: For adjacent reference points (such as No. 1 and No. 2), take the middle value of the equipment distribution range of the two as the boundary. The maximum X axis of No. 1 equipment is 6 meters, and the minimum X axis of No. 2 equipment is 6 meters, so a vertical boundary line is drawn at X=6 meters; for No. 1 and No. 3, the maximum Y axis of No. 1 equipment is 4 meters, and the minimum Y axis of No. 3 equipment is 4 meters, so a horizontal boundary line is drawn at Y=4 meters.

[0094] Boundary line verification: Ensure that all equipment is inside the boundary line of the reference point to which it belongs (such as No. 1 equipment is all in the area where X<6 meters and Y<4 meters), if there are equipment across the boundary, adjust the boundary line position (such as ±0.5 meters), finally form a closed boundary line network covering the entire exhibition hall.

[0095] Step 304: According to the spatial separation boundary line generated in step 303, the system automatically divides the grid cells covering the entire exhibition hall:

[0096] Grid size setting: dynamically adjust the grid size according to the device distribution density - use small grid of 0.5m x 0.5m in device dense area (such as showcase concentrated area), and use large grid of 1m x 1m in device sparse area (such as corridor). For example, the showcase dense area of the first reference point is divided into grids with an interval of 0.5m in X axis and 0.5m in Y axis.

[0097] Grid attribute labeling: label three key information for each grid unit: ① associated reference point number (such as "No. 1"); ② device type contained (such as "showcase - 3, camera - 1"); ③ grid center point coordinates (for subsequent positioning). For example, the grid center point coordinates (2.5, 1.5) are labeled as "reference point No. 1 + 2 showcases + no security device".

[0098] Global coverage verification: ensure that all grids are seamlessly connected without overlapping or missing areas (including corners, corridor corners, etc.), and finally form a complete "space coordination grid".

[0099] A unified coordinate system is established through three core reference points, eliminating the differences in coordinate systems of different devices positioning; the spatial device coordinate set comprehensively covers the showcases and security devices, and the near neighbor allocation rule clearly determines the device ownership, so that the "responsibility reference point" of each device is clear and traceable, laying the foundation for targeted control (such as adjusting the lighting of a certain reference point area). The boundary line and grid generated based on the device distribution density ensure the clear separation of adjacent areas.

[0100] In a preferred embodiment of the present application, the above step 4, generating a lighting compensation coefficient according to the grid unit people flow heat value, dynamically scaling the lighting parameter intensity, and generating a robot navigation base path based on the showcase electronic lock state, can include:

[0101] Step 400, real-time acquisition of people flow heat value data of each grid unit, determination of lighting compensation coefficient according to people flow heat value data and preset projection brightness grading threshold;

[0102] Step 401, dynamically adjusting the intensity level of the projection area lighting basic parameter based on the lighting compensation coefficient, and real-time acquisition of the opening and closing state signal of the core showcase electronic lock, when the electronic lock state signal is open, taking the electronic lock physical position coordinates as the starting point to generate the navigation base path coordinates sequence of the robot inspection.

[0103] In the embodiment of the present application, the infrared thermal imaging sensor (1 per grid, sampling frequency of 5 seconds / time) deployed in each grid unit collects real-time human flow thermal value data. The human flow thermal value is represented by an index of 0-100: 0 means no one in the grid, 30-50 means 3-5 people staying, and 80-100 means 8 or more people densely gathered (the higher the value, the more dense the flow). For example, the thermal values collected by a certain grid unit for three consecutive times are 65, 68, and 70, and the system takes the average value 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 five levels and the corresponding compensation coefficients:

[0104] Thermal value 0-20 (sparse flow): compensation coefficient 0.8 (reduce lighting intensity to avoid energy waste);

[0105] 21-40 (less flow): compensation coefficient 0.9;

[0106] 41-60 (moderate flow): compensation coefficient 1.0 (maintain basic brightness);

[0107] 61-80 (more dense flow): compensation coefficient 1.2 (enhance brightness to improve visibility);

[0108] 81-100 (dense flow): compensation coefficient 1.5 (significantly enhance brightness to cope with obstruction and viewing needs).

[0109] Compare the effective thermal value of the grid unit with the threshold value: if the thermal value of a certain grid is 67, which falls in the interval of 61-80, the corresponding lighting compensation coefficient is 1.2; for a projection area containing multiple grids, the system takes the average value of the compensation coefficients of each grid as the final lighting compensation coefficient of the area (for example, a projection area contains 3 grids, the compensation coefficients are 1.2, 1.0, and 1.2, respectively, and the average value is 1.13, rounded to 1.1).

[0110] Step 401, the system first calls the projection area lighting base parameters generated in step 202 (such as the base brightness of 3000 lumens), and then combines the lighting compensation coefficient determined in step 400 (such as 1.1) to calculate the adjusted actual lighting intensity: multiply the base parameter value by the compensation coefficient, that is, 3000 lumens x 1.1 = 3300 lumens. At the same time, the system sets the intensity level limit: the minimum is not less than 70% of the base parameter (to avoid too dark), and the maximum is not more than 180% of the base parameter (to avoid too bright damage to the exhibits). For example, if the compensation coefficient is 1.5, the base parameter is 3000 lumens, and the adjustment is 4500 lumens, but because the 180% upper limit is 5400 lumens, the final execution is 4500 lumens; if the compensation coefficient is 0.8, the adjustment is 2400 lumens, which is higher than the 70% upper limit of 2100 lumens, so 2400 lumens is executed. The adjusted parameters are sent to the projection area lighting controller in real time through wireless signals to trigger brightness adjustment, and the whole process is completed within 10 seconds.

[0111] Generating robot navigation base path:

[0112] The system receives the state signal of the core showcase electronic lock in real time (transmitted through the low-power Bluetooth module, the signal is updated every 1 second): when the electronic lock is closed, the signal is "0"; when it is opened (such as when the staff opens the showcase for exhibit replacement or inspection), the signal is "1". When a "1" signal is detected, the system immediately calls the physical position coordinates of the electronic lock of the showcase (from the space rectangular coordinate system established in step 300, such as (10, 8)), and sets it as the starting point of the robot inspection. Then, the system screens out the target showcases (such as ZG-01, ZG-03, ZG-05) that need to be inspected according to the principle of "covering all core showcases + passing through high-value exhibit showcases", and obtains the coordinates of the electronic locks of these showcases (such as (12, 9), (8, 10), (15, 7)). Subsequently, according to the order of "starting point → nearest target showcase → next nearest target showcase → endpoint (exhibition hall robot charging pile, coordinate (5, 2))", the coordinate sequence of the navigation base path is generated. For example, taking (10, 8) as the starting point, passing through (12, 9) → (8, 10) → (15, 7) → (5, 2), the path between each coordinate point is a straight line, and the finally output coordinate sequence is "(10, 8) → (12, 9) → (8, 10) → (15, 7) → (5, 2)", which is used as the initial navigation base line of the robot.

[0113] The compensation coefficient is generated based on the real-time heat value of the grid people flow, so that the lighting intensity can dynamically match the people flow density of different areas (denser area is brighter, and sparser area is dimmed), which not only ensures the viewing experience of the audience, but also reduces invalid energy consumption, realizes "on-demand lighting", and ensures environmental adaptability through intensity level limitation to avoid lighting parameters exceeding equipment load or affecting exhibit protection (such as damage to light-sensitive exhibits due to excessive brightness), and rapid response (adjustment completed within 10 seconds) to ensure environmental adaptability. Only when the electronic lock of the showcase is opened, the navigation base path is generated, so that the robot inspection is focused on the "showcase that needs attention" (such as the open state which may have a security risk), the invalid inspection is reduced, and the security efficiency is improved; the coordinate sequence is generated in the optimal order, the navigation distance is shortened, and the time cost is saved. The lighting compensation coefficient and the robot navigation are both based on the grid unit data, which strengthens the linkage of "people flow distribution-lighting system-security robot", and upgrades the exhibition hall intelligent control from single device adjustment to multi-system collaborative response.

[0114] In a preferred embodiment of the present application, the above-mentioned step 5, the navigation base path and the showcase boundary coordinate input line segment intersection detection algorithm output collision avoidance instruction; the collision avoidance instruction and the lighting compensation coefficient are fed back to the air conditioning equipment, and the air supply intensity is adaptively adjusted based on the heat load increment and the grid people flow density, which can include:

[0115] Step 500, the navigation base path coordinate sequence and the showcase boundary coordinate are detected for line segment spatial relationship, and the intersection point coordinates of the navigation base path line segment and the showcase boundary line segment are identified;

[0116] Step 501, the intersection point is taken as a reference to segment reconstruction of the path, and collision avoidance instruction containing avoidance bias distance is generated;

[0117] Step 502, the path bias parameter in the collision avoidance instruction and the lighting compensation coefficient are transmitted to the air conditioning equipment control unit, and the people flow density distribution data of each grid unit in the space coordination grid is analyzed in real time;

[0118] Step 503, according to the people flow 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 amount;

[0119] Step 504, based on the total air supply intensity adjustment amount, the air supply equipment control instruction is output, and the continuous adaptive adjustment of the air supply machine speed is realized.

[0120] In the embodiment of the present application, the navigation base path is composed of the coordinate sequence generated by step 401, for example, "(10, 8)→(12, 9)→(8, 10)", and each two adjacent coordinates form a straight line segment (such as line segment 1: (10, 8) to (12, 9); line segment 2: (12, 9) to (8, 10)). The showcase boundary coordinates are generated based on the showcase positioning points collected in step 301--each showcase is a rectangular structure, and the positioning point (the lower left corner of the front face of the showcase) is taken as the starting point, and four boundary line segments are generated according to the actual size of the showcase (such as 1.2 meters long and 0.6 meters wide): the left side (from the positioning point to the positioning point+height), the right side (from the positioning point+length to the positioning point+length+height), the upper side (from the positioning point+length to the positioning point), and the lower side (from the positioning point+height to the positioning point+length+height), for example, the boundary line segments of a certain showcase 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 line segment and the four boundary line segments of all showcases one by one:

[0121] For line segment 1 (10, 8)→(12, 9) and the right side of a certain showcase (6.2, 6)→(6.2, 6.6), first, it is judged whether the extension directions of the two line segments can intersect (such as the path line segment extends to the right and upward, and the boundary line segment of the showcase extends vertically upward, and if there is no intersection in the direction, it is excluded);

[0122] For the line segments with possible intersection directions (such as the path line segment (12, 9)→(8, 10) and the upper side of a certain showcase (7, 9.5)→(9, 9.5)), the existence of intersection is determined by comparing the relative positions of the end points of the line segments (such as the start point (12, 9) of the path line segment is on the right side of the upper side of the showcase, and the end point (8, 10) is on the left side of the upper side of the showcase); finally, the intersection point coordinates are identified, for example, the intersection point of the above-mentioned intersecting line segments is (8.5, 9.5), and the point is recorded as a collision risk point.

[0123] Step 501, for the intersection point (such as (8.5, 9.5)) identified in step 500, the system reconstructs the original navigation base path by segmentation:

[0124] The original path line 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);

[0125] Based on the intersection point, a avoidance bias distance is set in the direction away from the showcase (such as the outside of the showcase, which is parallel to the boundary of the showcase); the avoidance bias distance is preset to 0.5 meters according to the size of the showcase, to ensure a safe gap between the robot and the showcase;

[0126] The reconstructed path detours at the intersection: for example, from (8.5, 9.5) to offset 0.5 meters outside the showcase, generating a new intermediate point (8.5, 10), and then connecting to the original end point (8, 10), the new path segment becomes (12, 9)→(8.5, 9.5)→(8.5, 10)→(8, 10);

[0127] The generated collision avoidance instruction includes: original path segment point coordinates (8.5, 9.5), offset direction (outside the showcase), offset distance (0.5 meters), reconstructed path coordinate sequence (including new intermediate point), execution priority (higher than the original navigation base path).

[0128] Step 502, through the industrial Ethernet inside the exhibition hall, the collision avoidance instruction (including offset distance 0.5 meters, reconstructed path coordinates, etc.) generated in step 501 and the lighting compensation coefficient (such as 1.2) determined in step 400 are packaged and transmitted to the air conditioning equipment control unit:

[0129] The transmission data format is "instruction 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 air conditioning control unit preferentially analyzes the crowd density distribution data of the space coordination grid: retrieves the real-time number of people in each grid unit (such as grid (2, 3) has 5 people, grid (5, 4) has 2 people) from the grid management system, and connects the grid area (such as the grid area of 0.5 meters x 0.5 meters is 0.25 square meters), calculates the "crowd density" (number of people / area) of each grid, for example, the density of grid (2, 3) is 5 people / 0.25 square meters=20 people / square meter.

[0130] Step 503, the air conditioning control unit calls the preset "crowd density-heat load increment table", which sets the corresponding heat load increment (unit: watt / square meter) according to the crowd density interval:

[0131] Density ≤ 5 people / square meter: increment 50 watts / square meter (low load, less human body heat dissipation);

[0132] 6-15 people / square meter: increment 100 watts / square meter (medium load);

[0133] Density ≥ 16 people / square meter: increment 150 watts / square meter (high load, concentrated human body heat dissipation).

[0134] Calculate the heat load increment grid by grid:

[0135] Grid (2, 3) density 20 people / square meter (≥ 16), corresponding increment 150 watts / square meter, grid area 0.25 square meters, the grid increment is 150 x 0.25 = 37.5 watts;

[0136] Grid (5, 4) density 2 people / square meter (≤5), corresponding increment 50 watts / square meter, area 1 square meter (passage grid), increment 50 x 1 = 50 watts.

[0137] The heat load increment values of all grids are aggregated to obtain a total heat load increment (e.g., 1200 watts after aggregation of all grids). According to the "total heat load increment-air supply intensity adjustment amount mapping relationship" (preset 100-watt increment corresponding to 10% increase in air supply intensity), the total air supply intensity adjustment amount is calculated: 1200 watts corresponds to an adjustment amount of 120% (i.e., a 20% increase over the base air supply intensity).

[0138] In step 504, the air conditioning control unit converts the total air supply intensity adjustment amount calculated in step 503 (e.g., 120%) into an air supply fan speed control instruction:

[0139] The base speed of the air supply fan is 1500 revolutions per minute (corresponding to the base air supply intensity), and the adjustment amount of 120% means that the target speed is: base speed x adjustment amount. The instruction includes a speed change rate (e.g., an increase of 300 revolutions per minute to avoid airflow shock caused by sudden changes in speed) and a duration (until the grid people flow density decreases to trigger a reduction in the adjustment amount); the control unit sends the instruction to the air supply fan and monitors the speed feedback in real time (e.g., the sensor detects the current speed of 1700 revolutions per minute), and if the target has not been reached, it continues to fine-tune until it stabilizes at 1800 revolutions per minute; when the people flow density in a certain grid decreases (e.g., the number of people in grid (2, 3) decreases to 2), steps 502-504 are repeated to dynamically reduce the total adjustment amount (e.g., to 90%), and the speed of the air supply fan is reduced to 1350 revolutions per minute, achieving continuous adaptive adjustment.

[0140] By detecting the collision risk points of the path and the showcases through line segment intersection detection, and reconstructing the path combined with the bias distance, the robot can maintain a safe distance from the showcases during navigation, avoiding equipment collision or damage to exhibits, and improving security and equipment operation safety. Based on the grid people flow density, the heat load increment is calculated unit by unit, so that the air supply intensity adjustment directly matches the actual needs of "more intense air supply in people flow dense areas", avoiding "local overcooling / overheating" caused by global extensive adjustment, and improving environmental comfort; the collision avoidance instruction (reflecting the local cooling caused by the robot's activity) and the lighting compensation coefficient (reflecting the cooling of lighting equipment) are fed back to the air conditioning system, so that the heat load calculation takes into account the "people + equipment" dual heat source, and the adjustment accuracy is higher. By continuously adjusting the speed (rather than stepwise switching), airflow noise or temperature fluctuations caused by sudden changes in air supply intensity are avoided, while real-time response to changes in people flow is ensured, ensuring that the exhibition hall environment is always in a state of dynamic balance, improving the audience experience and energy utilization efficiency.

[0141] As shown in Figure 2 The embodiment of the present application also provides a digital exhibition hall intelligent control data linkage control system, which comprises:

[0142] An instruction generation module is configured to collect people flow density data in real time at an exhibition hall entrance gate location, and input the people flow density data into an edge computing node; the edge computing node fuses temperature and humidity collection terminal data and air conditioning equipment protocols, dynamically calculates a heat load compensation value based on people flow density, and generates an environment adjustment instruction;

[0143] A parameter generation module is configured to input the people flow density data into a dynamic analysis unit, form a projection curtain reference point based on the optical characteristics of the holographic projection curtain, and generate projection area lighting basic parameters in combination with the illumination intensity of the projection curtain reference point;

[0144] A grid generation module is configured to take the core showcase electronic lock position as a showcase positioning point, combine the entrance gate coordinates and the curtain reference point coordinates, and apply a spatial region division method to generate a spatial coordination grid;

[0145] A path generation module is configured to generate a lighting compensation coefficient according to the grid unit people flow heat value, dynamically scale the lighting parameter intensity, and generate a robot navigation base path based on the showcase electronic lock state;

[0146] An adaptive adjustment module is configured to input the navigation base path and the showcase boundary coordinates into a line segment intersection detection algorithm to output a collision avoidance instruction; feed back the collision avoidance instruction and the lighting compensation coefficient to the air conditioning equipment, and send wind intensity adaptive adjustment based on the heat load increment and the grid people flow density.

[0147] It should be noted that the system corresponds to the above method, and all implementation manners in the above method embodiments are applicable to this embodiment and can achieve the same technical effects.

[0148] Embodiments of the present application also provide a computing device, comprising a processor and a memory storing a computer program, wherein the computer program is executed by the processor to perform the above method. All implementation manners in the above method embodiments are applicable to this embodiment and can achieve the same technical effects.

[0149] Embodiments of the present application also provide a computer readable storage medium storing instructions, which, when executed on a computer, cause the computer to perform the above method. All implementation manners in the above method embodiments are applicable to this embodiment and can achieve the same technical effects.

[0150] The above is the preferred embodiment of the present application, and it should be noted that for those skilled in the art, without departing from the principles of the present application, a number of improvements and refinements can be made, which should also be considered as the protection scope of the present application.

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 an environmental adjustment instruction, including: collecting crowd density data in real time through the infrared counting sensor of the entrance gate; the edge computing node receives the crowd density data and the temperature and humidity data uploaded by the temperature and humidity collection terminal, associates them 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 interval; calculates the temperature and humidity compensation value of the target space based on the heat load compensation coefficient, and generates an environmental adjustment instruction including the compensated target temperature value and target humidity value; 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, including: using the optical center point coordinates of the holographic projection screen as the projection screen reference point; using the light sensor to collect the ambient light intensity value at the projection screen reference point in real time; 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 the 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; 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: 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.

3. The method for intelligent data linkage control of a digital exhibition hall according to claim 2 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.

4. The method for intelligent data linkage control of a digital exhibition hall according to claim 3 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.

5. A digital exhibition hall intelligent data linkage control system, which implements the method according to any one of claims 1 to 4, 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 data from the temperature and humidity collection terminal with the air-conditioning equipment protocol, dynamically calculates the heat load compensation value based on the crowd density, and generates an environmental adjustment instruction, including: collecting crowd density data in real time through the infrared counting sensor of the entrance gate; the edge computing node receives the crowd density data and the temperature and humidity data uploaded by the temperature and humidity collection terminal, associates them 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 interval; calculates the temperature and humidity compensation value of the target space based on the heat load compensation coefficient, and generates an environmental adjustment instruction including the compensated target temperature value and target humidity value; The parameter generation module is used to 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, including: using the optical center point coordinates of the holographic projection screen as the projection screen reference point; collecting the ambient light intensity value at the projection screen reference point in real time through the light sensor; 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 the 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; 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 electronic lock of the display cabinet; 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.

6. 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 4.

7. A computer-readable storage medium, characterized in that The computer-readable storage medium stores a program, which implements the method according to any one of claims 1 to 4 when executed by a processor.

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