Digital display method and system for historical and cultural heritage

Abstract

The invention relates to the technical field of digital display, and discloses a digital display method and system for historical and cultural heritage, and the method comprises the steps: obtaining the ambient light intensity of an exhibition hall; when the ambient light intensity is smaller than a preset threshold value, position information of a plurality of portable devices in a preset space range and image information collected by the portable devices are obtained, and a temporary device network is constructed according to the position information and the image information; acquiring a plurality of target cultural relic images acquired by the plurality of portable devices by using the temporary device network; and performing image recognition according to the multiple target cultural relic images to obtain a cultural relic recognition result, and determining digital information corresponding to the cultural relic and transmitting the digital information to the target portable device for display. The temporary device network is constructed through the plurality of portable devices, the cultural relic images collected at different angles are received for cultural relic recognition, and the digital information is displayed by using the portable devices, so that the cultural relic recognition accuracy and efficiency are improved, congestion or queuing caused by large audience flow is improved, and the audience experience feeling is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of digital display, and particularly to a method and system for digital display of historical and cultural heritages. Background Art

[0002] Historical and cultural heritages are an important part of human civilization. Their cultural relics, buildings, artworks, etc. play an irreplaceable role in recording and inheriting history. However, due to the long age and limitations of storage conditions, the display environments of many cultural relics often have problems such as insufficient light, limited exhibition space, and large audience mobility, resulting in visitors having difficulty obtaining a clear and detailed viewing experience. At the same time, with the development of digital technology, the demand for digital protection and display of cultural relics from all sectors of society has become increasingly strong. In particular, using portable devices (such as smartphones, tablets, etc.) to achieve interactive display, guided tours, and identification of cultural relic information has become an important trend.

[0003] Existing digital display technologies are mostly based on fixed or single-perspective acquisition methods. Usually, special photography and imaging systems, scanning devices, or AR / VR devices are deployed in the exhibition hall to obtain images of cultural relics, and then digital guided tours or augmented reality content are provided. However, these digital image acquisition devices have high requirements for light. If the light in the exhibition hall environment is insufficient, it often leads to a decline in image acquisition quality or even abnormal recognition. Although relying on fixed lighting devices can alleviate this problem to a certain extent, the deployment and maintenance costs are relatively high, and it may also affect the environment where the cultural relics are located.

[0004] In existing exhibition halls, there is a way to identify cultural relics by setting two-dimensional codes. However, when there are a large number of visitors, it may cause problems such as crowding and queuing of visitors due to a large number of visitors scanning the two-dimensional codes around the cultural relics, and it is impossible to accurately obtain relevant information of the corresponding cultural relics.

[0005] Based on the above status quo of digital display of cultural relics, how to achieve accurate identification and digital information display of cultural relics in the case of insufficient light and large audience flow has become an urgent problem to be solved in the field of digital display of cultural relics. Summary of the Invention

[0006] In view of this, the present invention provides a method and system for digital display of historical and cultural heritages to solve the problem that digital information display cannot be achieved due to inaccurate identification of cultural relics in the case of insufficient light and large audience flow.

[0007] In a first aspect, the present invention provides a method for digital display of historical and cultural heritages, the method comprising:

[0008] Responding to a display request of a target portable device, obtaining the light intensity of the exhibition hall environment;

[0009] When the ambient light intensity is less than a preset threshold, obtain the location information of multiple portable devices within a preset space range and the image information collected by each portable device, and construct a temporary device network based on the location information and the image information;

[0010] Use the temporary device network to obtain multiple target cultural relic images collected by multiple portable devices from different angles;

[0011] Perform image recognition on the multiple target cultural relic images to obtain a cultural relic recognition result;

[0012] Determine the digital information of the corresponding cultural relic according to the cultural relic recognition result, and transmit the digital information to the target portable device for display.

[0013] Further, using the temporary device network to obtain multiple target cultural relic images collected by multiple portable devices from different angles includes:

[0014] Obtain the performance parameters and current processing loads of each portable device in the temporary device network;

[0015] Select at least one portable device as a processing node based on the performance parameters and current processing loads of each portable device;

[0016] Use the processing node to receive each target cultural relic image and perform preprocessing.

[0017] Further, selecting at least one portable device as a processing node based on the performance parameters and current processing loads of each portable device includes:

[0018] Calculate the comprehensive score of each portable device according to the performance parameters and current processing loads, and sort each portable device in descending order according to the comprehensive score;

[0019] Select the portable device with the highest comprehensive score as the main processing node;

[0020] When the processing load threshold of the main processing node is less than the load requirement corresponding to the current cultural relic image processing task, select at least one portable device as an auxiliary processing node in descending order of the comprehensive score;

[0021] Allocate the cultural relic image processing task to the main processing node and the auxiliary processing node.

[0022] Further, constructing a temporary device network based on the location information and the image information includes:

[0023] Extract preliminary image features from the image information collected by each portable device;

[0024] Calculate the relative positions and pointing relationships between the portable devices according to the location information of each portable device;

[0025] Based on the preliminary image features, location information, relative positions, and pointing relationships, establish a target consistency scoring mechanism;

[0026] According to the target consistency scoring mechanism, screen out multiple portable devices that are aligned with the same cultural relic, and construct the screened multiple portable devices into a temporary device network.

[0027] Furthermore, based on the preliminary image features, location information, relative positions, and pointing relationships, establish a target consistency scoring mechanism, including:

[0028] Calculate the similarity scores of each image information based on each preliminary image feature;

[0029] Calculate the location correlation scores of each portable device based on the location information of each portable device;

[0030] Calculate the perspective consistency scores of each portable device based on the relative positions and pointing relationships;

[0031] According to the preset weight allocation principle, perform weighted combination on the similarity scores, location correlation scores, and perspective consistency scores to obtain the target consistency score;

[0032] Establish a scoring mechanism based on the target consistency score.

[0033] Furthermore, according to the target consistency scoring mechanism, screen out multiple portable devices that are aligned with the same cultural relic, including:

[0034] Obtain the target consistency scores of each portable device;

[0035] Compare the target consistency scores with the preset scoring threshold, and take the multiple portable devices with target consistency scores higher than the preset scoring threshold as candidate devices;

[0036] Calculate the spatial distances between the candidate devices;

[0037] Compare the spatial distances with the preset device - to - device distance threshold, and take the candidate devices with spatial distances less than the preset device - to - device distance threshold as the portable devices that are aligned with the same cultural relic.

[0038] Furthermore, the method further includes:

[0039] Obtain the floor plan information and cultural relic distribution information of the exhibition hall;

[0040] Based on the floor plan information and cultural relic distribution information, combined with the real - time positioning technology, divide the exhibition hall into multiple virtual communication areas;

[0041] Dynamically allocate communication frequency bands and time slices for each virtual communication area according to the cultural relic density and visitor flow in each virtual communication area;

[0042] Adjust the communication strategy of the temporary device network according to the dynamically allocated communication frequency bands and time slices for each virtual communication area.

[0043] Further, dynamically allocating communication frequency bands and time slices for each virtual communication area includes:

[0044] Obtain the cultural relic density and visitor flow index in each virtual communication area;

[0045] Based on the cultural relic density and visitor flow index, calculate the communication resource requirements for each virtual communication area;

[0046] According to the communication resource requirements, select and allocate corresponding communication frequency bands and time slices from the preset communication resource pool to each virtual communication area.

[0047] Further, adjusting the communication strategy of the temporary device network according to the dynamically allocated communication frequency bands and time slices for each virtual communication area includes:

[0048] Obtain the real-time number of visitors N(t), visitor density distribution D(x,y,t) and historical flow data H(t) in the exhibition hall;

[0049] Optimize the resource allocation of the temporary device network according to the following model:

[0050] min J(t)=α(t)*J_comm(t)+β(t)*J_resource(t)+γ*J_smooth(t)

[0051] where J(t) is the overall optimization goal at time t, α(t) is the communication efficiency weight, β(t) is the resource utilization weight, γ is the system stability weight, J_comm(t) is the communication efficiency objective function, J_resource(t) is the resource utilization objective function, and J_smooth(t) is the system stability objective function;

[0052] α(t)=exp(-λ*|N_pred(t)-N(t)|) / (exp(-λ*|N_pred(t)-N(t)|)+1)

[0053] β(t)=1-α(t)

[0054] where λ is the adjustment parameter and N_pred(t) is the predicted number of visitors;

[0055] J_comm(t)=Σ(i)w_i(t)*(1 / log(1+SINR_i(t)))

[0056] Among them, \(w_i(t)\) is the dynamic weight of area \(i\), and \(SINR_i(t)\) is the signal-to-noise ratio of area \(i\).

[0057] \(J_{resource}(t)=\sum_{i}(|S(i,t)-S_{ideal}(i,t)| / F + |P(i,t)-P_{ideal}(i,t)| / T)\)

[0058] Among them, \(S(i,t)\) is the frequency band allocation of area \(i\) at time \(t\), \(P(i,t)\) is the time slice allocation of area \(i\) at time \(t\), \(F\) is the total number of available frequency bands, \(T\) is the total length of the time slice, and \(S_{ideal}(i,t)\) and \(P_{ideal}(i,t)\) are the ideal frequency band and time slice allocation values respectively.

[0059] \(J_{smooth}(t)=\sum_{i}(|S(i,t)-S(i,t - 1)|+|P(i,t)-P(i,t - 1)|)\)

[0060] Among them, \(S(i,t - 1)\) and \(P(i,t - 1)\) are the frequency band and time slice allocation values at the previous moment respectively.

[0061] Solve this model to obtain the frequency band and time slice allocation schemes for each area.

[0062] According to the allocation scheme, dynamically adjust the frequency band and time slice allocations of each area in the temporary device network to achieve a balance among communication efficiency, resource utilization rate, and system stability.

[0063] In a second aspect, the present invention provides a digital display system for historical and cultural heritages, and the system includes:

[0064] A light intensity acquisition module, configured to acquire the exhibition hall ambient light intensity in response to a display request of a target portable device.

[0065] A temporary device network construction module, configured to, when the ambient light intensity is less than a preset threshold, acquire the position information of multiple portable devices within a preset space range and the image information collected by each portable device, and construct a temporary device network according to the position information and the image information.

[0066] A target cultural relic image acquisition module, configured to use the temporary device network to acquire multiple target cultural relic images collected by multiple portable devices from different angles.

[0067] A cultural relic identification module, configured to perform image recognition on multiple target cultural relic images to obtain a cultural relic identification result.

[0068] A digital display module, configured to determine the digital information corresponding to a cultural relic according to the cultural relic identification result, and transmit the digital information to the target portable device for display.

[0069] The digital display method and system for historical and cultural heritages provided by the present invention detect the ambient light intensity in the exhibition hall. When the light intensity is lower than a preset threshold, the position information of multiple portable devices is obtained and a temporary device network is constructed. The cultural relic images collected from different angles by the multiple portable devices are received, feature extraction and fusion are performed to obtain the cultural relic recognition result, and the corresponding digital information is sent to the corresponding portable devices for display, thereby solving the problem of cultural relic recognition and display in low-light environments. It has the advantages of realizing multi-device collaborative acquisition and recognition of cultural relic images in low-light environments. At the same time, it improves the phenomenon of audience congestion or queuing caused by scanning two-dimensional codes simultaneously when the audience flow is large, improves the accuracy and efficiency of cultural relic recognition, and enhances the viewing experience of the audience. Brief Description of the Drawings

[0070] In order to more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the following will briefly introduce the drawings required for use in the description of the specific embodiments or the prior art. Obviously, the drawings in the following description are some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0071] Figure 1 It is a schematic flowchart of the digital display method for historical and cultural heritages according to an embodiment of the present invention;

[0072] Figure 2 It is a schematic flowchart of another digital display method for historical and cultural heritages according to an embodiment of the present invention;

[0073] Figure 3 It is a block diagram of the structure of the digital display system for historical and cultural heritages according to an embodiment of the present invention. Detailed Embodiments

[0074] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the drawings in the embodiments of the present invention. Obviously, the described embodiments are some, but not all, of the embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts fall within the scope of protection of the present invention.

[0075] In the field of digital display of historical and cultural heritage, traditional methods mainly rely on fixed or single-perspective acquisition devices, such as specialized photography and videography systems, scanning devices, or AR / VR devices. These methods face serious challenges in low-light environments. Specifically, low-light conditions can lead to a decline in image acquisition quality, affecting the accuracy of cultural relic identification. At the same time, fixed devices are difficult to adapt to the exhibition hall environment with dense crowds and limited space, and cannot provide flexible and diverse viewing perspectives. These problems severely restrict the effect of digital display of historical and cultural heritage and the user experience.

[0076] In response to the challenges faced by the digital display of historical and cultural heritage in low-light environments, this application has conducted in-depth analysis and exploration.

[0077] Based on the above problems, the embodiments of the present invention provide a method for digital display of historical and cultural heritage. By constructing a temporary device network to obtain target cultural relic images from multiple different angles for identification, the accuracy of cultural relic identification and the efficiency of digital display can be improved.

[0078] First, considering the limitations of traditional fixed devices in low-light conditions, this application proposes the idea of using the portable devices of on-site audiences for collaborative acquisition, which can make full use of the advantages of multiple devices to improve the quality and diversity of image acquisition. However, how to effectively organize and manage these devices has become a key issue.

[0079] To solve this problem, this application proposes the concept of constructing a temporary device network. By obtaining the location information of participating devices, a flexible network structure is established to enable collaborative work among devices. This can not only overcome the limitations of fixed network bandwidth but also dynamically adjust the network topology according to the actual situation, improving the adaptability of the system.

[0080] After determining the construction method of the temporary network, the next challenge is how to effectively use this network for the acquisition and processing of cultural relic images. This application proposes a strategy of multi-angle acquisition and fusion processing. Through the temporary network, cultural relic images from different angles can be received, and then feature extraction and fusion are performed on these images to obtain more accurate and comprehensive cultural relic identification results.

[0081] Finally, this application designs a mechanism to send the digital information corresponding to the identification results back to the devices participating in the acquisition, which can not only achieve instant information feedback but also provide a unique interactive experience for each visitor.

[0082] According to an embodiment of the present invention, an embodiment of a method for digital display of historical and cultural heritage is provided. It should be noted that the steps shown in the flowchart of the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions. And although the logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in a different order than here.

[0083] In this embodiment, a method for digital display of historical and cultural heritage is provided, which can be used in the above computer system. Figure 1 It is a flowchart of a method for digital display of historical and cultural heritage according to an embodiment of the present invention. The process includes the following steps:

[0084] Step S101, in response to a display request of a target portable device, obtain the ambient light intensity of the exhibition hall.

[0085] Among them, the ambient light intensity refers to the illumination level in the exhibition hall, and specifically, a light sensor can be used to measure it. Among them, the portable device refers to a mobile device such as a smart phone or a tablet computer carried by the audience, and its functions are realized by installing a dedicated application program. It can also be a dedicated mobile device distributed by the exhibition hall to the audience, or the personal smart phone of the audience, which at least includes a camera, and its position information can be obtained by using technologies such as Wi-Fi, Bluetooth or GPS.

[0086] When the target audience needs to use the target portable device for cultural relic display, a display request is sent through the target portable device. In response to the display request, the light sensor detects the ambient light intensity of the exhibition hall. When it is detected that the ambient light intensity is lower than a preset threshold, the system starts this digital display method.

[0087] Step S102, when the ambient light intensity is less than the preset threshold, obtain the position information of multiple portable devices within a preset space range and the image information collected by each portable device, and construct a temporary device network according to the position information and the image information.

[0088] Among them, the light intensity being less than the preset threshold actually means insufficient light and uneven light. In the case of uneven light, a specific angle may be required to obtain better cultural relic recognition results, while incorrect recognition is likely to occur at other angles. In this regard, multiple portable devices can be used to collect cultural relic images from different angles and perform collaborative processing to improve the recognition accuracy. This not only overcomes the problem of limited performance of a single device in a low-light environment, but also provides richer multi-angle information, which is suitable for scenarios with a large number of audience visits.

[0089] Among them, the temporary device network refers to a temporary communication network constructed based on multiple portable devices, and specifically, ad hoc network or mesh network technology can be used to implement it. The location information of multiple portable devices in the exhibition hall is obtained through Wi-Fi or Bluetooth technology. Based on this location information, the system constructs a temporary device network to connect the participating portable devices.

[0090] Portable devices with a distance less than a preset value can be placed in a temporary device network according to the location information. In some further preferred embodiments, the orientation of the portable device during shooting can be obtained through a gyroscope, and portable devices with a distance less than the preset value and pointing to the same cultural relic can be placed in a temporary device network, aiming to construct different temporary device networks for different cultural relics.

[0091] Through this temporary network, the system receives cultural relic images collected by multiple portable devices from different angles. These images can be transmitted to the processing node for feature extraction and fusion processing. In the feature extraction process, computer vision algorithms such as edge detection and texture analysis are used to extract information that can characterize the features of the cultural relic from the images. Then, these feature information are fused and processed to generate a more comprehensive and accurate representation of the cultural relic.

[0092] Step S103, use the temporary device network to obtain multiple target cultural relic images collected by multiple portable devices from different angles.

[0093] Among them, the target cultural relic image refers to the photo of the target cultural relic taken by the portable device, and specifically, the built-in camera of the device can be used to collect it.

[0094] Step S104, perform image recognition based on multiple target cultural relic images to obtain the cultural relic recognition result.

[0095] Among them, the cultural relic recognition result refers to the cultural relic identity information obtained through image processing, and specifically, machine learning or deep learning models can be used to achieve recognition.

[0096] Step S105, determine the digital information corresponding to the cultural relic according to the cultural relic recognition result, and transmit the digital information to the target portable device for display.

[0097] Among them, the digital information refers to data such as detailed introductions and historical backgrounds related to the cultural relic, and specifically, multimedia forms such as text, pictures, audio, or video can be used to present it.

[0098] The information display of cultural relics can be achieved through portable devices. The problem of cultural relic display in low-light environments is solved by using multi-device collaborative image recognition technology. The cultural relic recognition results include the specific name and unique number of the cultural relic. Each number corresponds to pre-prepared digital information, such as 3D models, detailed introductions, etc. When the audience aims the device at a certain cultural relic, the system will identify the cultural relic and then retrieve the corresponding digital information according to its number for display on the portable device.

[0099] In practical applications, it is also possible to only send the identity information of the cultural relics to the participating portable devices. The participating portable devices are installed with corresponding software through software installation. The corresponding software has built-in detailed introductions, historical backgrounds, etc. of the cultural relics. The corresponding information can be called for display through the identity information of the cultural relics.

[0100] The advantages of this working method are as follows: By utilizing the collaborative work of multiple devices, the system can obtain more angular and higher-quality images of cultural relics in low-light environments. The fusion processing of multi-angle images improves the accuracy of recognition. At the same time, directly sending the recognition results to the devices of the participants realizes personalized and instant display, enhancing the user experience.

[0101] In the existing display methods, the cultural relics are identified by setting two-dimensional codes. However, the method of setting two-dimensional codes is not suitable for scenarios with a large number of visitors. This embodiment effectively avoids the congestion and queuing problems that may be caused by placing two-dimensional codes around the cultural relics and can provide a more natural and immersive visiting experience.

[0102] This embodiment uses a temporary device network to achieve multi-device collaborative work, overcoming the limitations of traditional fixed devices in low-light environments. Through multi-angle image acquisition and fusion processing, the accuracy of cultural relic recognition and the display effect are improved. This method has the advantages of high flexibility, low cost, and easy implementation, providing a new technical path for the digital display of historical and cultural heritage.

[0103] In this embodiment, a method for digital display of historical and cultural heritage is provided, which can be used in the above computer system. Figure 2 It is a flowchart of the method for digital display of historical and cultural heritage according to an embodiment of the present invention, as Figure 2 shown. The process includes the following steps:

[0104] Step S201, in response to a display request from a target portable device, obtain the ambient light intensity of the exhibition hall. For details, please refer to Figure 1 step S101 of the embodiment shown here, which will not be elaborated here.

[0105] Step S202: When the ambient light intensity is less than the preset threshold, obtain the location information of multiple portable devices within a preset space range and the image information collected by each portable device, and construct a temporary device network based on the location information and the image information.

[0106] Specifically, constructing the temporary device network based on the location information and the image information in the above step S202 includes:

[0107] Step S2021: Extract preliminary image features from the image information collected by each portable device.

[0108] Among them, the image information can be collected in real time through the device's camera, and the spatial location information can be obtained using GPS, Wi-Fi positioning, or indoor positioning technology. Feature extraction refers to extracting information that can characterize the features of cultural relics from the image, and specifically, computer vision algorithms such as edge detection and texture analysis can be used to achieve this. For example, Scale-invariant feature transform (SIFT), Speeded Up Robust Features (SURF), or deep learning feature extraction methods. These algorithms can extract features such as key points, textures, and color distributions in the image, providing a basis for subsequent target consistency evaluation.

[0109] Step S2022: Calculate the relative positions and pointing relationships between each portable device according to the location information of each portable device.

[0110] Obtaining the image data and spatial location information of the portable device is the basic data for constructing the temporary device network and provides necessary information for subsequent analysis.

[0111] Triangulation or other spatial positioning algorithms can be used to calculate information such as the distances and angles between devices. At the same time, combined with the gyroscope data of the device, the orientation of each device can be determined, thereby inferring whether they are aligned with the same target.

[0112] Step S2023: Establish a target consistency scoring mechanism based on the preliminary image features, location information, relative positions, and pointing relationships.

[0113] A weighted scoring model can be adopted, comprehensively considering the similarity of image features, the correlation of device positions, and the consistency of perspectives. For example, the following scoring formula can be set:

[0114] Consistency score = w1 * Image similarity + w2 * Position correlation + w3 * Perspective consistency

[0115] Among them, w1, w2, and w3 are weight coefficients, which can be adjusted according to the actual application scenario. The image similarity can be calculated by a feature matching algorithm, the position correlation can be measured based on the reciprocal of the distance between devices, and the viewing angle consistency can be represented by the cosine value of the included angle of the device orientations.

[0116] In some optional implementation manners, step S2023 includes:

[0117] Step a1, calculating a similarity score for each image information based on each preliminary image feature.

[0118] A variety of image feature extraction and comparison algorithms can be adopted. For example, the SIFT (Scale-Invariant Feature Transform) or SURF (Speeded-Up Robust Features) algorithm can be used to extract image feature points, and then the image similarity can be calculated through feature point matching. Another method is to use a deep learning model, such as a convolutional neural network, to extract high-level features of the image, and then calculate the cosine similarity between the feature vectors.

[0119] Step a2, calculating a position correlation score for each portable device based on the position information of each portable device.

[0120] The GPS positioning information of the device or indoor positioning technology can be utilized to obtain the spatial coordinates of the device. Then, the Euclidean distance or Manhattan distance between the devices can be calculated, and the distance can be converted into a correlation score. For example, the Gaussian kernel function can be used to map the distance to a correlation score between 0 and 1.

[0121] Step a3, calculating a viewing angle consistency score for each portable device based on the relative position and pointing relationship.

[0122] The gyroscope and accelerometer data of the device can be utilized to obtain the orientation information of the device. By calculating the included angle between the device orientation vectors, it can be evaluated whether the devices are aligned with the same target. Additionally, the relative position of the devices can be considered, and the included angle between the line connecting the devices and their respective orientations can be calculated to further evaluate the viewing angle consistency.

[0123] Step a4, according to the preset weight allocation principle, performing a weighted combination of the similarity score, the position correlation score, and the viewing angle consistency score to obtain a target consistency score.

[0124] The initial weights can be set through expert experience, and then machine learning algorithms (such as genetic algorithms or particle swarm optimization) can be used to optimize the weights. Another method is to use adaptive weights, and the weights are dynamically adjusted according to different scenarios and cultural relic characteristics.

[0125] Step a5, establishing a scoring mechanism based on the target consistency score.

[0126] A scoring threshold can be set, and device groups with scores higher than the threshold are considered to be aligned with the same cultural relic. A clustering algorithm such as K-means or DBSCAN can also be introduced to cluster devices with similar scores to identify device groups for multiple different cultural relics.

[0127] The image similarity score provides content-based matching information, while the position correlation and viewing angle consistency scores provide spatial and orientation constraints. By integrating this information, the present application can more accurately determine whether devices are aligned with the same cultural relic, effectively solving the misjudgment problem that may be caused by a single feature.

[0128] For example, when two devices photograph similar but different cultural relics, relying solely on image similarity may result in misjudgment. However, by introducing position and viewing angle information, this situation can be effectively distinguished. Similarly, when multiple devices are close in position but photograph different cultural relics, the viewing angle consistency score can help distinguish them.

[0129] As a preferred implementation manner, the target consistency scoring mechanism of the present application can be implemented according to the following steps:

[0130] First, for the image similarity score, a pre-trained deep learning model (such as ResNet50) can be used to extract image features. Specifically, each image is input into the model to obtain the feature vector of the penultimate layer (assuming the dimension is 2048). Then, the cosine similarity between the two feature vectors is calculated to obtain a similarity score ranging from -1 to 1. Finally, the score is normalized to the range of 0-1.

[0131] Second, for the position correlation score, assume that the three-dimensional coordinates (x, y, z) of the device are obtained using indoor positioning technology. The Euclidean distance d between the two devices is calculated, and then the Gaussian kernel function is used to convert the distance into a correlation score: score = exp(-d^2 / (2*sigma^2)), where sigma is an adjustable parameter that controls the influence degree of the distance on the score.

[0132] Third, for the viewing angle consistency score, the orientation vector (dx, dy, dz) of the device is obtained using the orientation sensor data of the device. The included angle a between the two device orientation vectors is calculated, and then the cosine function is used to convert the angle into a consistency score: score = (cos(a)+1) / 2, so that the score can be mapped to the range of 0-1.

[0133] Then, initial weights for the three scores are set, for example, w1 = 0.4, w2 = 0.3, w3 = 0.3.

[0134] Finally, a target consistency scoring threshold is set, for example, 0.8. When the final score between two devices is higher than this threshold, it is considered that they are aligned with the same cultural relic.

[0135] Through this implementation manner, the present application can effectively integrate multi-dimensional information, accurately screen out portable devices that are aligned with the same cultural relic, and provide a reliable basis for subsequent collaborative recognition and information display.

[0136] Compared with the prior art, the method of the present application has significant advantages. Traditional methods usually rely only on a single feature, such as only using image matching or location information to determine whether the devices are aligned with the same cultural relic. This method is prone to interference in complex environments, resulting in misjudgment or missed judgment. For example, a method based only on image features may misjudge visually similar but actually different cultural relics as the same target; while a method based only on location information may not be able to distinguish adjacent different cultural relics.

[0137] In contrast, the present application establishes a multi-dimensional scoring mechanism by comprehensively utilizing image features, spatial positions, and perspective information. This method can evaluate the consistency between devices more comprehensively and accurately, significantly improving the accuracy and reliability of screening. Especially in a complex exhibition hall environment with dense crowds and numerous cultural relics, the method of the present application shows stronger robustness and adaptability.

[0138] The present application solves the problem of how to accurately screen out portable devices that are aligned with the same cultural relic by establishing a multi-dimensional scoring mechanism. This solution first calculates scores from three aspects: image features, spatial positions, and perspectives, and then weights and combines these scores through preset weights to obtain the final target consistency score. This method comprehensively considers multiple factors such as image content, device position, and shooting angle, and can more accurately determine whether different devices are aligned with the same cultural relic.

[0139] The core inventive point of this technical solution lies in organically combining image features, spatial positions, and perspective information to obtain a comprehensive scoring index through a weighted combination method. Compared with a single-dimensional judgment criterion, this method can more comprehensively reflect the consistency between devices, thereby improving the accuracy and reliability of screening. At the same time, by introducing preset weights, this solution also has a certain degree of flexibility, and can adjust the importance of different factors according to the actual application scenario to adapt to different exhibition hall environments and cultural relic characteristics.

[0140] In step S2024, according to the target consistency scoring mechanism, screen out multiple portable devices that are aligned with the same cultural relic, and construct the screened multiple portable devices into a temporary device network.

[0141] According to the target consistency scoring mechanism, portable devices aligned with the same cultural relic are screened out. A threshold can be set for this step, and only devices with a score exceeding this threshold are considered to be aligned with the same cultural relic. To further improve accuracy, a clustering algorithm can be used to group devices with similar scores, thereby identifying multiple possible cultural relic targets.

[0142] Finally, a temporary device network is constructed based on the screened portable devices. Each device can act as both a data source and a relay node. During network construction, factors such as the processing capacity and power of the devices can be considered, and the most suitable device is selected as the master node, responsible for data aggregation and task distribution.

[0143] In some alternative embodiments, the above step S2024 includes:

[0144] Step b1, obtaining the target consistency scores of each portable device.

[0145] This can be achieved by invoking the previously established scoring mechanism. The scoring mechanism may include multiple dimensions such as image similarity, location correlation, and viewing angle consistency. The weights of each dimension can be adjusted according to the actual situation to adapt to different display environments.

[0146] Step b2, comparing the target consistency scores with a preset scoring threshold, and taking multiple portable devices with target consistency scores higher than the preset scoring threshold as candidate devices.

[0147] The preset scoring threshold can be dynamically adjusted according to the actual application scenario. For example, in a gallery with dense cultural relics, a higher threshold (such as 0.8) can be set to improve the screening accuracy; in a gallery with sparse cultural relics, the threshold can be appropriately lowered (such as 0.6) to increase the number of candidate devices. The setting of the threshold directly affects the result of the preliminary screening and plays a key role in the entire screening process.

[0148] Step b3, calculating the spatial distances between the candidate devices.

[0149] This can be achieved by using the GPS positioning information of the devices or indoor positioning technology. When calculating the distance, three-dimensional space can be considered, especially in multi-story galleries, to improve the positioning accuracy.

[0150] Step b4, comparing the spatial distances with a preset inter-device distance threshold, and taking the candidate devices with spatial distances less than the preset inter-device distance threshold as portable devices aligned with the same cultural relic.

[0151] The preset device - to - device distance threshold can be determined according to the size of the exhibition hall and the distribution of cultural relics. For example, it can be set to 2 meters in a small exhibition hall and 5 meters in a large exhibition hall. The setting of this parameter needs to consider the size of the cultural relics and the display method to ensure that the selected devices are indeed aligned with the same cultural relic.

[0152] It can be achieved through simple comparison operations. A buffer zone can be set to include devices with scores close to but slightly lower than the threshold in the candidate range to increase the flexibility of screening.

[0153] The technical solution of this application realizes the accurate screening of portable devices aligned with the same cultural relic by setting thresholds and the maximum allowable distance, combined with target consistency scoring and spatial distance calculation. This solution first uses the target consistency scoring mechanism to preliminarily screen candidate devices, and then further verifies through spatial distance, effectively improving the accuracy and reliability of screening. This multi - step and multi - dimensional screening method can effectively solve the problem of accurately identifying devices aligned with the same cultural relic when constructing a temporary device network, laying a foundation for subsequent collaborative image acquisition and cultural relic identification.

[0154] A minimum device quantity threshold can be set to ensure that each cultural relic has at least a sufficient number of devices aligned to guarantee the quality of subsequent image fusion.

[0155] There are close associations and interactions among these features. For example, the target consistency scoring threshold and the maximum allowable distance between devices jointly determine the range of candidate devices. The scoring mechanism and spatial distance calculation complement each other, improving the accuracy of screening. This multi - dimensional screening method not only improves the accuracy of identifying devices aligned with the same cultural relic but also provides a reliable device group for subsequent collaborative image acquisition.

[0156] First, by setting the target consistency scoring threshold, devices that may be aligned with the same cultural relic are preliminarily screened. This step effectively reduces the number of devices that need to be further processed and improves the screening efficiency. Then, by setting the maximum allowable distance between devices, the spatial range of the devices is further limited to ensure that the selected devices are indeed around the same cultural relic.

[0157] Obtaining the target consistency scores of each portable device is the core of the entire screening process. This scoring mechanism comprehensively considers multiple factors such as image features, spatial positions, and perspectives, and can comprehensively reflect whether the devices are aligned with the same cultural relic. By comparing the scores with the preset threshold, candidate devices with a high probability of being aligned with the same cultural relic can be quickly screened out.

[0158] After the candidate devices are determined, calculating the spatial distance between the computing devices further verifies the relative positional relationship of the devices. This step effectively excludes devices that, although having a high score, are actually far apart in terms of their physical positions, improving the accuracy of the screening. Finally, the group of candidate devices with a spatial distance less than the maximum allowable distance is determined as the group of devices aligned with the same cultural relic, completing the entire screening process.

[0159] This method has higher accuracy and reliability compared to screening methods that rely solely on a single factor. It can effectively handle complex exhibition hall environments, such as situations where multiple similar cultural relics are displayed side by side or where cultural relics are densely distributed. At the same time, this method also has good adaptability and can be adjusted by modifying the threshold and distance parameters to suit different exhibition hall environments and cultural relic characteristics.

[0160] As a preferred implementation manner, the technical solution of the present application can be implemented as follows:

[0161] First, set the target consistency score threshold to 0.75 and the maximum allowable distance between devices to 3 meters. These parameters can be adjusted according to the size of the exhibition hall and the density of the cultural relic distribution.

[0162] Then, obtain the target consistency scores of each portable device. For example, the score of device A is 0.82, device B is 0.78, device C is 0.76, device D is 0.69, and device E is 0.88.

[0163] Next, screen out the candidate devices with scores higher than the threshold. In this example, devices A, B, C, and E are selected as candidate devices.

[0164] Calculate the spatial distances between the candidate devices. Assume the distance between device A and B is 2.5 meters, between A and C is 3.2 meters, between A and E is 1.8 meters, between B and C is 4.1 meters, between B and E is 2.2 meters, and between C and E is 3.5 meters.

[0165] Finally, determine the group of candidate devices with a spatial distance less than the maximum allowable distance as the group of devices aligned with the same cultural relic. In this example, devices A, B, and E are determined as the group of devices aligned with the same cultural relic because the distances between them are all less than 3 meters.

[0166] Through this method, the technical solution of the present application can accurately identify the group of devices aligned with the same cultural relic, providing a reliable basis for subsequent collaborative image acquisition and cultural relic identification. This method not only improves the accuracy of the screening but also can adapt to different exhibition hall environments and cultural relic distribution situations, demonstrating strong practicality and flexibility.

[0167] Compared with the prior art, the technical solution of the present application has obvious advantages. Traditional methods usually rely on a single factor for screening, such as only based on image similarity or device location, which is prone to misjudgment due to environmental factors. For example, in a museum hall with dense cultural relics, relying solely on location information may not be able to accurately distinguish adjacent cultural relics; while relying solely on image similarity may be affected by factors such as light and angle.

[0168] The technical solution of the present application effectively overcomes these limitations by comprehensively considering the target consistency score and spatial distance. The target consistency scoring mechanism synthesizes multiple dimensions such as image features, spatial location, and perspective, providing a more comprehensive basis for judgment. And the calculation and verification of spatial distance further improve the accuracy of screening. This multi-dimensional and multi-step screening method significantly improves the accuracy of device recognition for the same cultural relic, laying a solid foundation for subsequent collaborative image acquisition and cultural relic recognition.

[0169] In addition, the technical solution of the present application has strong adaptability and flexibility. By adjusting the target consistency score threshold and the maximum allowable distance, it can adapt to different museum hall environments and cultural relic distributions. This flexibility enables the method to play a role in various complex display environments, significantly enhancing its practical value.

[0170] In this embodiment, by comprehensively using image and location information and establishing a target consistency scoring mechanism, the problem of insufficient multi-view collaboration in traditional methods is effectively solved. This solution can accurately identify devices aligned with the same cultural relic, avoiding redundant acquisition and repeated calculations, and improving system efficiency. At the same time, by constructing a targeted temporary device network, a reliable communication foundation is provided for subsequent collaborative image acquisition and processing, effectively overcoming the limitations of traditional fixed networks in dynamic environments.

[0171] The innovation of this technical solution lies in combining image feature analysis with spatial location information. By establishing a target consistency scoring mechanism, intelligent screening and networking of multiple portable devices are realized. This method not only improves the accuracy and efficiency of device collaboration, but also lays a foundation for subsequent multi-angle and multi-directional cultural relic image acquisition and fusion, effectively enhancing the accuracy of cultural relic recognition and the richness of display.

[0172] As a preferred implementation, in practical applications, it is assumed that in a dimly lit museum hall, 10 portable devices (such as smartphones) are taking pictures of cultural relics. The system first obtains the image data and location information of these 10 devices. 300 feature points are extracted from each image through the SIFT algorithm, and the three-dimensional coordinates of the devices are obtained using indoor positioning technology.

[0173] Next, the system calculates the relative positions and pointing relationships between devices. For example, the distance between devices A and B is 2 meters, and the included angle between their shooting directions is 15 degrees. Based on this information, the system establishes a target consistency scoring mechanism with weights set as: w1 = 0.5, w2 = 0.3, w3 = 0.2.

[0174] Through the scoring mechanism, the system discovers that the consistency scores of 7 devices exceed the preset threshold of 0.8, and determines that these devices are aligning with the same cultural relic. The system immediately organizes these 7 devices into a temporary network and selects the device with the strongest processing power and the most sufficient power as the master node, which is responsible for coordinating data transmission and task allocation.

[0175] This method has significant advantages compared with traditional fixed network solutions. First, this solution can adaptively identify and organize devices that are aligned with the same cultural relic, avoiding unnecessary data transmission and processing, and improving system efficiency. Second, by comprehensively using image features and spatial information, the accuracy of device collaboration is improved, and it can work effectively even in an environment with insufficient light.

[0176] Compared with the prior art, the solution of this application greatly improves the accuracy and robustness of device collaboration by fusing multi-source information such as image features, spatial positions, and device orientations. Existing fixed network solutions are difficult to cope with the dynamic changes in the exhibition hall environment, while the temporary device network of this application can be flexibly adjusted according to real-time situations, improving the adaptability of the system. Through the target consistency scoring mechanism, this application can intelligently identify and organize relevant devices, avoiding resource waste and improving the overall system efficiency. To address the problem of insufficient light in the exhibition hall, this application comprehensively uses multiple information sources, reducing the dependence on the quality of a single image while ensuring the accuracy of recognition.

[0177] Step S203: Use the temporary device network to obtain multiple images of the target cultural relic collected by multiple portable devices from different angles.

[0178] Specifically, the above step S203 includes:

[0179] Step S2031: Obtain the performance parameters and current processing loads of each portable device in the temporary device network. Obtaining the performance parameters and current processing loads of each portable device in the temporary device network can be achieved in various ways. For example, in the device self-reporting method, each portable device actively reports its performance parameters such as processor model, memory size, and remaining power when joining the temporary network. Another way is through network detection, where a certain node in the network actively sends a detection request to other devices to obtain their response times and processing capabilities. The method of historical data analysis can also be used to evaluate the performance of devices based on their past task performances.

[0180] Step S2032: Select at least one portable device as a processing node based on the performance parameters and current processing load of each portable device.

[0181] Multiple algorithms can be adopted in the process of selecting a processing node. A simple method is to directly compare the processor speed and memory size of each device and select the one with the highest value as the processing node. A more complex method may consider multiple factors, such as processor speed, memory size, network bandwidth, remaining battery level, etc., and calculate a comprehensive score through weighted calculation to select the device with the highest score. A dynamic selection strategy can also be adopted to adjust the processing node in real time according to the current network load and device status.

[0182] In some specific embodiments, the communication resources and processing speed can be evaluated to determine the number of processing nodes.

[0183] Compared with the traditional method, the technical solution of this application has obvious advantages. The traditional method usually relies on fixed processing devices or cloud servers, and this method is prone to delays or interruptions when the network conditions are poor. However, in this application, by selecting the best device on-site as the processing node, not only the dependence on the external network is reduced, but also the local computing resources can be fully utilized. In addition, in the traditional method, each device usually processes its own images independently, and it is difficult to achieve multi-angle information fusion. In this application, by centrally processing the images of multiple devices, it provides convenience for subsequent multi-angle fusion recognition and helps to improve the accuracy of recognition. Finally, the solution of this application has better flexibility and scalability, and can dynamically adjust the processing strategy according to the number and performance of on-site devices, which is difficult to achieve by fixed devices or cloud services.

[0184] In some optional embodiments, step S2032 includes:

[0185] Step c1: Calculate the comprehensive score of each portable device according to the performance parameters and current processing load, and sort the portable devices in descending order according to the comprehensive score.

[0186] Obtain the current processing load of each portable device in the temporary device network. This step can be implemented in various ways. For example, the current processing load can be obtained by regularly polling indicators such as the CPU usage rate, memory occupancy, and network bandwidth of each device. Another way is to let each device actively report its current load status. The frequency of obtaining the processing load can be adjusted according to the network scale and the complexity of the processing task to balance real-time performance and communication overhead.

[0187] Next, this application calculates the comprehensive scores of each portable device based on performance parameters and the current processing load. The performance parameters may include hardware metrics such as CPU speed, memory capacity, GPU capabilities, etc., as well as runtime states such as the remaining battery power and network connection quality. The comprehensive score can be calculated using the method of weighted summation. For example:

[0188] Comprehensive score = w1*(CPU score) + w2*(memory score) + w3*(GPU score) + w4*(1 - current load rate) + w5*(remaining battery ratio) + w6*(network quality score)

[0189] Among them, w1 to w6 are the weights of each index and can be adjusted according to specific application scenarios. This scoring mechanism can comprehensively consider the static performance and dynamic states of the device, providing a basis for selecting the most suitable processing node.

[0190] Step c2, select the portable device with the highest comprehensive score as the main processing node.

[0191] Based on the calculated comprehensive scores, this application selects the portable device with the highest comprehensive score as the main processing node. The main processing node will undertake most of the tasks of receiving and preprocessing cultural relic images. Selecting the device with the highest comprehensive score can ensure that the device with the best performance and the lightest load in the network is fully utilized, thereby improving the overall processing efficiency.

[0192] Step c3, when the processing load threshold of the main processing node is less than the load requirement corresponding to the current cultural relic image processing task, select at least one portable device as an auxiliary processing node in descending order of comprehensive scores.

[0193] Considering the complexity and dynamics of cultural relic image processing tasks, a single main processing node may not be able to meet all processing requirements. Therefore, this application introduces the concept of an auxiliary processing node. When the processing capacity of the main processing node is insufficient to meet the current cultural relic image processing requirements, select the portable device with the second highest comprehensive score as the auxiliary processing node. This mechanism can provide additional computing resources when the processing demand suddenly increases, effectively preventing the main processing node from being overloaded and ensuring the continuity and stability of the processing tasks.

[0194] Determining whether the processing capacity of the main processing node is insufficient can be achieved in various ways. For example, a load threshold can be set, and when the CPU usage rate or memory occupancy of the main processing node exceeds this threshold, the selection of the auxiliary processing node is triggered. Another method is to monitor the length of the task queue, and when the number of tasks to be processed exceeds a preset value, the auxiliary processing node is enabled.

[0195] Step c4, allocate the cultural relic image processing tasks to the main processing node and the auxiliary processing nodes.

[0196] Assign the cultural relic image processing tasks to the main processing node and the auxiliary processing nodes. The task assignment strategy can be dynamically adjusted according to the processing capabilities and current loads of each node. For example, algorithms such as weighted round-robin or shortest queue first can be used to achieve load balancing. At the same time, considering that there may be dependencies in the cultural relic image processing tasks, the assignment strategy also needs to consider the priorities of the tasks and the data flow direction to minimize the data transmission overhead between nodes.

[0197] In this embodiment, by introducing a dynamic processing node selection mechanism based on comprehensive scoring and a collaborative working mode between the main processing node and the auxiliary processing nodes, the dynamic selection and task assignment of processing nodes in the ad-hoc device network are realized. This design can adapt to the performance differences and load changes of different devices, improving the flexibility and scalability of the system. Compared with the method of fixedly assigning processing nodes, this application can better cope with the dynamic changes in device performance and load in the ad-hoc device network, thereby improving the processing efficiency and stability of the entire system.

[0198] As a preferred implementation manner, the present application can implement the following specific steps in the ad-hoc device network:

[0199] Initialization phase: When the ad-hoc device network is established, the system broadcasts a performance parameter collection request.

[0200] Performance parameter collection: Each portable device responds to the request and reports its static parameters such as CPU frequency, number of cores, memory size, GPU model, etc.

[0201] Dynamic load monitoring: The system collects dynamic load data such as CPU usage rate, memory occupancy rate, network bandwidth usage of each device every 5 seconds.

[0202] Comprehensive score calculation: Use the following formula to calculate the comprehensive score:

[0203] Score = 0.3*(CPU score) + 0.2*(memory score) + 0.2*(GPU score) + 0.15*(1 - CPU usage rate) + 0.1*(1 - memory occupancy rate) + 0.05*(network quality score)

[0204] Main processing node selection: Select the device with the highest score as the main processing node and broadcast its IP address to other devices in the network.

[0205] Load monitoring: Continuously monitor the CPU usage rate of the main processing node and set the threshold to 80%.

[0206] Auxiliary node activation: When the CPU usage rate of the main processing node exceeds 80% for three consecutive samples, select the device with the second highest score as the auxiliary processing node.

[0207] Task allocation: The main processing node receives 80% of the image processing tasks, and the auxiliary processing node receives 20% of the tasks. After every 10 tasks are completed, the load situation is re-evaluated and the allocation ratio is adjusted.

[0208] Dynamic adjustment: The comprehensive score is recalculated every 1 minute. If a device with a higher score appears, the node role is smoothly migrated to ensure system stability.

[0209] Through this implementation, the present application can flexibly select and adjust processing nodes in a temporary device network, effectively cope with differences in device performance and load changes, and improve the efficiency and reliability of cultural relic image processing.

[0210] Compared with the prior art, the technical solution of the present application can select the optimal processing node according to the current network status through real-time scoring and dynamic selection mechanism, thereby improving the adaptability and robustness of the system. The prior art may cause high-performance devices to be idle or low-performance devices to be overloaded. The comprehensive scoring mechanism of the present application takes into account the static performance and dynamic load of the device, and can more reasonably allocate processing tasks and improve the overall resource utilization efficiency. The collaborative working mode of the main processing node and the auxiliary processing node introduced in the present application provides good scalability for the system. When the processing demand increases, auxiliary nodes can be flexibly added without redesigning the entire system architecture. Through the dynamic task allocation strategy, the present application can achieve better load balancing among multiple processing nodes, avoid single-point performance bottlenecks, and improve the overall processing capacity and response speed of the system. The present application takes into account the battery status of the device in the scoring mechanism, and can give priority to devices with sufficient power as processing nodes, which helps to extend the working time of the entire temporary device network, and is particularly suitable for cultural relics display applications in mobile scenarios.

[0211] In summary, the technical solution proposed in this application effectively solves the technical problem of selecting appropriate processing nodes in a temporary device network by introducing a dynamic processing node selection mechanism based on comprehensive scoring, as well as a collaborative working mode of the main processing node and the auxiliary processing node. This solution can adapt to differences in device performance and load changes, improve the flexibility, scalability and resource utilization efficiency of the system, and provide reliable technical support for the efficient processing and display of cultural relic images.

[0212] Step S2033: using a processing node to receive each target cultural relic image and perform pre-processing.

[0213] There are different strategies for image transmission. You can choose to transmit all images at once or in batches. To improve transmission efficiency, you can compress images before transmission, or only transmit key feature points of images. In the case of limited network bandwidth, you can use an adaptive transmission strategy to dynamically adjust the transmission quality and speed according to the network conditions.

[0214] The preprocessing can include multiple steps, such as image denoising, contrast enhancement, feature point extraction, etc. The processing node can dynamically adjust the depth and scope of preprocessing according to its own processing capabilities and the current task volume. For example, when the processing load is light, more in-depth preprocessing can be performed; when the load is heavy, only basic preprocessing can be carried out, leaving more processing tasks to subsequent steps.

[0215] First, by obtaining the performance parameters of each device, it provides a basis for selecting the most suitable processing node. Then, based on these parameters, the processing node is selected to ensure that the image processing task is assigned to the most capable device. Next, the images collected from multiple angles are transmitted to the processing node, centralizing the data and laying a foundation for subsequent processing. Finally, the processing node preprocesses the received images, which not only improves the processing efficiency but also prepares for subsequent feature extraction and fusion.

[0216] In this embodiment, by selecting a device with better performance as the processing node in the temporary device network to centrally process the cultural relic images collected by multiple devices, it solves the problem of how to efficiently receive and preprocess multi-angle cultural relic images, makes full use of the performance differences of each device in the network, and by selecting the most suitable device for centralized processing, it not only improves the efficiency of image processing but also ensures the processing quality. At the same time, through preprocessing by the processing node, the computational burden of subsequent processing can be reduced, further improving the operating efficiency of the entire system. This distributed collaborative processing method not only improves the overall performance of the system but also enhances the flexibility and scalability of the system.

[0217] As a preferred implementation manner, this embodiment can be implemented in a museum exhibition hall with multiple visitors. Suppose there are 10 visitors, each holding a smartphone, and these smartphones form a temporary device network, and a specified software program is installed on the phones. When the visitors take pictures of an ancient bronze ware, the system will first obtain the performance parameters of each phone. For example, the processor speed of phone A is 2.8 GHz and the memory is 8 GB; the processor speed of phone B is 3.0 GHz and the memory is 12 GB; the performances of other phones are different.

[0218] The system will compare these performance parameters and select phone B with the best performance as the processing node. Next, the other 9 phones will transmit the captured images of the bronze ware to phone B. To improve the transmission efficiency, each image will be compressed to about 1 MB before transmission. After receiving these images, phone B will immediately start the preprocessing work. The preprocessing includes image denoising, contrast enhancement, and preliminary feature point extraction. Since phone B has better performance, it can complete the preprocessing of all 10 images within 2 seconds.

[0219] After the preprocessing is completed, mobile phone B will transmit the processed image data back to the main server of the system for subsequent feature fusion and cultural relic identification. The entire process from image acquisition to the completion of preprocessing takes no more than 5 seconds, greatly improving the efficiency of cultural relic identification.

[0220] Selecting at least one portable device as a processing node avoids congestion caused by all portable devices simultaneously sending pictures to the main server in parallel.

[0221] Step S204: Perform image recognition based on multiple target cultural relic images to obtain the cultural relic identification result. For details, please refer to Figure 1 Step S104 of the embodiment shown, which will not be elaborated here.

[0222] Step S205: Determine the digital information of the corresponding cultural relic according to the cultural relic identification result, and transmit the digital information to the target portable device for display. For details, please refer to Figure 1 Step S105 of the embodiment shown, which will not be elaborated here.

[0223] Furthermore, the method further includes:

[0224] Step S206: Obtain the floor plan information and cultural relic distribution information of the exhibition hall.

[0225] Obtaining the floor plan of the exhibition hall and the cultural relic distribution information provides basic data for subsequent area division and resource allocation, and helps to more accurately understand the layout of the exhibition hall and the locations of cultural relics.

[0226] Laser scanning technology or 3D modeling software can be used to create a digital floor plan of the exhibition hall, and the precise locations of cultural relics can be recorded through RFID tags or QR code systems. In addition, computer vision technology can be utilized to automatically identify and locate cultural relics in the exhibition hall by analyzing the image data of the exhibition hall surveillance cameras.

[0227] Step S207: Based on the floor plan information and cultural relic distribution information, combined with real-time positioning technology, divide the exhibition hall into multiple virtual communication areas.

[0228] Combined with real-time positioning technology, dividing the exhibition hall into multiple virtual communication areas can manage communication resources more flexibly and adapt to the different demand differences in different areas.

[0229] Multiple partitioning algorithms can be adopted. One method is to use the K-means clustering algorithm to divide the exhibition hall space into several virtual areas according to the cultural relic density and the expected visitor flow path. Another method is to adopt a partitioning algorithm based on graph theory, regarding the exhibition hall as a weighted graph, where nodes represent cultural relics or key locations, and the weights of edges represent communication requirements, and area division is achieved through the minimum cut algorithm.

[0230] Step S208: Dynamically allocate communication frequency bands and time slices for each virtual communication area according to the cultural relic density and visitor flow in each virtual communication area.

[0231] Dynamically allocating communication frequency bands and time slices according to the cultural relic density and visitor flow in each area can optimize resource allocation according to actual needs and improve communication efficiency.

[0232] An adaptive resource allocation algorithm can be adopted. Specifically, a prediction model based on machine learning can be designed. This model predicts the communication requirements of each area at different time periods by analyzing historical data (such as visitor flow, stay time, cultural relic interaction frequency, etc.). Based on the prediction results, a dynamic programming algorithm is used to calculate the optimal frequency band and time slice allocation scheme. At the same time, a feedback mechanism is introduced to monitor the actual communication quality of each area in real time. If the service quality of a certain area is detected to decline, the system will adjust the resource allocation in a timely manner.

[0233] Step S209: Adjust the communication strategy of the temporary device network according to the dynamically allocated communication frequency bands and time slices for each virtual communication area.

[0234] Adjust the communication strategy of the temporary device network according to the results of dynamic allocation, so that the network structure can adapt to the changes in communication requirements in different areas of the exhibition hall.

[0235] Software-Defined Network (SDN) technology can be adopted. By deploying an SDN controller inside the exhibition hall, centralized management of the network topology and data flow can be achieved. When the resource allocation in the virtual communication area changes, the SDN controller can dynamically update the routing table and QoS policy of the network devices to ensure that the data flow can be efficiently transmitted according to the new resource allocation scheme. In addition, load balancing can also be achieved, and some communication tasks in high-traffic areas can be assigned to adjacent low-load areas for processing to improve the overall network performance.

[0236] This embodiment has a synergistic effect with the solution in the basic embodiment. By dynamically dividing the virtual communication area and adjusting the resource allocation, a more optimized communication environment is provided for the temporary device network in the basic solution. This synergistic effect enables the temporary device network to operate more stably and efficiently in a complex exhibition hall environment with dense crowds and uneven distribution of cultural relics, thereby improving the acquisition quality and transmission speed of cultural relic images, and further enhancing the accuracy of cultural relic identification and the effect of digital display.

[0237] In this embodiment, by dividing the exhibition hall into multiple virtual communication areas and dynamically allocating communication resources according to the actual situation of each area, the problems of uneven distribution of communication resources and fixed network topology structure in the exhibition hall are solved. This method can flexibly adjust the communication strategy according to the cultural relic density and the flow of visitors, effectively improving the communication efficiency and reducing resource waste. At the same time, through real-time positioning technology and dynamic resource allocation, this solution can adapt to the changes in the flow of people and the distribution of exhibits in the exhibition hall, providing a more stable and efficient communication environment for the temporary device network, thus enhancing the digital display effect of historical and cultural heritage.

[0238] In practical applications, the technical solution of this application can be implemented as follows:

[0239] First, use a high-precision three-dimensional laser scanner to scan the exhibition hall comprehensively to generate an accurate digital floor plan. At the same time, use RFID technology to equip each cultural relic with an electronic tag to record its accurate location information. These data are input into the central control system to form a digital basic model of the exhibition hall.

[0240] Next, deploy a real-time positioning system based on ultra-wideband (UWB) technology. Install UWB base stations at key positions in the exhibition hall, and each visitor's portable device is equipped with a UWB tag. The system can track the location of each device in real time, with an accuracy of up to 10 centimeters.

[0241] Based on the obtained floor plan, cultural relic distribution, and real-time positioning data, use a clustering algorithm to divide the exhibition hall into virtual communication areas. The algorithm considers cultural relic density, expected pedestrian flow paths, and communication requirements as clustering features, initially divided into 10 areas, and the division results are re-evaluated every 15 minutes.

[0242] To dynamically allocate communication resources, the system adopts a resource allocation algorithm based on deep reinforcement learning. This algorithm trains a policy network by analyzing historical data, and can predict the communication requirements within the next 5 minutes according to the current cultural relic density, number of visitors, and movement patterns in each area. Based on the prediction results, the algorithm allocates the optimal communication resource combination for each area among 20 available frequency bands (each frequency band has a bandwidth of 20 MHz) and 100 time slices (each time slice is 10 ms).

[0243] The communication strategy adjustment of the temporary device network is implemented through the SDN controller. When the resource allocation changes, the SDN controller updates the flow table of the network device to adjust the forwarding path and priority of the data packet. At the same time, a dynamic load balancing mechanism is introduced. When it is detected that the communication load in a certain area exceeds 80%, part of the data flow is automatically diverted to the adjacent low-load area.

[0244] Compared with the prior art, the technical solution of this application has the following advantages:

[0245] First, traditional exhibition hall communication systems usually adopt fixed network topologies and static resource allocation methods, making it difficult to adapt to the dynamically changing communication requirements within the exhibition hall. In contrast, this application introduces the concept of virtual communication areas and combines real-time positioning technology to achieve flexible allocation of communication resources and dynamic adjustment of network topologies, significantly improving the system's adaptability and resource utilization efficiency.

[0246] Second, resource allocation methods in existing technologies often rely on simple statistical data or fixed allocation rules and are unable to accurately capture the complex changes in the flow of people and communication requirements within the exhibition hall. This application uses advanced algorithms such as machine learning and deep reinforcement learning, which can more accurately predict and respond to the communication requirements of each area, thereby achieving more intelligent and refined resource allocation.

[0247] Finally, traditional systems usually respond slowly in terms of network policy adjustment and are difficult to handle emergencies in a timely manner. This application introduces SDN technology, which separates the network control plane and data plane, enabling more flexible and rapid network policy adjustment. It can complete communication policy adjustment within milliseconds, greatly improving the system's response speed and stability.

[0248] These innovative points enable the technical solution of this application to better solve the problems of uneven distribution of communication resources and low network efficiency within the exhibition hall, providing more reliable and efficient technical support for the digital display of historical and cultural heritage.

[0249] Specifically, the above step S208 includes:

[0250] Step S2081, obtaining the cultural relic density and visitor flow index within each virtual communication area.

[0251] The cultural relic density can be obtained by combining a pre-set cultural relic distribution map with real-time positioning technology, while the visitor flow index can be statistically obtained through the people flow detection system within the exhibition hall or the location information of portable devices.

[0252] Step S2082, calculating the communication resource requirements for each virtual communication area based on the cultural relic density and visitor flow index.

[0253] The cultural relic density weight α and the visitor flow index weight β can be set. The communication resource requirement Q can be expressed as:

[0254] Q = α * D + β * F

[0255] where D is the cultural relic density and F is the visitor flow index. The values of α and β can be adjusted according to the actual situation to adapt to the characteristics of different exhibition halls.

[0256] Selecting and allocating corresponding communication frequency bands and time slices from a preset communication resource pool can adopt a dynamic programming algorithm. First, discretize the frequency bands and time slices in the communication resource pool, and then, according to the demand in each region, use the dynamic programming method to find the optimal allocation scheme. This method can ensure the fairness and efficiency of resource allocation.

[0257] When the technical solution of this application is used in combination with the temporary device network construction method in the foregoing embodiment, better effects can be produced. The construction of the temporary device network provides a basis for the dynamic allocation of communication resources, and the dynamic resource allocation can optimize the performance of the temporary device network. For example, in a crowded area, more communication resources can be allocated to support the access of more devices and higher data transmission requirements.

[0258] Step S2083: Select and allocate corresponding communication frequency bands and time slices from a preset communication resource pool to each virtual communication region according to the communication resource demand.

[0259] It can be integrated with the intelligent management system of the exhibition hall. By real-time monitoring the cultural relic density and visitor flow in each region, the system can predict the change of resource demand in the future period of time, so as to adjust the resource allocation strategy in advance and achieve smoother and more efficient resource utilization.

[0260] As a preferred implementation manner, this application can introduce a machine learning algorithm in the resource allocation process. By collecting and analyzing historical data, the system can learn the optimal resource allocation strategy in different situations and continuously optimize it during actual operation. This adaptive method can enable the system to better cope with the dynamic changes of the exhibition hall environment.

[0261] For example, in a large historical and cultural exhibition hall, the exhibition hall is divided into 10 virtual communication regions. Each region is equipped with a cultural relic density detector and a pedestrian flow detector. The system updates the cultural relic density and visitor flow index in each region every 5 minutes.

[0262] Suppose at a certain moment, the data obtained by the system is as follows:

[0263] Region 1: Cultural relic density 0.8, visitor flow index 0.6;

[0264] Region 2: Cultural relic density 0.5, visitor flow index 0.9; ...

[0266] Region 10: Cultural relic density 0.3, visitor flow index 0.2

[0267] The system sets the cultural relic density weight α = 0.6 and the visitor flow index weight β = 0.4.

[0268] For Region 1, the calculated communication resource requirement is:

[0269] Q1 = 0.6 * 0.8 + 0.4 * 0.6 = 0.72;

[0270] Similarly, calculate the requirements for other regions.

[0271] Suppose the communication resource pool has a total of 100 frequency band units and 1000 time slice units. The system uses the dynamic programming algorithm to calculate the optimal resource allocation plan according to the requirements of each region:

[0272] Region 1: 12 frequency band units, 120 time slice units;

[0273] Region 2: 10 frequency band units, 100 time slice units; ...

[0275] Region 10: 5 frequency band units, 50 time slice units;

[0276] The system dynamically adjusts the communication resources of each region according to this allocation plan to ensure that high-demand regions obtain sufficient resources while avoiding resource waste.

[0277] Compared with the prior art, the technical solution of this application can be flexibly adjusted according to real-time situations, improving the adaptability of the system. By considering the cultural relic density and the visitor flow index, the solution of this application can more accurately evaluate the actual needs of each region, avoiding the problem of uneven resource allocation and improving the overall resource utilization efficiency. By reasonably allocating communication resources, the solution of this application can ensure better communication services in areas with dense crowds or concentrated cultural relics, thus enhancing the visitor experience. The solution of this application can be integrated with other intelligent systems, such as the exhibition hall management system, the visitor navigation system, etc., providing a good foundation for future function expansion.

[0278] This embodiment effectively solves the problem of uneven communication requirements in different regions of the exhibition hall environment by dynamically allocating communication resources. It can flexibly adjust the allocation of communication resources according to the real-time distribution of cultural relics and the situation of visitors, thereby improving the communication efficiency and user experience of the entire exhibition hall. This method is particularly suitable for large exhibition halls or scenarios with uneven distribution of cultural relics, and can significantly improve the performance and reliability of the digital display system of historical and cultural heritage.

[0279] The core inventive point of the solution is to incorporate two factors unique to the exhibition hall, namely cultural relic density and visitor flow, into the consideration of communication resource allocation, and establish a mapping relationship from these factors to specific resource requirements. This innovative design enables the communication resource allocation to better meet the actual needs of historical and cultural heritage display, and has better adaptability and efficiency compared with traditional fixed allocation or simple load balancing methods.

[0280] Furthermore, dynamically allocate communication frequency bands and time slices according to each virtual communication area, and adjust the communication strategy of the temporary device network, including:

[0281] Obtain the real-time number of visitors N(t), the visitor density distribution D(x, y, t), and the historical flow data H(t) in the exhibition hall.

[0282] Optimize the resource allocation of the temporary device network according to the following model:

[0283] min J(t) = α(t) * J_comm(t) + β(t) * J_resource(t) + γ * J_smooth(t)

[0284] where J(t) is the overall optimization objective at time t, α(t) is the communication efficiency weight, β(t) is the resource utilization weight, γ is the system stability weight, J_comm(t) is the communication efficiency objective function, J_resource(t) is the resource utilization objective function, and J_smooth(t) is the system stability objective function.

[0285] α(t) = exp(-λ * |N_pred(t) - N(t)|) / (exp(-λ * |N_pred(t) - N(t)|) + 1)

[0286] β(t) = 1 - α(t)

[0287] where λ is the adjustment parameter and N_pred(t) is the predicted number of visitors.

[0288] J_comm(t) = Σ(i) w_i(t) * (1 / log(1 + SINR_i(t)))

[0289] where w_i(t) is the dynamic weight of area i and SINR_i(t) is the signal-to-noise ratio of area i.

[0290] J_resource(t) = Σ(i) [|S(i, t) - S_ideal(i, t)| / F + |P(i, t) - P_ideal(i, t)| / T]

[0291] where S(i, t) is the frequency band allocation of area i at time t, P(i, t) is the time slice allocation of area i at time t, F is the total number of available frequency bands, T is the total length of the time slice, and S_ideal(i, t) and P_ideal(i, t) are the ideal frequency band and time slice allocation values respectively.

[0292] J_smooth(t) = Σ(i)(|S(i,t) - S(i,t-1)| + |P(i,t) - P(i,t-1)|)

[0293] Wherein, S(i,t-1) and P(i,t-1) are respectively the frequency band and time slice allocation values at the previous moment.

[0294] The model satisfies the following constraint conditions:

[0295] Σ(i)S(i,t) ≤ F / / Total frequency band constraint;

[0296] Σ(i)P(i,t) ≤ T / / Total time slice constraint;

[0297] SINR_i(t) ≥ SINR_min / / Minimum quality of service guarantee;

[0298] |S(i,t) - S(j,t)| ≥ δ / / Adjacent area frequency band interference control, i, j are adjacent areas;

[0299] |S(i,t) - S(i,t-1)| ≤ ΔS_max / / Frequency band allocation smoothing constraint;

[0300] |P(i,t) - P(i,t-1)| ≤ ΔP_max / / Time slice allocation smoothing constraint;

[0301] Solve the model to obtain the frequency band and time slice allocation schemes for each area.

[0302] According to the allocation scheme, dynamically adjust the frequency band and time slice allocations of each area in the temporary device network to achieve a balance of communication efficiency, resource utilization rate, and system stability.

[0303] By obtaining the real-time number of visitors, density distribution, and historical pedestrian flow data in the exhibition hall, a comprehensive optimization model is established to dynamically adjust the resource allocation of the temporary device network. The model includes three main objective functions: communication efficiency, resource utilization rate, and system stability. By introducing dynamic weights α(t) and β(t), the model can adaptively adjust the weights of communication efficiency and resource utilization rate according to the difference between the actual number of visitors and the predicted number.

[0304] The communication efficiency objective function considers the dynamic weights and signal-to-noise ratios of each area. The resource utilization rate objective function measures the deviation between the frequency band and time slice allocations and the ideal allocations. The system stability objective function ensures the smooth operation of the system by comparing the allocation differences at adjacent moments.

[0305] The model also sets a series of constraint conditions, including total frequency band and time slice constraints, minimum quality of service guarantee, adjacent area frequency band interference control, and allocation smoothing constraints, to ensure the rationality and feasibility of resource allocation.

[0306] By solving this optimization model, the system can obtain the frequency band and time slice allocation schemes for each area, and dynamically adjust the resource allocation in each area of the temporary device network accordingly, so as to achieve a balance among communication efficiency, resource utilization rate, and system stability.

[0307] The technical solution of this application dynamically adjusts the resource allocation of the temporary device network by establishing a comprehensive optimization model. The specific implementation process is as follows:

[0308] First, obtain the real-time number of visitors N(t), the visitor density distribution D(x, y, t), and the historical flow data H(t) in the exhibition hall. These data can be collected in real time through the sensor network, cameras, or other monitoring devices in the exhibition hall.

[0309] Next, construct an optimization model. The objective function J(t) of the model consists of three parts: communication efficiency J_comm(t), resource utilization rate J_resource(t), and system stability J_smooth(t). These three parts are adjusted by weights α(t), β(t), and γ respectively.

[0310] α(t) and β(t) are dynamic weights, and their calculation methods consider the difference between the actual number of visitors and the predicted number. When the actual number of visitors is close to the predicted value, α(t) is close to 1 and β(t) is close to 0. At this time, the model pays more attention to communication efficiency; otherwise, it pays more attention to resource utilization rate. This dynamic weight mechanism enables the system to adaptively adjust the optimization objective according to the actual situation.

[0311] The communication efficiency objective function J_comm(t) considers the dynamic weight w_i(t) and signal-to-noise ratio SINR_i(t) of each area. By minimizing this function, the overall communication quality can be improved.

[0312] The resource utilization rate objective function J_resource(t) measures the deviation between the actual frequency band and time slice allocation and the ideal allocation. Minimizing this function can make the resource allocation closer to the ideal state and improve the resource utilization efficiency.

[0313] The system stability objective function J_smooth(t) ensures the stable operation of the system by comparing the allocation differences at adjacent times, avoiding system instability caused by frequent large-scale adjustments.

[0314] The model also sets a series of constraint conditions, including total frequency band and time slice constraints, minimum quality of service guarantee, adjacent area frequency band interference control, and allocation smoothness constraints. These constraints ensure the rationality and feasibility of resource allocation.

[0315] By solving this optimization model, the frequency band and time slot allocation schemes for each region can be obtained. According to this scheme, the system dynamically adjusts the frequency band and time slot allocation in each region of the temporary device network to achieve a balance among communication efficiency, resource utilization rate, and system stability.

[0316] This dynamic resource allocation method can effectively handle complex situations in the exhibition hall environment, such as changes in the density of visitors and uneven distribution of cultural relics. By adjusting the resource allocation in real time, the system can allocate more resources in areas with dense visitors, while avoiding resource waste, thus improving the adaptability and efficiency of the system.

[0317] As a preferred implementation manner, assume that an exhibition hall is divided into 5 virtual communication regions, and the communication resource requirements of each region change over time. The system performs resource allocation optimization every 5 minutes.

[0318] First, obtain the real-time number of visitors N(t), the visitor density distribution D(x, y, t), and the historical visitor flow data H(t) through the sensor network in the exhibition hall. For example, at a certain moment t, N(t) = 500, and D(x, y, t) shows that visitors are mainly concentrated in Region 2 and Region 3.

[0319] Next, the system predicts the number of visitors N_pred(t) in the next 5 minutes. Assume N_pred(t) = 550.

[0320] Then, calculate the dynamic weights:

[0321] λ = 0.01 (preset adjustment parameter);

[0322] α(t) = exp(-0.01 * |550 - 500|) / (exp(-0.01 * |550 - 500|) + 1) ≈ 0.61;

[0323] β(t) = 1 - 0.61 = 0.39;

[0324] For each region i, calculate its dynamic weight w_i(t) and signal-to-noise ratio SINR_i(t). For example, Regions 2 and 3 may be given higher weights due to the concentration of visitors.

[0325] Assume that the total available frequency band F = 100 MHz and the total time slot length T = 1 s. The system will calculate the ideal frequency band and time slot allocations S_ideal(i, t) and P_ideal(i, t) according to the requirements of each region.

[0326] By solving the optimization model, the final resource allocation schemes S(i, t) and P(i, t) are obtained. For example:

[0327] Region 1: S(1,t) = 15 MHz, P(1,t) = 0.15 s;

[0328] Region 2: S(2,t) = 30 MHz, P(2,t) = 0.3 s;

[0329] Region 3: S(3,t) = 25 MHz, P(3,t) = 0.25 s;

[0330] Region 4: S(4,t) = 20 MHz, P(4,t) = 0.2 s;

[0331] Region 5: S(5,t) = 10 MHz, P(5,t) = 0.1 s;

[0332] Finally, the system dynamically adjusts the frequency band and time slice allocation of each region in the temporary device network according to this scheme.

[0333] This dynamic resource allocation method has significant advantages compared with the traditional static allocation method. The traditional method usually adopts a fixed resource allocation strategy and cannot adapt to the dynamic changes in the exhibition hall environment. For example, in the case of a sudden increase in the number of visitors, fixed allocation may lead to insufficient resources in some regions while resources are idle in other regions.

[0334] In this embodiment, a digital display system for historical and cultural heritage is also provided. This system is used to implement the above embodiments and preferred implementation manners, and those that have been described will not be repeated. As used hereinafter, the term "module" can be a combination of software and / or hardware that realizes a predetermined function. Although the systems described in the following embodiments are preferably implemented in software, implementation in hardware, or a combination of software and hardware is also possible and contemplated.

[0335] This embodiment provides a digital display system for historical and cultural heritage, as Figure 3 shown, including:

[0336] An ambient light intensity acquisition module 301, configured to acquire the ambient light intensity of the exhibition hall in response to a display request of a target portable device.

[0337] A temporary device network construction module 302, configured to obtain the location information of multiple portable devices and the image information collected by each portable device within a preset spatial range when the ambient light intensity is less than a preset threshold, and construct a temporary device network according to the location information and the image information.

[0338] A target cultural relic image acquisition module 303, configured to use the temporary device network to acquire multiple target cultural relic images collected by multiple portable devices from different angles.

[0339] A cultural relic identification module 304, configured to perform image recognition on multiple target cultural relic images to obtain a cultural relic identification result.

[0340] The digital display module 305 is configured to determine the digital information of the corresponding cultural relic according to the cultural relic recognition result, and transmit the digital information to the target portable device for display.

[0341] The further functional descriptions of the above-mentioned various modules and units are the same as those in the corresponding foregoing embodiments, and will not be elaborated herein.

[0342] Although the embodiments of the present invention have been described in conjunction with the accompanying drawings, those skilled in the art can make various modifications and variations without departing from the spirit and scope of the present invention, and such modifications and variations fall within the scope defined by the appended claims.

Claims

1. A method for digital display of historical and cultural heritage, characterized in that: The method comprises: In response to a display request of a target portable device, obtaining the ambient light intensity of the exhibition hall; When the ambient light intensity is less than a preset threshold, obtaining location information of multiple portable devices within a preset spatial range and image information collected by each portable device, and building a temporary device network based on the location information and image information; Using the temporary device network to obtain multiple target cultural relic images collected from different angles by multiple portable devices; Performing image recognition according to the plurality of target cultural relic images to obtain a cultural relic recognition result; The digital information of the corresponding cultural relic is determined according to the cultural relic identification result, and the digital information is transmitted to the target portable device for display.

2. The method according to claim 1, characterized in that: The temporary device network is used to obtain multiple target cultural relic images collected from different angles by multiple portable devices, including: Obtaining performance parameters and current processing load of each portable device in the temporary device network; selecting at least one portable device as a processing node based on performance parameters and current processing load of each portable device; The processing nodes are used to receive and pre-process the target cultural relic images.

3. The method according to claim 2, characterized in that Selecting at least one portable device as a processing node based on performance parameters and current processing loads of each portable device includes: Calculating a comprehensive score for each portable device according to the performance parameter and the current processing load, and sorting the portable devices in descending order according to the comprehensive score; Select the portable device with the highest comprehensive score as the main processing node; When the processing load threshold of the main processing node is less than the load requirement corresponding to the current cultural relic image processing task, at least one portable device is selected as an auxiliary processing node in descending order of comprehensive scores; The cultural relic image processing task is distributed to the main processing node and the auxiliary processing node.

4. The method according to claim 1, characterized in that: Constructing a temporary device network according to the location information and the image information, including: extracting preliminary image features from image information collected by each portable device; Calculate the relative position and orientation relationship between the portable devices according to the position information of the portable devices; Establishing a target consistency scoring mechanism based on the preliminary image features, position information, relative position and pointing relationship; According to the target consistency scoring mechanism, a plurality of portable devices aimed at the same cultural relic are screened out, and the screened out plurality of portable devices are constructed into a temporary device network.

5. The method according to claim 4, characterized in that Based on the preliminary image features, position information, relative position and pointing relationship, a target consistency scoring mechanism is established, including: Calculating a similarity score for each image information based on each preliminary image feature; Calculating a location relevance score for each portable device based on the location information of each portable device; Calculating a viewing angle consistency score for each portable device based on the relative position and orientation relationship; According to a preset weight distribution principle, the similarity score, the position correlation score, and the perspective consistency score are weighted and combined to obtain a target consistency score; A scoring mechanism is established based on the target consistency score.

6. The method according to claim 4, characterized in that According to the target consistency scoring mechanism, multiple portable devices aimed at the same artifacts are screened out, including: Obtain target consistency ratings for each portable device; comparing the target consistency score with a preset score threshold, and selecting a plurality of portable devices having target consistency scores higher than the preset score threshold as candidate devices; Calculate the spatial distance between each candidate device; The spatial distance is compared with a preset inter-device distance threshold, and candidate devices whose spatial distance is less than the preset inter-device distance threshold are used as portable devices aimed at the same cultural relic.

7. The method according to claim 1, characterized in that The method further comprises: Obtain the floor plan information of the exhibition hall and the distribution information of cultural relics; Based on the floor plan information and the cultural relics distribution information, combined with real-time positioning technology, the exhibition hall is divided into a plurality of virtual communication areas; Dynamically allocate communication frequency bands and time slices to each virtual communication area based on the density of cultural relics and visitor flows in each virtual communication area; The communication frequency band and time slice are dynamically allocated according to each virtual communication area, and the communication strategy of the temporary device network is adjusted.

8. The method according to claim 7, characterized in that Dynamically allocate communication frequency bands and time slices for each virtual communication area, including: Obtain the cultural relics density and visitor flow index in each virtual communication area; Calculating the communication resource demand of each virtual communication area based on the cultural relic density and visitor flow index; According to the communication resource demand, corresponding communication frequency bands and time slices are selected and allocated to each virtual communication area from a preset communication resource pool.

9. The method according to claim 7, characterized in that: Dynamically allocating communication frequency bands and time slices according to each virtual communication area, and adjusting the communication strategy of the temporary device network, including: Obtain the real-time number of visitors N(t), visitor density distribution D(x,y,t) and historical flow data H(t) in the exhibition hall; The resource allocation of the temporary device network is optimized according to the following model: min J(t)=α(t)*J_comm(t)+β(t)*J_resource(t)+γ*J_smooth(t) Among them, J(t) is the overall optimization goal at time t, α(t) is the communication efficiency weight, β(t) is the resource utilization weight, γ is the system stability weight, J_comm(t) is the communication efficiency objective function, J_resource(t) is the resource utilization objective function, and J_smooth(t) is the system stability objective function; α(t)=exp(-λ*|N_pred(t)-N(t)|) / (exp(-λ*|N_pred(t)-N(t)|)+1) β(t)=1-α(t) Among them, λ is the adjustment parameter, N_pred(t) is the predicted number of tourists; J_comm(t)=Σ(i)w_i(t)*(1 / log(1+SINR_i(t))) Wherein, w_i(t) is the dynamic weight of region i, and SINR_i(t) is the signal-to-noise ratio of region i; J_resource(t)=Σ(i)[|S(i,t)-S_ideal(i,t)| / F+|P(i,t)-P_ideal(i,t)| / T] Where S(i,t) is the frequency band allocation of region i at time t, P(i,t) is the time slice allocation of region i at time t, F is the total number of available frequency bands, T is the total length of the time slice, S_ideal(i,t) and P_ideal(i,t) are the ideal frequency band and time slice allocation values ​​respectively; J_smooth(t)=Σ(i)(|S(i,t)-S(i,t-1)|+|P(i,t)-P(i,t-1)|) Among them, S(i,t-1) and P(i,t-1) are the frequency band and time slice allocation values ​​at the previous moment respectively; Solve the model to obtain the frequency band and time slice allocation scheme for each area; According to the allocation scheme, the frequency band and time slice allocation of each area in the temporary device network are dynamically adjusted to achieve a balance between communication efficiency, resource utilization and system stability.

10. A digital display system for historical and cultural heritage, characterized in that: The system comprises: A light intensity acquisition module, used to acquire the ambient light intensity of the exhibition hall in response to a display request of a target portable device; A temporary device network construction module, used for obtaining the location information of multiple portable devices within a preset spatial range and the image information collected by each portable device when the ambient light intensity is less than a preset threshold, and constructing a temporary device network according to the location information and the image information; A target cultural relic image acquisition module, used to acquire multiple target cultural relic images acquired from different angles by multiple portable devices using the temporary device network; A cultural relic recognition module, used for performing image recognition based on the plurality of target cultural relic images to obtain a cultural relic recognition result; The digital display module is used to determine the digital information of the corresponding cultural relic according to the cultural relic identification result, and transmit the digital information to the target portable device for display.

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