Intelligent exhibition hall exhibition guidance system and method based on big data

Abstract

The invention discloses an intelligent exhibition hall display guidance system and method based on big data, and particularly relates to the field of intelligent exhibition hall display guidance, and the system comprises a data batch division module, an exhibition hall data collection module, an exhibition hall data analysis module, a comprehensive data evaluation module, and a real-time feedback module. The exhibition hall data acquisition module comprises an exhibit data acquisition unit and a facility environment data acquisition unit and is used for acquiring target data in real time and transmitting the acquired data to the exhibition hall data analysis module; the exhibit data acquisition unit is used for acquiring exhibit physical state data, exhibit display effect data and exhibit associated data; the facility environment data acquisition unit is used for acquiring exhibition hall facility operation data, exhibition hall environment physical data and exhibition hall environment adjustment data; through real-time feedback and continuous observation, prompt information is sent out immediately once guiding deviation or abnormal conditions are found, and related workers can rapidly know the display state and potential problems of the intelligent exhibition hall.

Description

Technical Field

[0001] The present invention relates to the technical field of intelligent exhibition hall display guidance, and more specifically, to an intelligent exhibition hall display guidance system and method based on big data. Background Art

[0002] With the continuous advancement of modern science and technology and the vigorous development of the field of cultural display, the application of smart exhibition halls in various cultural communication, product display and other activities is becoming more and more common, and the quality and effectiveness requirements of their display guidance are also rising. The smart exhibition hall display guidance system and method based on big data has brought new challenges and opportunities to exhibition hall operations due to its key role in improving the visiting experience and display efficiency. Accurate display guidance can ensure that visitors can obtain information smoothly, improve visitor satisfaction, and meet the needs of high-quality display activities.

[0003] The existing intelligent exhibition hall display guidance system and method mainly include data acquisition module, data processing module, path planning module, and interactive feedback module. Through the data acquisition module, various types of sensors, such as cameras, infrared sensors, Bluetooth beacons, etc., are installed at various key locations in the exhibition hall to collect data such as the location, movement trajectory, stay time, and interest point attention of visitors, so as to achieve real-time monitoring of the visiting process; through the data processing module, the collected data is processed using specific data cleaning and classification algorithms to ensure the validity and availability of the data during the processing process; through the path planning module, the processed data is analyzed, and the optimal visiting path is planned according to the exhibit layout, visiting time limit and the characteristics of different visitor groups, providing visitors with convenient and efficient guidance; through the interactive feedback module, staff can intuitively understand the needs and visiting status of visitors, realize real-time adjustment and optimization of the display guidance, modify the guidance information in time, and improve the accuracy and flexibility of the display guidance.

[0004] However, this system and method still have some shortcomings in practical applications. For example, in terms of data collection, due to the arrangement density and type restrictions of sensors, some visitor data may be missing or inaccurate, resulting in insufficient collection of comprehensive data, which has an adverse effect on subsequent path planning; in terms of path planning, facing the diverse needs of visitors and the complex exhibition hall environment, it is easy to cause the planning results to be not personalized and accurate enough, which may result in visitors not being able to obtain a satisfactory visiting experience and reduce the effectiveness of the smart exhibition hall display guidance.

[0005] Therefore, there is an urgent need to provide a smart exhibition hall display guidance system and method based on big data to solve the problems of insufficient data collection and inaccurate planning results of the existing smart exhibition hall display guidance system, and to further improve the quality and reliability of smart exhibition hall display activities. Summary of the invention

[0006] In order to overcome the above-mentioned defects of the prior art, the embodiments of the present invention provide a smart exhibition hall display guidance system and method based on big data, and solve the problems raised in the above-mentioned background technology through the following scheme.

[0007] To achieve the above-mentioned purpose, the present invention provides the following technical solution: a smart exhibition hall display guidance system based on big data, comprising: Data batch division module: used to determine the data to be collected as target data, divide the target data into different batches according to equal time division, and mark them as 1, 2, ..., n in sequence; Exhibition hall data collection module: including an exhibit data collection unit and a facility environment data collection unit, which are used to collect target data in real time and transmit the collected data to the exhibition hall data analysis module; the exhibit data collection unit is used to collect exhibit physical state data, exhibit display effect data and exhibit related data; the facility environment data collection unit is used to collect exhibition hall facility operation data, exhibition hall environment physical data and exhibition hall environment adjustment data; Exhibition hall data analysis module: including an exhibit data analysis unit and a facility environment data analysis unit, used to analyze the data transmitted by the exhibition hall data acquisition module and transmit the analysis results to the comprehensive data evaluation module; the exhibit data analysis unit includes an exhibit physical state data analysis node, an exhibit display effect data analysis node and an exhibit association data analysis node; the facility environment data analysis unit includes an exhibition hall facility operation data analysis node, an exhibition hall environment physical data analysis node and an exhibition hall environment adjustment data analysis node; Comprehensive data evaluation module: including the intelligent exhibition hall display guide data analysis unit, which is used to comprehensively analyze the data transmitted by the exhibition hall data analysis module and transmit the analysis results to the real-time feedback module; Real-time feedback module: used to establish a preset value of the comprehensive evaluation index of the smart exhibition hall display guide, judge the comprehensive evaluation index value of the smart exhibition hall display guide according to the preset value of the comprehensive evaluation index of the smart exhibition hall display guide, and send a corresponding signal according to the judgment result.

[0008] Preferably, the exhibit physical state data include the exhibit material density change Ec, the exhibit internal stress distribution gradient Es and the exhibit surface micro-roughness growth rate Eg; the exhibit display effect data include the exhibit color contrast change rate Er, the exhibit light and shadow layering change coefficient Sc and the exhibit visual focus attraction change value Ca; the exhibit association data include the exhibit style similarity coefficient Se, the visit route matching degree Pm and the exhibit description information fit degree Fd; the exhibition hall facility operation data include the display equipment energy consumption fluctuation rate De, the display light luminous efficiency attenuation rate La and the display device mechanical transmission component wear rate Dt; the exhibition hall environment physical data include the air suspended particle concentration gradient Ap, the ground flatness change value Gc and the spatial magnetic field intensity fluctuation amplitude Fm; the exhibition hall environment adjustment data include the temperature adjustment system response time standard deviation Rs, the humidity adjustment change rate Hc and the air purification effect maintenance time fluctuation Pt.

[0009] Preferably, the exhibit physical state data analysis node is used to establish an exhibit physical state data calculation model, import the exhibit physical state data transmitted by the exhibition hall data acquisition module into the exhibit physical state data calculation model, and obtain the exhibit physical risk coefficient value. The exhibit physical state data calculation model is specifically expressed as: , in, Indicates the physical risk coefficient value of the exhibit calculated for the i-th time, Ec i Indicates the change in the density of the exhibit material collected for the i-th time, Es i represents the internal stress distribution gradient of the exhibit collected for the i-th time, Eg i Represents the growth rate of micro-roughness of the exhibit surface collected for the i-th time.

[0010] Preferably, the exhibit display effect data analysis node is used to establish an exhibit display effect data calculation model, import the exhibit display effect data transmitted by the exhibition hall data acquisition module into the exhibit display effect data calculation model, and obtain the exhibit display effect stability coefficient value. The exhibit display effect data calculation model is specifically expressed as: , in, represents the stability coefficient of the exhibit display effect calculated for the i-th time, Er i represents the rate of change of the color contrast of the exhibit collected for the i-th time, Er c Indicates the initial value of the exhibit color contrast, Sc i Sc represents the coefficient of variation of light and shadow layering of the exhibit collected for the i-th time, c Indicates the initial value of the light and shadow layering of the exhibit, Ca i represents the change value of the visual focus attraction of the exhibit collected for the i-th time, Cac Indicates the initial value of the exhibit's visual focus attraction.

[0011] Preferably, the exhibit association data analysis node is used to establish an exhibit association data calculation model, import the exhibit association data transmitted by the exhibition hall data acquisition module into the exhibit association data calculation model, and obtain the exhibit association coordination coefficient value. The exhibit association data calculation model is specifically expressed as: , in, Indicates the exhibit association coordination coefficient value calculated for the i-th time, Se i Pm represents the similarity coefficient of the exhibit style collected for the i-th time. i Indicates the matching degree of the visit path collected for the i-th time, Fd i Indicates the degree of fit of the exhibit description information collected for the i-th time.

[0012] Preferably, the exhibition hall facility operation data analysis node is used to establish an exhibition hall facility operation data calculation model, import the exhibition hall facility operation data transmitted by the exhibition hall data acquisition module into the exhibition hall facility operation data calculation model, and obtain the facility operation health coefficient value. The exhibition hall facility operation data calculation model is specifically expressed as: , in, Denotes the value of the facility operation health coefficient calculated for the i-th time, De i Denotes the energy consumption fluctuation rate of the display equipment collected for the i-th time, De def Indicates the normal threshold of the energy consumption fluctuation rate of the display equipment, La i Indicates the luminous efficiency attenuation rate of the display light collected for the i-th time, La def Indicates the normal threshold of the light luminous efficiency attenuation rate, Dt i represents the wear rate of the mechanical transmission parts of the display device collected for the i-th time, Dt def Indicates the normal threshold value of wear rate of mechanical transmission parts of the display device.

[0013] Preferably, the exhibition hall environment physical data analysis node is used to establish an exhibition hall environment physical data calculation model, import the exhibition hall environment physical data transmitted by the exhibition hall data acquisition module into the exhibition hall environment physical data calculation model, and obtain the environmental physical stability coefficient value. The exhibition hall environment physical data calculation model is specifically expressed as: , in, represents the environmental physical stability coefficient value calculated for the i-th time, Ap i represents the concentration gradient of airborne particles collected for the i-th time, Ap defGc represents the safety threshold of air suspended particle concentration gradient. i Gc represents the change in ground flatness collected for the i-th time. def Indicates the safety threshold of ground flatness change, Fm i Indicates the fluctuation amplitude of the spatial magnetic field intensity collected for the i-th time, Fm def Indicates the safety threshold of the fluctuation amplitude of the spatial magnetic field intensity.

[0014] Preferably, the exhibition hall environment adjustment data analysis node is used to establish an exhibition hall environment adjustment data calculation model, import the exhibition hall environment adjustment data transmitted by the exhibition hall data acquisition module into the exhibition hall environment adjustment data calculation model, and obtain the environment adjustment reliability coefficient value. The exhibition hall environment adjustment data calculation model is specifically expressed as: , Among them, γ i Represents the reliability coefficient of the environmental adjustment calculated for the i-th time, Rs i represents the standard deviation of the temperature control system response time collected for the i-th time, Hc i represents the humidity adjustment change rate collected for the i-th time, Pt i Indicates the fluctuation of the duration of air purification effect collected for the i-th time.

[0015] Preferably, the smart exhibition hall display guide data analysis unit is used to establish a smart exhibition hall display guide data calculation model, and the exhibit physical risk coefficient value, exhibit display effect stability coefficient value, exhibit association coordination coefficient value, facility operation health coefficient value, environmental physical stability coefficient and environmental regulation reliability coefficient value transmitted by the exhibition hall data analysis module are introduced into the smart exhibition hall display guide data calculation model to obtain the smart exhibition hall display guide comprehensive evaluation index value. The smart exhibition hall display guide data calculation model is specifically expressed as: , Among them, A represents the calculated comprehensive evaluation index value of the smart exhibition hall display guide, represents the physical risk coefficient value of the exhibit calculated for the i-th time, represents the stability coefficient value of the exhibit display effect calculated for the i-th time, represents the exhibit association coordination coefficient value calculated for the i-th time, represents the value of the facility operation health coefficient calculated for the i-th time, represents the minimum value of the calculated facility operation health coefficient, Indicates the maximum value of the calculated facility operation health coefficient, represents the environmental physical stability coefficient value calculated for the i-th time, represents the minimum value of the calculated environmental physical stability coefficient, Indicates the maximum value of the calculated environmental physical stability coefficient, represents the reliability coefficient value of the environmental adjustment calculated for the i-th time, represents the minimum value of the calculated environmental adjustment reliability coefficient, It represents the maximum value of the calculated environmental adjustment reliability coefficient, i represents starting from the i-th number, and n represents ending at the n-th number.

[0016] Preferably, the smart exhibition hall display guidance method based on big data includes: S1, data batch division step: used to determine the data to be collected as target data, divide the target data into different batches in an equal time division manner, and mark them as 1, 2, ..., n in sequence; S2, exhibition hall data collection step: including an exhibit data collection unit and a facility environment data collection unit, which are used to collect target data in real time and transmit the collected data to the exhibition hall data analysis step; the exhibit data collection unit is used to collect exhibit physical state data, exhibit display effect data and exhibit related data; the facility environment data collection unit is used to collect exhibition hall facility operation data, exhibition hall environment physical data and exhibition hall environment adjustment data; S3, exhibition hall data analysis step: including an exhibit data analysis unit and a facility environment data analysis unit, which are used to analyze the data transmitted in the exhibition hall data collection step and transmit the analysis results to the comprehensive data evaluation step; the exhibit data analysis unit includes an exhibit physical state data analysis node, an exhibit display effect data analysis node and an exhibit association data analysis node; the facility environment data analysis unit includes an exhibition hall facility operation data analysis node, an exhibition hall environment physical data analysis node and an exhibition hall environment adjustment data analysis node; S4, comprehensive data evaluation step: including a smart exhibition hall display guide data analysis unit, which is used to comprehensively analyze the data transmitted by the exhibition hall data analysis step and transmit the analysis results to the real-time feedback step; S5, real-time feedback step: used to establish a preset value of the comprehensive evaluation index of the smart exhibition hall display guide, judge the value of the comprehensive evaluation index of the smart exhibition hall display guide according to the preset value of the comprehensive evaluation index of the smart exhibition hall display guide, and send a corresponding signal according to the judgment result.

[0017] Technical effects and advantages of the present invention: The present invention effectively improves the pertinence and applicability of the guidance data by dividing the data of the smart exhibition hall display guidance into batches. By collecting the physical status data of exhibits, the display effect data of exhibits, the related data of exhibits, the operation data of exhibition hall facilities, the physical data of exhibition hall environment and the adjustment data of exhibition hall environment in multiple dimensions, this multi-faceted data collection mode has greatly broken through the one-sided limitations of traditional exhibition hall guidance data and provided a rich and reliable information basis for subsequent accurate display guidance. The present invention conducts in-depth analysis of the collected data through data analysis, and then calculates the exhibit physical risk coefficient value, exhibit display effect stability coefficient value, exhibit association coordination coefficient value, facility operation health coefficient value, environmental physical stability coefficient and environmental regulation reliability coefficient value, and clearly points out the factors that may affect the display guidance effect; The present invention significantly enhances the scientificity and effectiveness of the guidance scheme by comprehensively considering and deeply integrating the results of data analysis; through real-time feedback and continuous observation, once guidance deviations or abnormal situations are found, prompt information is immediately issued, so that relevant staff can quickly understand the status and potential problems of the smart exhibition hall display, and take timely actions to adjust and optimize the display strategy and guidance path, providing a solid guarantee for the realization of high-quality smart exhibition hall display guidance based on big data and efficient and reasonable operation management. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Figure 1 It is a schematic diagram of the system structure of the present invention.

[0019] Figure 2 It is a schematic diagram of the structure of the method of the present invention. DETAILED DESCRIPTION

[0020] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0021] As attached Figure 1 The big data-based intelligent exhibition hall display guidance system shown includes a system operation database, a system central processor and a user information terminal, and also includes a data batch division module, an exhibition hall data collection module, an exhibition hall data analysis module, a comprehensive data evaluation module, and a real-time feedback module.

[0022] The system operation database includes all data texts of the intelligent exhibition hall display guidance system based on big data, and collects the information text output by each module in real time. The system central processor is used to control the information text instructions output by each module. The user information terminal is an information output device for receiving the intelligent exhibition hall display guidance system based on big data.

[0023] The data batch division module is used to determine the data to be collected as target data, divide the target data into different batches in an equal time division manner, and mark them as 1, 2, ..., n in sequence.

[0024] In this embodiment, it is specifically necessary to explain that: the method of dividing by equal time is to divide the database according to the characteristics of each target data through the background system running the database. According to the real-time requirements of each target data, it is collected once at an equal interval and divided into different collection times.

[0025] The exhibition hall data collection module includes an exhibit data collection unit and a facility environment data collection unit, which are used to collect target data in real time and transmit the collected data to the exhibition hall data analysis module; the exhibit data collection unit is used to collect exhibit physical status data, exhibit display effect data and exhibit related data; the facility environment data collection unit is used to collect exhibition hall facility operation data, exhibition hall environment physical data and exhibition hall environment adjustment data.

[0026] In this embodiment, it is specifically necessary to explain that: the exhibit physical state data includes the exhibit material density change Ec, the exhibit internal stress distribution gradient Es and the exhibit surface micro-roughness growth rate Eg; the exhibit display effect data includes the exhibit color contrast change rate Er, the exhibit light and shadow layering change coefficient Sc and the exhibit visual focus attraction change value Ca; the exhibit association data includes the exhibit style similarity coefficient Se, the visit path matching degree Pm and the exhibit description information fit degree Fd; the exhibition hall facility operation data includes the display equipment energy consumption fluctuation rate De, the display light luminous efficiency attenuation rate La and the display device mechanical transmission component wear rate Dt; the exhibition hall environment physical data includes the air suspended particle concentration gradient Ap, the ground flatness change value Gc and the spatial magnetic field intensity fluctuation amplitude Fm; the exhibition hall environment adjustment data includes the temperature adjustment system response time standard deviation Rs, the humidity adjustment change rate Hc and the air purification effect maintenance time fluctuation Pt; The density change of the exhibit material indicates a variety of problems. For example, reactions such as water absorption, expansion, oxidation, etc. caused by environmental humidity, temperature changes, or chemical erosion may also be a precursor to internal structural damage. By monitoring the density change, potential problems of the exhibits can be discovered in a timely manner, providing a basis for preventive protection measures and ensuring the physical integrity of the exhibits. The collection method is: use high-precision, non-destructive density measuring instruments, such as densitometers based on the principle of gamma ray absorption or ultrasonic measurement. Set measurement points at multiple representative locations of the exhibits and measure at fixed times every month. For small exhibits, overall measurement can be used; for large exhibits, a sufficient number of sample points are selected at key locations. When measuring, ensure that the instrument is accurately calibrated and the environmental conditions are stable to reduce errors. Compare the data of each measurement with the initial density data of the exhibit to calculate the density change.

[0027] The stress distribution gradient inside the exhibit reflects the stress conditions inside the exhibit. Uneven stress distribution may be caused by factors such as gravity, thermal stress caused by temperature changes, and external vibrations that the exhibit is subjected to during the display process. Excessive stress gradients may cause microcracks inside the exhibit, which then develop into macro cracks and damage the exhibit structure. Monitoring this parameter can detect unstable factors in the internal structure of the exhibit in advance and take corresponding protective measures, such as adjusting the display position, improving the supporting structure, etc. The collection method is: using a stress sensor array, select a suitable sensor type according to the material, shape and structural characteristics of the exhibit, such as a resistance strain gauge sensor or a fiber grating sensor. For situations where sensors can be implanted inside the exhibit, the sensor is installed at the key stress point when making or repairing the exhibit; for situations where the surface of the exhibit cannot be damaged, non-contact stress measurement technology can be used, such as a measurement method based on the principle of laser interferometry. The sensor collects stress data in real time, transmits it to the computer through the data acquisition system, and uses professional software to calculate the stress distribution gradient based on the geometric model of the exhibit.

[0028] The growth rate of the microscopic roughness of the exhibit surface directly affects its appearance quality and artistic value. The increase in roughness may be caused by abrasion of particles in the air, touch by visitors, chemical corrosion, etc. By monitoring the growth rate of roughness, the protection status of the exhibit surface can be evaluated, and measures such as cleaning, repair or renewal of protective coating can be taken in time to extend the life of the exhibit and maintain its display effect. The collection method is: using an atomic force microscope (AFM) or other high-precision surface topography measurement equipment, such as a white light interferometer. Select several representative areas on the surface of the exhibit as measurement points. These areas should include parts that are susceptible to wear, corrosion or contact. These points are measured every year under the same environmental conditions (such as stable temperature and humidity). AFM scans the sample surface with a tiny probe, measures the interaction force between the probe and the sample surface, and obtains surface topography information. Compare the roughness data obtained from each measurement with the data of the previous year to calculate the growth rate.

[0029] The rate of change of the color contrast of the exhibit is one of the important factors in the visual appeal of the exhibit. The rate of change can reflect the stability of the color of the exhibit during the display process. If the color contrast changes, it may be due to changes in light intensity and spectrum, aging or pollution of the exhibit surface, etc. Monitoring this parameter helps to adjust the display environment in time, such as changing lights, cleaning exhibits, etc., to maintain the visual effect and artistic appeal of the exhibits. The collection method is: install color calibration sensors in the exhibition hall. These sensors should be distributed at different positions and angles around the exhibits to fully obtain the color information of the exhibits. The sensors need to be calibrated regularly to ensure the accuracy of the measurement. At a fixed time every month, use sensors to measure the color contrast between the exhibits and the surrounding environment (such as the display background, adjacent exhibits). Through a special color analysis algorithm, the currently measured contrast data is compared with the initial data to calculate the rate of change.

[0030] The coefficient of variation of the light and shadow layering of the exhibit reflects the key to the three-dimensional sense and artistic expression of the exhibit. The coefficient of variation can reflect the change of the light and shadow effect of the exhibit under the display light. This change may be caused by changes in the position, angle, intensity of the light, or changes in the physical properties of the surface of the exhibit. By monitoring this parameter, the lighting settings can be optimized to ensure that the exhibit presents the best visual effect and enhance the viewing experience of visitors. The collection method is: using a three-dimensional light field analysis device to sample the light field of the exhibit from multiple directions and angles. These devices work together through multiple cameras or light detectors to capture the light reflection, refraction and scattering information on the surface of the exhibit. After the exhibit is installed, the first comprehensive light field measurement is performed as the baseline data. After that, the light field data is re-measured regularly (such as every quarter) under the same environmental conditions (such as when the lights in the exhibition hall are fully turned on and there is no other external light interference). Through a special light and shadow analysis algorithm, the data of different periods are compared to calculate the layering variation coefficient.

[0031] The change value of the visual focus attraction of the exhibit can measure the change in the exhibit's ability to attract the visitor's visual attention. One of the purposes of displaying an exhibit is to attract the visitor's attention. If the attraction decreases, the display method, location or surrounding environment of the exhibit may need to be adjusted. By monitoring the change value, the display strategy can be optimized to improve the display effect and visit value of the exhibit. The collection method is as follows: analyze the surveillance camera data in the exhibition hall, first identify and track the visitor's eye gaze direction through an image recognition algorithm. Arrange multiple high-definition surveillance cameras in the exhibition hall to ensure that all exhibit display areas are covered, and the angle and focal length of the camera are calibrated to ensure image quality. Analyze the video data of each time period (such as different time periods every day), and count the frequency and duration of visitors' eyes falling on the exhibits. Compare the current statistical data with the historical data to obtain the change value of attraction.

[0032] The exhibit style similarity coefficient is used to evaluate the visual and thematic coordination between exhibits. In the exhibition hall display, the coordination between exhibits can create a unified artistic atmosphere and improve the visitor's experience and understanding of the exhibits. If the similarity coefficient is too low, it may destroy the display style of the entire exhibition hall and make visitors feel abrupt. By monitoring this coefficient, the layout of the exhibits can be adjusted to enhance the overall beauty and logic of the exhibition hall. The collection method is: digital analysis of the shape, color, theme and other elements of the exhibits. For the shape, the geometric model of the exhibits can be obtained through three-dimensional scanning technology to analyze its line and shape characteristics; for the color, the main color tone, color distribution and other information of the exhibits can be extracted using color analysis software; for the theme, it is determined by combining manual annotation and text analysis. These data are compared with the corresponding data of the surrounding exhibits, and the style similarity coefficient is calculated using a special similarity calculation algorithm. During the calculation process, different weights can be given according to the importance of different elements to more accurately reflect the relationship between the exhibits.

[0033] The tour path matching degree is used to measure the logical coherence between the theme of the exhibits and the tour path. A reasonable tour path should enable visitors to naturally understand the relationship between the exhibits during the visit and maintain the fluency and interest of the visit. If the matching degree is low, visitors may be confused or lose interest in continuing the visit. By monitoring this parameter, the planning of the tour path can be optimized to improve the visitor's satisfaction and understanding of the connotation of the exhibits. The collection method is: analyzing the theme information of the exhibits, which can be summarized through the introduction materials, historical background, artistic style and other aspects of the exhibits. At the same time, the preset tour path is logically analyzed to determine the order and relationship between each node (exhibit) in the path. Through semantic analysis and knowledge graph and other technologies, the logical relationship between the theme of the exhibits and the tour path is quantified. For example, the theme of the exhibits can be divided into different categories and levels, and the tour path can be regarded as a sequence composed of these categories and levels, and the matching degree between the two can be calculated.

[0034] The degree of fit of the exhibit description information reflects the degree to which the display description information accurately conveys the connotation of the exhibit. Accurate display descriptions can help visitors better understand the value, historical background and artistic characteristics of the exhibits. If the degree of fit is low, it may cause visitors to misunderstand the exhibits and reduce the quality of the visit. By monitoring this parameter, the display description content can be updated and improved in a timely manner to improve the accuracy of information transmission. The collection method is: evaluate the fit through text analysis and semantic matching technology. First, conduct an in-depth study of the exhibits themselves, extract their key features, historical and cultural background, artistic value and other information, and structure this information. Then, perform the same analysis on the display description content. Use natural language processing algorithms, such as word vector models, semantic similarity calculation algorithms, etc., to compare the similarity between the actual information of the exhibits and the display description content. Determine the degree of fit based on the similarity calculation results. For example, the corresponding weight can be assigned according to the number and importance of matching key information to calculate the final fit percentage.

[0035] The equipment energy consumption fluctuation rate reflects the stability of energy utilization of the display equipment during operation. Stable energy consumption is an important indicator of normal operation of the equipment. If the fluctuation rate is too large, it may indicate that there is a fault in the equipment, such as circuit aging, increased resistance caused by wear of mechanical parts, etc. Monitoring this parameter can timely discover potential problems of the equipment, reduce energy waste, and ensure the continuous display. The collection method is: real-time energy consumption monitoring of the display equipment through energy monitoring equipment such as smart meters. Smart meters should have high-precision and high-frequency data collection functions, and can accurately record the energy consumption data of the equipment under different working conditions. Connect the meter to the circuit of the display equipment to ensure that the actual energy consumption of the equipment is measured. After collecting energy consumption data every day, the fluctuation rate is calculated using statistical analysis methods. The calculation of the fluctuation rate can use statistical indicators such as standard deviation and coefficient of variation to compare the energy consumption data of the day with the average energy consumption data of the past period of time (such as a week) to obtain the fluctuation rate.

[0036] The luminous efficiency decay rate of the display light is an important indicator of the performance of the display light, and its decay rate reflects the aging speed of the light during use. As the luminous efficiency decreases, the brightness and lighting quality of the light will decrease, affecting the display effect of the exhibits. Monitoring this parameter can replace the aging lights in time to ensure that the exhibits are always in a good lighting environment. The collection method is: use professional lighting measurement equipment such as a photometer to regularly measure the luminous intensity of the display light. The photometer should measure the light at a specified distance and angle when the light is in a stable working state to ensure the accuracy of the measurement results. Record the time and luminous intensity data of each measurement. When the cumulative use time of the light reaches an integer multiple of 1000 hours, calculate the luminous efficiency decay rate based on the change in luminous intensity. The luminous efficiency can be calculated by the ratio of luminous intensity to light power. The current luminous efficiency is compared with the initial luminous efficiency to obtain the decay rate.

[0037] The wear rate of the mechanical transmission parts of the display device indicates that the mechanical transmission parts of the display device will wear out during long-term operation, and the wear rate directly affects the operation accuracy and stability of the device. Excessive wear may cause the transmission parts to loosen and the transmission efficiency to decrease, thereby affecting the display effect of the exhibits, such as the unsmooth translation or rotation of the display platform and the inaccurate display angle. By monitoring the wear rate, the replacement or maintenance of the parts can be arranged in time to ensure the normal operation of the display device. The collection method is: using high-precision displacement sensors or ultrasonic detection equipment to measure the wear of the transmission parts. For displacement sensors, they can be installed near the key connection points or motion trajectories of the transmission parts, and the degree of wear can be inferred by measuring the small displacement changes of the parts during the movement. Ultrasonic detection equipment detects changes in the internal structure of the parts, such as cracks, cavities, etc., by emitting ultrasonic waves and receiving reflected waves. These changes are often related to wear. Measurements are taken every month during equipment maintenance, and the wear data obtained by the measurement is compared with the initial size of the parts to calculate the wear rate.

[0038] The airborne particle concentration gradient reflects the difference in air cleanliness at different locations in the exhibition hall. A large concentration gradient may cause dust accumulation in local areas, which may adhere to the surface of the exhibits and cause damage to the exhibits, such as corrosion and wear, and also affect the visual experience of visitors. By monitoring this parameter, the operating parameters of the air purification equipment can be adjusted to optimize air circulation and keep the air in the exhibition hall uniformly clean. The collection method is: install air particle counters at different heights and positions in the exhibition hall. These counters should be able to accurately measure the concentration of suspended particles in different particle size ranges. For example, counters that can measure common particle sizes such as PM2.5 and PM10 can be selected. In order to obtain accurate concentration gradient data, the installation location of the counter should cover various areas of the exhibition hall, including exhibit display areas, passages, corners, etc. Collect particle concentration data regularly (such as every hour), and calculate the concentration gradient based on the data at different locations through spatial interpolation and gradient calculation methods.

[0039] The ground flatness change value is crucial for the display of exhibits and the safety of visitors. The change in ground flatness may be caused by aging of ground materials, foundation settlement and other reasons. Uneven ground may cause the exhibit display device to tilt, affect the display angle and stability of the exhibits, and may also make it inconvenient for visitors to walk, or even cause safety accidents such as falls. By monitoring this parameter, ground problems can be discovered in time and repair work can be arranged. The collection method is: use high-precision measuring equipment such as laser level or 3D terrain scanner to measure the flatness of the exhibition hall floor. The laser level can emit a laser beam along the measurement route laid on the ground and detect the height difference of the ground by receiving reflected light. The 3D terrain scanner can obtain more comprehensive 3D topographic information of the ground. The first measurement after the construction of the exhibition hall is completed is used as the benchmark data, and the measurement is re-performed every year under the same measurement route and conditions. The two measurement results are compared to calculate the flatness change value.

[0040] The fluctuation amplitude of the spatial magnetic field intensity indicates that the stability of the magnetic field intensity in the exhibition hall space is crucial. Fluctuations in magnetic field intensity may interfere with the normal performance of exhibits, or even cause irreversible damage to exhibits. By monitoring this parameter, magnetic field anomalies can be discovered in a timely manner, and magnetic field shielding or adjustment measures can be taken to protect the safety of exhibits. The collection method is to set up a magnetic field intensity sensor network in the exhibition hall. The sensor should have high sensitivity and stability, and be able to accurately measure the magnetic field intensity in different directions. The distribution of sensors should cover all areas of the exhibition hall, especially around the exhibits. Collect magnetic field intensity data in real time, perform statistical analysis on the data once every hour, and calculate the fluctuation amplitude. The fluctuation amplitude can be determined by calculating the difference between the maximum and minimum magnetic field intensity within the hour, or by using statistical methods such as standard deviation.

[0041] The standard deviation of the temperature control system response time reflects the performance stability of the exhibition hall temperature control system. If the standard deviation is large, it means that the response speed of the system varies greatly under different circumstances, which may cause temperature fluctuations in the exhibition hall, affecting the preservation of exhibits and the comfort of visitors. By monitoring this parameter, the control algorithm of the temperature control system can be optimized and the reliability of the system can be improved. The collection method is: during the operation of the temperature control system, the temperature changes in the exhibition hall are monitored in real time through the temperature sensor. When the temperature control system receives a temperature adjustment instruction (such as a set temperature change or a temperature deviation from the set range), the time when the instruction is issued and the time when the temperature in the exhibition hall reaches the target temperature are recorded, and the response time for each time is calculated. Collect a large amount of response time data over a period of time (such as a month) and use statistical software to calculate the standard deviation.

[0042] The humidity control change rate reflects the stability of the working effect of the humidity control equipment. Humidity has an important impact on the preservation of exhibits and the comfort of visitors. If the efficiency change rate is abnormal, it may be a signal of equipment failure or maintenance, such as filter blockage, aging of humidification / dehumidification components, etc. By monitoring this parameter, the equipment can be maintained or calibrated in time to maintain a suitable humidity environment in the exhibition hall. The collection method is: use humidity sensors to monitor humidity changes at different locations in the exhibition hall, and record the operating parameters of the humidity control equipment (such as humidification amount, dehumidification amount, operating time, etc.). According to the working principle of the equipment and related calculation formulas, the humidification / dehumidification efficiency is calculated. The calculation results are analyzed every month, and the efficiency data of the current month is compared with the data of the past few months to calculate the change rate.

[0043] The fluctuation of the air purification effect maintenance time is used to evaluate the continuous working ability of the air purification equipment. A large fluctuation means that the maintenance time of the purification effect of the equipment in different operation cycles is unstable, which may be a manifestation of equipment failure, filter replacement or other operation problems. By monitoring this parameter, the air purification equipment can be maintained and managed in time to ensure the air environment quality of the exhibition hall. The collection method is: install air quality monitoring sensors in the exhibition hall to monitor the concentration of pollutants in the air (such as formaldehyde, volatile organic compounds, particulate matter, etc.). When the air purification equipment is turned on, the time point when the air quality reaches the excellent standard is recorded as the time when the purification effect begins to be maintained; when the air quality drops to the set threshold again, it is recorded as the time when the purification effect ends, and the maintenance time of each time is calculated. After multiple operation cycles, analyze the maintenance time data and calculate the fluctuation, such as using statistical methods such as standard deviation.

[0044] The exhibition hall data analysis module includes an exhibit data analysis unit and a facility environment data analysis unit, which are used to analyze the data transmitted by the exhibition hall data acquisition module and transmit the analysis results to the comprehensive data evaluation module; the exhibit data analysis unit includes an exhibit physical state data analysis node, an exhibit display effect data analysis node and an exhibit related data analysis node; the facility environment data analysis unit includes an exhibition hall facility operation data analysis node, an exhibition hall environment physical data analysis node and an exhibition hall environment adjustment data analysis node.

[0045] In this embodiment, it is specifically required to be explained that: the exhibit physical state data analysis node is used to establish an exhibit physical state data calculation model, and the exhibit physical state data transmitted by the exhibition hall data acquisition module is imported into the exhibit physical state data calculation model to obtain the exhibit physical risk coefficient value. The exhibit physical state data calculation model is specifically expressed as: , in, Indicates the physical risk coefficient value of the exhibit calculated for the i-th time, Ec i Indicates the change in the density of the exhibit material collected for the i-th time, Es i represents the internal stress distribution gradient of the exhibit collected for the i-th time, Eg i Represents the growth rate of micro-roughness of the exhibit surface collected for the i-th time.

[0046] In this embodiment, it is specifically required to be explained that: the exhibit display effect data analysis node is used to establish an exhibit display effect data calculation model, and the exhibit display effect data transmitted by the exhibition hall data acquisition module is imported into the exhibit display effect data calculation model to obtain the exhibit display effect stability coefficient value. The exhibit display effect data calculation model is specifically expressed as: , in, represents the stability coefficient of the exhibit display effect calculated for the i-th time, Er i represents the rate of change of the color contrast of the exhibit collected for the i-th time, Er c Indicates the initial value of the exhibit color contrast, Sc i Sc represents the coefficient of variation of light and shadow layering of the exhibit collected for the i-th time, c Indicates the initial value of the light and shadow layering of the exhibit, Ca i represents the change value of the visual focus attraction of the exhibit collected for the i-th time, Ca c Indicates the initial value of the exhibit's visual focus attraction.

[0047] In this embodiment, it is specifically required to be explained that: the exhibit association data analysis node is used to establish an exhibit association data calculation model, import the exhibit association data transmitted by the exhibition hall data acquisition module into the exhibit association data calculation model, and obtain the exhibit association coordination coefficient value. The exhibit association data calculation model is specifically expressed as: , in, Indicates the exhibit association coordination coefficient value calculated for the i-th time, Se i Pm represents the similarity coefficient of the exhibit style collected for the i-th time. i Indicates the matching degree of the visit path collected for the i-th time, Fd i Indicates the degree of fit of the exhibit description information collected for the i-th time.

[0048] In this embodiment, it is specifically required to be explained that: the exhibition hall facility operation data analysis node is used to establish an exhibition hall facility operation data calculation model, and the exhibition hall facility operation data transmitted by the exhibition hall data acquisition module is imported into the exhibition hall facility operation data calculation model to obtain the facility operation health coefficient value. The exhibition hall facility operation data calculation model is specifically expressed as: , in, Denotes the value of the facility operation health coefficient calculated for the i-th time, De i Denotes the energy consumption fluctuation rate of the display equipment collected for the i-th time, De def Indicates the normal threshold of the energy consumption fluctuation rate of the display equipment, La i Indicates the luminous efficiency attenuation rate of the display light collected for the i-th time, La def Indicates the normal threshold of the light luminous efficiency attenuation rate, Dt i represents the wear rate of the mechanical transmission parts of the display device collected for the i-th time, Dt def Indicates the normal threshold value of wear rate of mechanical transmission parts of the display device.

[0049] In this embodiment, it is specifically required to explain that: the exhibition hall environment physical data analysis node is used to establish an exhibition hall environment physical data calculation model, and the exhibition hall environment physical data transmitted by the exhibition hall data acquisition module is imported into the exhibition hall environment physical data calculation model to obtain the environmental physical stability coefficient value. The exhibition hall environment physical data calculation model is specifically expressed as: , in, represents the environmental physical stability coefficient value calculated for the i-th time, Ap i represents the concentration gradient of airborne particles collected for the i-th time, Ap def Gc represents the safety threshold of air suspended particle concentration gradient. iGc represents the change in ground flatness collected for the i-th time. def Indicates the safety threshold of ground flatness change, Fm i Indicates the fluctuation amplitude of the spatial magnetic field intensity collected for the i-th time, Fm def Indicates the safety threshold of the fluctuation amplitude of the spatial magnetic field intensity.

[0050] In this embodiment, it is specifically required to explain that: the exhibition hall environment adjustment data analysis node is used to establish an exhibition hall environment adjustment data calculation model, and the exhibition hall environment adjustment data transmitted by the exhibition hall data acquisition module is imported into the exhibition hall environment adjustment data calculation model to obtain the environment adjustment reliability coefficient value. The exhibition hall environment adjustment data calculation model is specifically expressed as: , Among them, γ i Represents the reliability coefficient of the environmental adjustment calculated for the i-th time, Rs i represents the standard deviation of the temperature control system response time collected for the i-th time, Hc i represents the humidity adjustment change rate collected for the i-th time, Pt i Indicates the fluctuation of the duration of air purification effect collected for the i-th time.

[0051] The comprehensive data evaluation module includes an intelligent exhibition hall display guide data analysis unit, which is used to comprehensively analyze the data transmitted by the exhibition hall data analysis module and transmit the analysis results to the real-time feedback module.

[0052] In this embodiment, it is specifically required to be explained that: the smart exhibition hall display guide data analysis unit is used to establish a smart exhibition hall display guide data calculation model, and the exhibit physical risk coefficient value, exhibit display effect stability coefficient value, exhibit association coordination coefficient value, facility operation health coefficient value, environmental physical stability coefficient and environmental regulation reliability coefficient value transmitted by the exhibition hall data analysis module are introduced into the smart exhibition hall display guide data calculation model to obtain the smart exhibition hall display guide comprehensive evaluation index value. The smart exhibition hall display guide data calculation model is specifically expressed as: , Among them, A represents the calculated comprehensive evaluation index value of the smart exhibition hall display guide, represents the physical risk coefficient value of the exhibit calculated for the i-th time, represents the stability coefficient value of the exhibit display effect calculated for the i-th time, represents the exhibit association coordination coefficient value calculated for the i-th time, represents the value of the facility operation health coefficient calculated for the i-th time, represents the minimum value of the calculated facility operation health coefficient, Indicates the maximum value of the calculated facility operation health coefficient, Represents the value of the environmental physical stability coefficient for the i-th calculation, Represents the minimum value of the calculated environmental physical stability coefficient, Represents the maximum value of the calculated environmental physical stability coefficient, Represents the value of the environmental regulation reliability coefficient for the i-th calculation, Represents the minimum value of the calculated environmental regulation reliability coefficient, Represents the maximum value of the calculated environmental regulation reliability coefficient. i represents starting from the i-th number, and n represents ending at the n-th number.

[0053] The real-time feedback module is used to establish a preset value of the comprehensive evaluation index for the intelligent exhibition hall display guide, judge the value of the comprehensive evaluation index for the intelligent exhibition hall display guide according to the preset value, and send out corresponding signals according to the judgment results.

[0054] In this embodiment, specifically, it should be noted that: the preset value of the comprehensive evaluation index for the intelligent exhibition hall display guide is marked as A def , when A def < A, a normal signal is sent. This signal indicates that the preset value of the comprehensive evaluation index for the intelligent exhibition hall display guide is less than the value of the comprehensive evaluation index for the intelligent exhibition hall display guide, indicating that the display guide effect of the intelligent exhibition hall is good; when A def > A, a warning signal is sent to relevant technical management personnel. This signal indicates that the preset value of the comprehensive evaluation index for the intelligent exhibition hall display guide is greater than or equal to the value of the comprehensive evaluation index for the intelligent exhibition hall display guide, indicating that the display guide effect of the intelligent exhibition hall is poor.

[0055] Based on the above solution and the appendix Figure 2 , this application embodiment also discloses a method for intelligent exhibition hall display guide based on big data, including: S1. Data batch division step: used to determine the data to be collected as target data, divide the target data into different batches in the way of equal-time division, and mark them as 1, 2,..., n in sequence; S2. Exhibition hall data collection step: including an exhibit data collection unit and a facility environment data collection unit, used to collect the target data in real time and transmit the collected data to the exhibition hall data analysis step; the exhibit data collection unit is used to collect exhibit physical state data, exhibit display effect data, and exhibit association data; the facility environment data collection unit is used to collect exhibition hall facility operation data, exhibition hall environmental physical data, and exhibition hall environmental regulation data; S3, exhibition hall data analysis step: including an exhibit data analysis unit and a facility environment data analysis unit, which are used to analyze the data transmitted in the exhibition hall data collection step and transmit the analysis results to the comprehensive data evaluation step; the exhibit data analysis unit includes an exhibit physical state data analysis node, an exhibit display effect data analysis node and an exhibit association data analysis node; the facility environment data analysis unit includes an exhibition hall facility operation data analysis node, an exhibition hall environment physical data analysis node and an exhibition hall environment adjustment data analysis node; S4, comprehensive data evaluation step: including a smart exhibition hall display guide data analysis unit, which is used to comprehensively analyze the data transmitted by the exhibition hall data analysis step and transmit the analysis results to the real-time feedback step; S5, real-time feedback step: used to establish a preset value of the comprehensive evaluation index of the smart exhibition hall display guide, judge the value of the comprehensive evaluation index of the smart exhibition hall display guide according to the preset value of the comprehensive evaluation index of the smart exhibition hall display guide, and send a corresponding signal according to the judgment result.

[0056] The present invention effectively improves the pertinence and applicability of the guidance data by dividing the data of the smart exhibition hall display guidance into batches. By collecting data on the physical status of exhibits, data on the display effects of exhibits, data on the association of exhibits, data on the operation of exhibition hall facilities, data on the physical environment of the exhibition hall, and data on the regulation of the environment of the exhibition hall in multiple dimensions, this multi-faceted data collection model has greatly broken through the one-sided limitations of traditional exhibition hall guidance data, and provided a rich and reliable information basis for subsequent accurate display guidance; through data analysis, the collected data is deeply analyzed, and then the physical risk coefficient of exhibits, the stability coefficient of exhibit display effects, the coordination coefficient of exhibit associations, the health coefficient of facility operation, the physical stability coefficient of the environment, and the reliability coefficient of environmental regulation are calculated, and the factors that may affect the effect of display guidance are clearly pointed out; through comprehensive consideration and in-depth integration of the results of data analysis, the scientific nature and effectiveness of the guidance plan are significantly enhanced; through real-time feedback and continuous observation, once a deviation or abnormality in guidance is found, a prompt message will be immediately issued, so that relevant staff can quickly understand the status and potential problems of the smart exhibition hall display, and take timely actions to adjust and optimize the display strategy and guidance path, providing a solid guarantee for the realization of high-quality smart exhibition hall display guidance based on big data and efficient and reasonable operation management.

[0057] Secondly: In the drawings of the embodiments disclosed in the present invention, only the structures related to the embodiments disclosed in the present invention are involved, and other structures can refer to the general design. In the absence of conflict, the same embodiment and different embodiments of the present invention can be combined with each other; Finally: The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. The intelligent exhibition hall display guidance system based on big data is characterized by: include: Data batch division module: used to determine the data to be collected as target data, divide the target data into different batches according to equal time division, and mark them as 1, 2, ..., n in sequence; Exhibition hall data collection module: including an exhibit data collection unit and a facility environment data collection unit, which are used to collect target data in real time and transmit the collected data to the exhibition hall data analysis module; the exhibit data collection unit is used to collect exhibit physical state data, exhibit display effect data and exhibit related data; the facility environment data collection unit is used to collect exhibition hall facility operation data, exhibition hall environment physical data and exhibition hall environment adjustment data; Exhibition hall data analysis module: including an exhibit data analysis unit and a facility environment data analysis unit, used to analyze the data transmitted by the exhibition hall data acquisition module and transmit the analysis results to the comprehensive data evaluation module; the exhibit data analysis unit includes an exhibit physical state data analysis node, an exhibit display effect data analysis node and an exhibit association data analysis node; the facility environment data analysis unit includes an exhibition hall facility operation data analysis node, an exhibition hall environment physical data analysis node and an exhibition hall environment adjustment data analysis node; Comprehensive data evaluation module: including the intelligent exhibition hall display guide data analysis unit, which is used to comprehensively analyze the data transmitted by the exhibition hall data analysis module and transmit the analysis results to the real-time feedback module; Real-time feedback module: used to establish a preset value of the comprehensive evaluation index of the smart exhibition hall display guide, judge the comprehensive evaluation index value of the smart exhibition hall display guide according to the preset value of the comprehensive evaluation index of the smart exhibition hall display guide, and send a corresponding signal according to the judgment result.

2. The intelligent exhibition hall display guidance system based on big data according to claim 1 is characterized by: The exhibit physical state data includes the exhibit material density change Ec, the exhibit internal stress distribution gradient Es and the exhibit surface micro-roughness growth rate Eg; the exhibit display effect data includes the exhibit color contrast change rate Er, the exhibit light and shadow layering change coefficient Sc and the exhibit visual focus attraction change value Ca; the exhibit association data includes the exhibit style similarity coefficient Se, the visit path matching degree Pm and the exhibit description information fit degree Fd; the exhibition hall facility operation data includes the display equipment energy consumption fluctuation rate De, the display light luminous efficiency attenuation rate La and the display device mechanical transmission component wear rate Dt; the exhibition hall environment physical data includes the air suspended particle concentration gradient Ap, the ground flatness change value Gc and the spatial magnetic field intensity fluctuation amplitude Fm; the exhibition hall environment adjustment data includes the temperature adjustment system response time standard deviation Rs, the humidity adjustment change rate Hc and the air purification effect maintenance time fluctuation Pt.

3. The intelligent exhibition hall display guidance system based on big data according to claim 1 is characterized in that: The exhibit physical state data analysis node is used to establish an exhibit physical state data calculation model, import the exhibit physical state data transmitted by the exhibition hall data acquisition module into the exhibit physical state data calculation model, and obtain the exhibit physical risk coefficient value. The exhibit physical state data calculation model is specifically expressed as: ， in, Indicates the physical risk coefficient value of the exhibit calculated for the i-th time, Ec i Indicates the change in the density of the exhibit material collected for the i-th time, Es i represents the internal stress distribution gradient of the exhibit collected for the i-th time, Eg i Represents the growth rate of micro-roughness of the exhibit surface collected for the i-th time.

4. The intelligent exhibition hall display guidance system based on big data according to claim 1 is characterized in that: The exhibit display effect data analysis node is used to establish an exhibit display effect data calculation model, import the exhibit display effect data transmitted by the exhibition hall data acquisition module into the exhibit display effect data calculation model, and obtain the exhibit display effect stability coefficient value. The exhibit display effect data calculation model is specifically expressed as: ， in, represents the stability coefficient of the exhibit display effect calculated for the i-th time, Er i represents the rate of change of the color contrast of the exhibit collected for the i-th time, Er c Indicates the initial value of the exhibit color contrast, Sc i Sc represents the coefficient of variation of light and shadow layering of the exhibit collected for the i-th time, c Indicates the initial value of the light and shadow layering of the exhibit, Ca i represents the change value of the visual focus attraction of the exhibit collected for the i-th time, Ca c Indicates the initial value of the exhibit's visual focus attraction.

5. The intelligent exhibition hall display guidance system based on big data according to claim 1 is characterized in that: The exhibit-related data analysis node is used to establish an exhibit-related data calculation model, import the exhibit-related data transmitted by the exhibition hall data acquisition module into the exhibit-related data calculation model, and obtain the exhibit-related coordination coefficient value. The exhibit-related data calculation model is specifically expressed as: ， in, Indicates the exhibit association coordination coefficient value calculated for the i-th time, Se i Pm represents the similarity coefficient of the exhibit style collected for the i-th time. i Indicates the matching degree of the visit path collected for the i-th time, Fd i Indicates the degree of fit of the exhibit description information collected for the i-th time.

6. The intelligent exhibition hall display guidance system based on big data according to claim 1 is characterized by: The exhibition hall facility operation data analysis node is used to establish an exhibition hall facility operation data calculation model, import the exhibition hall facility operation data transmitted by the exhibition hall data acquisition module into the exhibition hall facility operation data calculation model, and obtain the facility operation health coefficient value. The exhibition hall facility operation data calculation model is specifically expressed as: ， in, Denotes the value of the facility operation health coefficient calculated for the i-th time, De i Denotes the energy consumption fluctuation rate of the display equipment collected for the i-th time, De def Indicates the normal threshold of the energy consumption fluctuation rate of the display equipment, La i Indicates the luminous efficiency attenuation rate of the display light collected for the i-th time, La def Indicates the normal threshold of the light luminous efficiency attenuation rate, Dt i represents the wear rate of the mechanical transmission parts of the display device collected for the i-th time, Dt def Indicates the normal threshold value of wear rate of mechanical transmission parts of the display device.

7. The intelligent exhibition hall display guidance system based on big data according to claim 1 is characterized by: The exhibition hall environment physical data analysis node is used to establish an exhibition hall environment physical data calculation model, import the exhibition hall environment physical data transmitted by the exhibition hall data acquisition module into the exhibition hall environment physical data calculation model, and obtain the environmental physical stability coefficient value. The exhibition hall environment physical data calculation model is specifically expressed as: ， in, represents the environmental physical stability coefficient value calculated for the i-th time, Ap i represents the concentration gradient of airborne particles collected for the i-th time, Ap def Gc represents the safety threshold of air suspended particle concentration gradient. i Gc represents the change in ground flatness collected for the i-th time. def Indicates the safety threshold of ground flatness change, Fm i Indicates the fluctuation amplitude of the spatial magnetic field intensity collected for the i-th time, Fm def Indicates the safety threshold of the fluctuation amplitude of the spatial magnetic field intensity.

8. The intelligent exhibition hall display guidance system based on big data according to claim 1 is characterized by: The exhibition hall environment adjustment data analysis node is used to establish an exhibition hall environment adjustment data calculation model, import the exhibition hall environment adjustment data transmitted by the exhibition hall data acquisition module into the exhibition hall environment adjustment data calculation model, and obtain the environment adjustment reliability coefficient value. The exhibition hall environment adjustment data calculation model is specifically expressed as: ， Among them, γ i Represents the reliability coefficient of the environmental adjustment calculated for the i-th time, Rs i表示 The standard deviation of the temperature control system response time collected for the i-th time, Hc i represents the humidity adjustment change rate collected for the i-th time, Pt i Indicates the fluctuation of the duration of air purification effect collected for the i-th time.

9. The intelligent exhibition hall display guidance system based on big data according to claim 1 is characterized by: The smart exhibition hall display guide data analysis unit is used to establish a smart exhibition hall display guide data calculation model, and import the exhibit physical risk coefficient value, exhibit display effect stability coefficient value, exhibit association coordination coefficient value, facility operation health coefficient value, environmental physical stability coefficient and environmental regulation reliability coefficient value transmitted by the exhibition hall data analysis module into the smart exhibition hall display guide data calculation model to obtain the smart exhibition hall display guide comprehensive evaluation index value. The smart exhibition hall display guide data calculation model is specifically expressed as: ， Among them, A represents the calculated comprehensive evaluation index value of the smart exhibition hall display guide, represents the physical risk coefficient value of the exhibit calculated for the i-th time, represents the stability coefficient value of the exhibit display effect calculated for the i-th time, represents the exhibit association coordination coefficient value calculated for the i-th time, represents the value of the facility operation health coefficient calculated for the i-th time, represents the minimum value of the calculated facility operation health coefficient, Indicates the maximum value of the calculated facility operation health coefficient, represents the environmental physical stability coefficient value calculated for the i-th time, represents the minimum value of the calculated environmental physical stability coefficient, Indicates the maximum value of the calculated environmental physical stability coefficient, represents the reliability coefficient value of the environmental adjustment calculated for the i-th time, represents the minimum value of the calculated environmental adjustment reliability coefficient, It indicates the maximum value of the calculated environmental adjustment reliability coefficient, i indicates starting from the i-th number, and n indicates ending at the n-th number.

10. A smart exhibition hall display guidance method based on big data, used to implement the smart exhibition hall display guidance system based on big data as described in any one of claims 1 and 9 above, characterized in that: include: S1, data batch division step: used to determine the data to be collected as target data, divide the target data into different batches in an equal time division manner, and mark them as 1, 2, ..., n in sequence; S2, exhibition hall data collection step: including an exhibit data collection unit and a facility environment data collection unit, which are used to collect target data in real time and transmit the collected data to the exhibition hall data analysis step; the exhibit data collection unit is used to collect exhibit physical state data, exhibit display effect data and exhibit related data; the facility environment data collection unit is used to collect exhibition hall facility operation data, exhibition hall environment physical data and exhibition hall environment adjustment data; S3, exhibition hall data analysis step: including an exhibit data analysis unit and a facility environment data analysis unit, which are used to analyze the data transmitted in the exhibition hall data collection step and transmit the analysis results to the comprehensive data evaluation step; the exhibit data analysis unit includes an exhibit physical state data analysis node, an exhibit display effect data analysis node and an exhibit association data analysis node; the facility environment data analysis unit includes an exhibition hall facility operation data analysis node, an exhibition hall environment physical data analysis node and an exhibition hall environment adjustment data analysis node; S4, comprehensive data evaluation step: including a smart exhibition hall display guide data analysis unit, which is used to comprehensively analyze the data transmitted by the exhibition hall data analysis step and transmit the analysis results to the real-time feedback step; S5, real-time feedback step: used to establish a preset value of the comprehensive evaluation index of the smart exhibition hall display guide, judge the value of the comprehensive evaluation index of the smart exhibition hall display guide according to the preset value of the comprehensive evaluation index of the smart exhibition hall display guide, and send a corresponding signal according to the judgment result.

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