Holographic intelligent building energy-saving operation and maintenance management system based on BIM model

Abstract

The invention discloses a holographic intelligent building energy-saving operation and maintenance management system based on a BIM model, and particularly relates to the technical field of data transmission, and the system comprises a building information model building module, a real-time information collection module, a holographic image module, an analysis module, a quality judgment module and an encryption module. The building information model establishing module is used for establishing a BIM model; the real-time information acquisition module collects data and transmits the data to the holographic image module; the holographic image module generates a dynamic holographic image; the analysis module monitors and preliminarily analyzes the data transmission quality; the quality judgment module calculates a delay kurtosis index and a packet loss probability skewness index and generates a data comprehensive quality coefficient; the encryption module is used for determining a confidentiality level and encrypting data; according to the invention, the timeliness and reliability of the data are ensured, the high expectation of a user is met, the stable and reliable service is ensured to be provided by real-time monitoring and dynamic encryption, and the balance relationship between the real-time performance and the reliability of data transmission is better established.

Description

Technical Field

[0001] The present invention relates to the technical field of data transmission, and more specifically, to a holographic intelligent building energy-saving operation and maintenance management system based on a BIM model. Background Art

[0002] BIM is the abbreviation of Building Information Modeling. It is a digital technology that provides support for the design, construction and operation of buildings by building a three-dimensional model of the building and integrating various information of the building (such as structure, materials, systems, equipment, etc.). BIM technology makes the visualization, simulation and management of building information more accurate and efficient, which helps to improve the overall efficiency and collaboration of construction projects. Holographic intelligent building energy-saving operation and maintenance management based on BIM model is an intelligent management system that combines building information modeling (BIM) and holographic technology. BIM model provides a detailed digital representation of the building, including information on structure, system and components, and holographic technology uses three-dimensional imaging technology to present this digital information in the form of realistic three-dimensional images. This combination allows building managers to observe and analyze the operating status of the building in real time and intuitively, including energy consumption and system performance, so as to carry out more accurate and efficient management and maintenance. This management method collects the operating data of various parts of the building in real time through sensors and Internet of Things technology, and combines this data with the BIM model to generate dynamic holographic images. Managers can easily identify energy waste and system failure points through these holographic images, which is conducive to taking timely measures to optimize energy use and maintenance operations. This not only improves the energy-saving effect of the building, but also significantly improves the efficiency and accuracy of operation and maintenance management, ensuring the sustainable operation of the building throughout its life cycle.

[0003] The existing technology has the following shortcomings: the accuracy of the holographic image depends on the accuracy and real-time nature of the collected data. If there are no strict security measures during data transmission and storage, there may be risks such as data leakage and hacker attacks, resulting in the theft or tampering of building operation information, threatening the safety and normal operation of the building. The generated holographic image may be inaccurate, affecting the decision-making of managers. While improving security, some real-time performance will be sacrificed. Whether security measures need to be taken and to what extent security measures need to be taken have become problems that need to be solved. Therefore, a holographic intelligent building energy-saving operation and maintenance management system based on the BIM model is proposed. Summary of the invention

[0004] To achieve the above object, the present invention provides the following technical solutions: A holographic intelligent building energy-saving operation and maintenance management system based on BIM model, including a building information model establishment module, a real-time information collection module, a holographic image module, an analysis module, a quality judgment module, and an encryption module; The building information modeling module is used to digitally represent the building and establish a building information model; The real-time information acquisition module is used to collect real-time information of each node in the model according to the building information model and transmit it to the holographic image module; The holographic image module is used to generate a dynamic building holographic image based on the real-time information and building information model transmitted by the real-time information acquisition module; The analysis module is used to monitor the real-time information transmission process of each node, and conduct preliminary analysis based on the monitoring information to determine whether there is a hidden danger of insufficient information quality in the real-time information transmission process of each node; The quality judgment module is used to perform abnormal analysis on the real-time information transmission process of the target node with the hidden danger of insufficient information quality, obtain the delay kurtosis index and the packet loss rate skewness index, and then substitute the delay kurtosis index and the packet loss rate skewness index into the pre-trained machine learning model to generate the comprehensive data quality coefficient and transmit it to the encryption module; The encryption module obtains the data transmission confidentiality level of the target node based on the type of the target node and the generated data comprehensive quality coefficient, and then performs corresponding encryption operations on it according to the preset encryption strategy and transmits it to the holographic image module again.

[0005] In a preferred embodiment, the holographic image module includes a holographic image unit, a decryption unit, and a temporary storage unit; The temporary storage unit is used to temporarily store the real-time information transmitted by the real-time information acquisition module, delete the real-time information of nodes with hidden dangers, and transmit the real-time information of nodes without hidden dangers to the holographic image unit; The decryption unit is used to decrypt the encrypted transmission data and then transmit the decrypted data to the holographic image unit; The holographic image unit is used to generate a dynamic building holographic image based on the data transmitted by the temporary storage unit, the decryption unit, and the building information model.

[0006] In a preferred embodiment, the steps of performing preliminary analysis based on monitoring information to determine whether there is a hidden danger of insufficient information quality in the real-time information transmission process of each node are as follows: Real-time monitoring and recording of the amount of data transmitted per unit time during the real-time information transmission process of each node, as well as the number of data packets transmitted through the network per unit time, to obtain the preset data volume mean standard value and standard deviation , and the preset standard value of the mean number of packets and standard deviation , set the standard range of data volume transmitted per unit time to [ , ], set the standard range of the number of packets transmitted through the network per unit time to [ , ], which will satisfy the data volume within the standard range [ , ] and the number of packets is within the standard range [ , ] is marked as a normal window, which will not satisfy the data volume within the standard range [ , ] and the number of packets is within the standard range [ , ] is marked as an abnormal window, k1 and k2 are preset proportional coefficients, k1 and k2 are both greater than zero, and each node has a preset abnormal window number threshold. When the total number of abnormal windows marked by the target node is greater than or equal to its preset abnormal window number threshold, it is judged that there is a hidden danger of insufficient information quality in its real-time information transmission process.

[0007] In a preferred embodiment, the acquisition logic of the delay kurtosis index is: Delay data within the data transmission time window are collected to obtain the delay time series X: (L1, L2, ..., Ln), where n is the total number of delay data. The delay mean of the delay time series is then calculated, and then the fourth power deviation of each data point from the delay mean is calculated and summed. The square deviation of each data point from the delay mean is calculated and summed, and finally substituted into the calculation formula of the delay kurtosis index: ; represents the delayed kurtosis index, Li represents the delayed data with sequence number i, Indicates the mean delay.

[0008] In a preferred embodiment, the logic for obtaining the packet loss rate skewness index is: Collect the packet loss rate data within the data transmission time window to obtain the packet loss rate time series Y: (P1, P2, ..., Pn), where n is the total number of packet loss rate data. Calculate the average and standard deviation of the packet loss rate data in the packet loss rate time series respectively, then calculate the cubic deviation of each data point from the mean, sum them up, and finally substitute them into the calculation formula of the packet loss rate skewness index: ; represents the packet loss rate skewness index, The average value of the packet loss rate data in the packet loss rate time series, represents the standard deviation of the packet loss rate data of the packet loss rate time series, and Pi represents the packet loss rate data with sequence number i.

[0009] In a preferred embodiment, the data transmission channel corresponding to the node is divided into high-quality channel and low-quality channel according to the generated data comprehensive quality coefficient, and the encryption module uses fuzzy reasoning to determine the data transmission confidentiality level of the target node.

[0010] In a preferred embodiment, the division logic is: obtain the data comprehensive quality coefficient corresponding to the target node and the preset quality reference threshold, compare the data comprehensive quality coefficient corresponding to the target node with the preset quality reference threshold, if the data comprehensive quality coefficient is greater than or equal to the preset quality reference threshold, then the data transmission channel corresponding to the node is divided into a high-quality channel, if the data comprehensive quality coefficient is less than the preset quality reference threshold, then the data transmission channel corresponding to the node is divided into a low-quality channel.

[0011] In a preferred embodiment, the fuzzy inference logic is: For the target node whose data transmission channel is divided into a low-quality channel, the type of the target node and the generated data comprehensive quality coefficient are obtained, the type of the target node and the generated data comprehensive quality coefficient are used as input variables, the data transmission confidentiality level of the target node is used as the output variable, the input variables are fuzzified, and the values ​​of the input variables are converted into fuzzy sets, the output variables are fuzzified, and the output variables are converted into fuzzy sets, fuzzy rules are formulated to describe the confidentiality requirements under different data type combinations, the fuzzified input variables are inferred through fuzzy rules, and the level of confidentiality required for the data transmission of the target node is determined.

[0012] In a preferred embodiment, a holographic intelligent building energy-saving operation and maintenance management system based on a BIM model also includes an adaptive optimization module, which is used to adaptively optimize various communication parameters of low-quality channels.

[0013] Technical effects and advantages of the present invention: The present invention monitors the data transmission process of each node in real time and conducts detailed analysis and optimization of nodes with hidden dangers. The system can significantly improve the quality and reliability of data transmission. The data is encrypted by the encryption module to ensure data security during transmission. The encryption strategy is dynamically adjusted according to the data transmission confidentiality level to ensure that data of different importance is properly protected. The encryption strategy is dynamically adjusted according to the comprehensive data quality coefficient and node type to balance security and transmission efficiency.

[0014] The holographic image module in the present invention generates dynamic architectural holographic images, providing intuitive visual displays, which is convenient for managers to monitor and manage the operating status of the building in real time: the dynamic holographic image reflects the operating status and data transmission status of each node of the building in real time, helping managers to quickly discover and solve problems, integrating data from different sensors and nodes, providing a comprehensive system view, and improving data readability and analysis efficiency.

[0015] The present invention helps to achieve energy-saving optimization and reduce operating costs by accurately monitoring and analyzing the energy consumption of buildings. Users can identify energy waste points in holographic images, thereby providing support for optimizing energy use strategies, which is conducive to achieving energy-saving goals and improving operation and maintenance efficiency.

[0016] The present invention significantly improves user experience by providing high-quality data transmission and real-time monitoring, ensures the timeliness and reliability of data, and meets users' high expectations. Real-time monitoring and dynamic encryption ensure rapid response to user needs, provide stable and reliable services, and better establish a balance between the real-time and reliability of data transmission. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] In order to facilitate understanding by those skilled in the art, the present invention is further described below in conjunction with the accompanying drawings; Figure 1 This is a schematic diagram of a holographic intelligent building energy-saving operation and maintenance management system based on a BIM model in the present invention.

[0018] Figure 2 It is a principle diagram of the holographic image module in the present invention. DETAILED DESCRIPTION

[0019] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0020] Reference Figure 1 and Figure 2 The following examples are obtained: Embodiment 1: A holographic intelligent building energy-saving operation and maintenance management system based on BIM model, including a building information model establishment module, a real-time information collection module, a holographic image module, an analysis module, a quality judgment module, and an encryption module; each module is communicatively connected.

[0021] The building information modeling module is used to digitally represent buildings and establish building information models; digitally represent buildings, establish detailed building information models (BIM), collect the structure, system, equipment and other related information of the building, create a detailed digital model containing all nodes, and provide a data basis for other modules; The real-time information acquisition module is used to collect real-time information of each node in the model according to the building information model and transmit it to the holographic image module; deploy sensors and Internet of Things devices to collect real-time operating data of each node of the building (such as temperature, humidity, energy consumption, etc.), and transmit the collected real-time information to the holographic image module for the generation of dynamic holographic images; The holographic image module is used to generate dynamic building holographic images based on the real-time information and building information model transmitted by the real-time information acquisition module; receive and integrate real-time information and BIM model data, generate and display dynamic holographic images of buildings, so that managers can intuitively observe the operating status of the building; The analysis module is used to monitor the real-time information transmission process of each node, and conduct preliminary analysis based on the monitoring information to determine whether there are hidden dangers of insufficient information quality in the real-time information transmission process of each node; monitor the data transmission status of each node in real time, record key indicators such as transmission delay and packet loss rate, conduct preliminary analysis, and identify possible information quality problems in the data transmission process; The quality judgment module is used to perform abnormal analysis on the real-time information transmission process of the target node with the hidden danger of insufficient information quality, obtain the delay kurtosis index and the packet loss rate skewness index, and then substitute the delay kurtosis index and the packet loss rate skewness index into the pre-trained machine learning model to generate the comprehensive data quality coefficient and transmit it to the encryption module; The encryption module obtains the data transmission confidentiality level of the target node based on the type of the target node and the generated data comprehensive quality coefficient, and then performs corresponding encryption operations on it according to the preset encryption strategy and transmits it to the holographic image module again.

[0022] The specific system process is to establish a building information model: the building information model establishment module creates a detailed BIM model. Real-time information collection: the real-time information collection module deploys sensors and IoT devices to collect data from each node of the building in real time and transmits it to the holographic image module. Generate dynamic holographic images: the holographic image module generates dynamic holographic images of the building based on the collected real-time information and BIM model data. Preliminary analysis of data transmission: the analysis module monitors the data transmission status of each node to determine whether there is a hidden danger of insufficient information quality. Abnormal analysis and quality judgment: the quality judgment module conducts in-depth analysis of the target nodes with hidden dangers, calculates the delay kurtosis index and the packet loss rate skewness index, and generates a comprehensive data quality coefficient. Determine the confidentiality level and encryption processing: the encryption module determines the data transmission confidentiality level based on the comprehensive data quality coefficient and node type, and adopts an appropriate encryption strategy to encrypt the data. Adaptive optimization: the adaptive optimization module adaptively optimizes the communication parameters of low-quality channels to ensure the stability and reliability of data transmission. Feedback to the holographic image module: the encrypted data is transmitted to the holographic image module again to ensure the dynamic update and safe display of the holographic image.

[0023] The holographic image module includes a holographic image unit, a decryption unit, and a temporary storage unit; the temporary storage unit is used to temporarily store the real-time information transmitted by the real-time information acquisition module, delete the real-time information of nodes with hidden dangers, transmit the real-time information of nodes without hidden dangers to the holographic image unit, and the real-time information of nodes divided into high-quality channels is also transmitted to the holographic image unit; the decryption unit is used to decrypt the encrypted transmission data, and then transmit the decrypted data to the holographic image unit; the holographic image unit is used to generate a dynamic building holographic image based on the data transmitted by the temporary storage unit and the decryption unit, as well as the building information model. The temporary storage unit temporarily stores the real-time information transmitted by the real-time information acquisition module, deletes the real-time information of nodes with hidden dangers according to the judgment of the analysis module, and transmits the real-time information of nodes without hidden dangers to the holographic image unit, ensuring that only data without hidden dangers is used, improving the accuracy and reliability of the holographic image, realizing the effective management and classification processing of real-time information, and ensuring the efficiency of system operation. The decryption unit decrypts the encrypted transmission data and transmits the decrypted data to the holographic image unit to ensure the security of the transmission data and prevent the data from being stolen or tampered with during the transmission process. The encrypted data is correctly decrypted and integrated with other data to ensure the integrity of the holographic image. The holographic image unit generates a dynamic building holographic image based on the data transmitted by the temporary storage unit and the decryption unit, as well as the building information model. Through the dynamic holographic image, the operating status of the building is intuitively displayed, which is convenient for managers to monitor and make decisions in real time. Data from various sources are integrated into a visual holographic image to improve data readability and analysis efficiency.

[0024] The steps for conducting a preliminary analysis based on the monitoring information to determine whether there is a hidden danger of insufficient information quality during the real-time information transmission of each node are as follows: Real-time monitoring and recording of the amount of data transmitted per unit time during the real-time information transmission process of each node, as well as the number of data packets transmitted through the network per unit time, to obtain the preset data volume mean standard value and standard deviation , and the preset standard value of the mean number of packets and standard deviation , set the standard range of data volume transmitted per unit time to [ , ], set the standard range of the number of packets transmitted through the network per unit time to [ , ], which will satisfy the data volume within the standard range [ , ] and the number of packets is within the standard range [ , ] is marked as a normal window, which will not satisfy the data volume within the standard range [ , ] and the number of packets is within the standard range [ , ] is marked as an abnormal window, k1 and k2 are both preset proportional coefficients, k1 and k2 are both greater than zero, and each node is preset with an abnormal window number threshold. When the total number of abnormal windows marked by the target node is greater than or equal to its preset abnormal window number threshold, it is judged that there is a hidden danger of insufficient information quality in its real-time information transmission process. By monitoring the real-time information transmission data volume and data packet number of each node, abnormal windows can be discovered and marked in time, which is helpful to identify bottlenecks and abnormalities in the transmission process, improve the overall information transmission quality, and ensure the stable operation of the system. Through the preset data volume and data packet number standard range, a reliable benchmark can be established for real-time information transmission, and abnormal windows that do not meet the standard range can be marked and counted. Potential problem nodes can be identified in advance to prevent the system from failing due to information quality problems, thereby improving the reliability of the system and providing a real-time monitoring and early warning mechanism. When the number of abnormal windows of a node exceeds the preset threshold, an alarm can be issued immediately to prompt the operation and maintenance personnel to check and handle it, prevent small problems from turning into major failures, and ensure the smooth operation of the system. By adjusting the preset proportional coefficients k1, k2 and the abnormal window number threshold, it can adapt to different network environments and application requirements.

[0025] The logic of obtaining the delay kurtosis index is as follows: collect the delay data within the data transmission time window to obtain the delay time series X: (L1, L2, ..., Ln), where n is the total number of delay data, and then calculate the delay mean of the delay time series. The calculation formula is: ; Then calculate the fourth power deviation of each data point from the delay mean and sum them up, which is specifically expressed as: ; and calculate the square deviation of each data point from the delay mean and sum them up, which is specifically expressed as: ; Finally, substitute it into the calculation formula of the delayed kurtosis index: ; represents the delayed kurtosis index, Li represents the delayed data with sequence number i, Indicates the mean value of delay. The delay kurtosis index is a statistical indicator used to describe the delay distribution pattern during data transmission. It measures the peak degree (kurtosis) of the delay data distribution. High kurtosis: indicates that there are more extreme values ​​in the delay data, that is, there are more extreme delays. This may indicate that there are unstable factors in the network, such as sudden congestion or temporary failures. Low kurtosis: indicates that the delay data distribution is relatively flat, there are fewer extreme delays, and the network operation is relatively stable.

[0026] The logic for obtaining the packet loss rate skewness index is: Collect the packet loss rate data within the data transmission time window to obtain the packet loss rate time series Y: (P1, P2, ..., Pn), where n is the total number of packet loss rate data. Calculate the average packet loss rate data of the packet loss rate time series respectively. The formula is: ; and standard deviation, the formula is: ; Then calculate the cubic deviation of each data point from the mean and sum them up, which is specifically expressed as: ; Finally, substitute it into the calculation formula of packet loss rate skewness index: ; represents the packet loss rate skewness index, The average value of the packet loss rate data in the packet loss rate time series, Represents the standard deviation of the packet loss rate data of the packet loss rate time series, and Pi represents the packet loss rate data with sequence number i. The packet loss rate skewness index is a statistical indicator used to describe the degree of asymmetry of the packet loss rate distribution during data transmission, and measures the skew direction and degree (skewness) of the packet loss rate data distribution. Positive skewness: indicates that the packet loss rate distribution is right-skewed, and the packet loss rate is low most of the time, but occasionally there is an extremely high packet loss rate. This may be due to temporary interference or sudden load in the network. Negative skewness: indicates that the packet loss rate distribution is left-skewed, and the packet loss rate is high most of the time, but occasionally there is an extremely low packet loss rate. This may be due to the presence of continuous high load or stable interference source in the network.

[0027] Substitute the delay kurtosis index and packet loss rate skewness index into the pre-trained machine learning model to generate the comprehensive data quality coefficient. The delay kurtosis index reflects the extreme situation of delay distribution in the data transmission channel. If the delay kurtosis index is high, it means that there are more extreme delay events in the data transmission channel, which may cause unstable network performance and affect the real-time and reliability of data transmission. The packet loss rate skewness index reflects the skewness of the packet loss rate distribution in the data transmission channel. If the packet loss rate skewness index is positive, it means that the network packet loss rate is low most of the time, but high packet loss rate events occur occasionally; if it is negative, it means that the network packet loss rate is high most of the time, and low packet loss rate events occur occasionally. The packet loss rate skewness index can help identify asymmetric packet loss problems in the network. The machine learning model is not specifically specified in the present invention. A model that can be used to generate a comprehensive data quality coefficient by comprehensive analysis through the delay kurtosis index and the packet loss rate skewness index can be used. The present invention provides a specific implementation method: for example, the delay kurtosis index and the packet loss rate skewness index are standardized respectively, and the processing method is exemplified by Z-score standardization, and then the standardized delay kurtosis index and the packet loss rate skewness index are respectively assigned a weight coefficient, and then the product of the two and their weight coefficients is calculated, and then the sum of the two product values ​​is calculated, and then divided by the sum of the squares of the two weight coefficients, that is, the data comprehensive quality coefficient is obtained. The data comprehensive quality coefficient provides a comprehensive evaluation of the overall quality of the data transmission process, which helps to quickly understand the overall health of the data transmission. High reliability: A larger data comprehensive quality coefficient indicates that the data transmission process is reliable, the packet loss rate is low, the data arrival rate is high, and the data loss during the transmission process is small. High stability: A high quality coefficient means that the delay distribution is relatively stable, the delay kurtosis index is low, there are few extreme delay events, and the transmission delay is relatively controllable. Low reliability: A smaller data comprehensive quality coefficient indicates that there is a high packet loss rate in the data transmission process, the data transmission is unreliable, and the data loss phenomenon is obvious. Low stability: A low quality factor means that the delay kurtosis exponent is high, the delay distribution is unstable, extreme delay events are frequent, and the transmission delay is unpredictable.

[0028] According to the generated data comprehensive quality coefficient, the data transmission channel corresponding to the node is divided into high-quality channel and low-quality channel. The encryption module uses fuzzy reasoning to determine the data transmission confidentiality level of the target node.

[0029] The division logic is: obtain the data comprehensive quality coefficient corresponding to the target node and the preset quality reference threshold, compare the data comprehensive quality coefficient corresponding to the target node with the preset quality reference threshold, if the data comprehensive quality coefficient is greater than or equal to the preset quality reference threshold, then the data transmission channel corresponding to the node is divided into a high-quality channel, if the data comprehensive quality coefficient is less than the preset quality reference threshold, then the data transmission channel corresponding to the node is divided into a low-quality channel.

[0030] The fuzzy reasoning logic is: for the target node whose data transmission channel is divided into a low-quality channel, the type of the target node and the generated data comprehensive quality coefficient are obtained, the type of the target node and the generated data comprehensive quality coefficient are used as input variables, the data transmission confidentiality level of the target node is used as the output variable, the input variable is fuzzified, the value of the input variable is converted into a fuzzy set, the output variable is fuzzified, the output variable is converted into a fuzzy set, fuzzy rules are formulated to describe the confidentiality requirements under different data type combinations, and the fuzzified input variables are inferred through fuzzy rules to determine the level of confidentiality required for the data transmission of the target node. An example is as follows: the type of the input variable target node is obtained: for example, server, workstation, mobile device, IoT device. The generated data comprehensive quality coefficient, the initial value range is mapped to the numerical range of 0 to 1. Input variable fuzzification: the type of the target node and the generated data comprehensive quality coefficient are fuzzified. Node type: server: high importance workstation: medium importance; mobile device: medium importance; IoT device: low importance; data comprehensive quality coefficient: low: 0 to 0.3; medium: 0.3 to 0.7; high: 0.7 to 1. Fuzzification of output variables: Fuzzify the confidentiality level of data transmission: low: 0 to 0.3; medium: 0.3 to 0.7; high: 0.7 to 1. Formulate fuzzy rules to describe the confidentiality requirements under different data type combinations. For example: if the node type is of high importance and the data comprehensive quality coefficient is high, the confidentiality level is high. If the node type is of medium importance and the data comprehensive quality coefficient is medium, the confidentiality level is medium. If the node type is of low importance and the data comprehensive quality coefficient is low, the confidentiality level is low. Fuzzy reasoning: The fuzzified input variables are reasoned through fuzzy rules to determine the level of confidentiality required for the data transmission of the target node.

[0031] According to the preset encryption strategy, the corresponding encryption operation is given as an example: Low confidentiality level: select AES-128. Medium confidentiality level: select AES-256. High confidentiality level: select multi-layer encryption, AES-256 and RSA. Generate keys first: symmetric encryption generates random keys (AES-128 or AES-256), and asymmetric encryption generates RSA key pairs. Then encrypt data: encrypt data using the selected encryption algorithm and key. In order to strengthen the encryption effect, you can also set cyclic encryption. For data with high confidentiality level, first use AES-256 to encrypt, and then use RSA to encrypt the AES key. Through encryption and corresponding verification mechanisms (such as digital signatures and hash functions), it can be ensured that the data has not been tampered with during transmission and storage. The receiver can verify the integrity of the data and confirm that the data has not been maliciously modified. During the data transmission process, encryption can prevent data from being stolen. Even if the attacker intercepts the data packet in transmission, they cannot read its content because the data is encrypted. Encryption operations can prevent man-in-the-middle attacks and ensure that data is not tampered with on the path from sender to receiver during transmission, thereby improving communication security and ensuring that the dynamic building holographic image generated by the final holographic image module has sufficient accuracy and credibility, which is beneficial for relevant personnel to formulate energy-saving operation and maintenance strategies based on the generated dynamic building holographic images and determine whether the effect of the implementation of the energy-saving operation and maintenance strategies meets the preset requirements.

[0032] Embodiment 2: Based on Embodiment 1, a holographic intelligent building energy-saving operation and maintenance management system based on a BIM model also includes an adaptive optimization module, which is used to adaptively optimize various communication parameters of low-quality channels; various communication parameters can be adaptively optimized by using preset optimization algorithms such as genetic algorithms and particle swarm optimization, in order to ensure good data transmission quality, and various communication parameters of low-quality channels are optimized to ensure the reliability and efficiency of data transmission. The genetic algorithm has strong global optimization ability: the genetic algorithm can effectively avoid falling into the local optimal solution by simulating natural selection and genetic mechanisms for global search. Adaptation to complex problems: the genetic algorithm is suitable for complex optimization problems with multiple objectives and multiple constraints, and can find the optimal solution in a larger solution space. Parallel processing: the genetic algorithm can process multiple candidate solutions in parallel to improve the optimization efficiency. The particle swarm optimization algorithm has a fast convergence speed: the particle swarm optimization algorithm uses group intelligence to quickly find the optimal solution through the interaction between individuals and the global optimal position. Simple implementation: the particle swarm optimization algorithm has fewer parameters and a simple implementation process, which is suitable for real-time optimization needs. Adaptation to dynamic changes: the particle swarm optimization algorithm can adapt to dynamic environmental changes and adjust the optimization strategy in real time.

[0033] Adaptive optimization can improve communication quality: Through adaptive optimization, communication parameters can be adjusted in real time to improve the communication quality of low-quality channels and ensure the stability and reliability of data transmission. Save energy: Optimizing communication parameters can reduce unnecessary data transmission and resource consumption, and improve the energy efficiency of the system. Improve system response speed: Adaptive optimization can promptly discover and repair communication problems, improve the response speed and overall performance of the system. Extend equipment life: By optimizing communication parameters, the equipment load is reduced, the failure rate is reduced, and the service life of the equipment is extended. Enhance user experience: Improving data transmission quality and system performance can provide a better user experience and improve user satisfaction. The user can choose the specific self-optimization method to optimize the communication parameters of the low-quality channel to ensure the reliability and efficiency of data transmission.

[0034] The above formulas are all dimensionless and numerical calculations. The formula is a formula for the most recent real situation obtained by collecting a large amount of data and performing software simulation. The preset parameters in the formula are set by technicians in this field according to actual conditions.

[0035] It should be understood that in the various embodiments of the present application, the size of the serial numbers of the above-mentioned processes does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0036] Those of ordinary skill in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0037] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0038] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

Claims

1. A holographic intelligent building energy-saving operation and maintenance management system based on BIM model, characterized in that: It includes building information model building module, real-time information collection module, holographic image module, analysis module, quality judgment module and encryption module; The building information modeling module is used to digitally represent the building and establish a building information model; The real-time information acquisition module is used to collect real-time information of each node in the model according to the building information model and transmit it to the holographic image module; The holographic image module is used to generate a dynamic building holographic image based on the real-time information and building information model transmitted by the real-time information acquisition module; The analysis module is used to monitor the real-time information transmission process of each node, and conduct preliminary analysis based on the monitoring information to determine whether there is a hidden danger of insufficient information quality in the real-time information transmission process of each node; The quality judgment module is used to perform abnormal analysis on the real-time information transmission process of the target node with the hidden danger of insufficient information quality, obtain the delay kurtosis index and the packet loss rate skewness index, and then substitute the delay kurtosis index and the packet loss rate skewness index into the pre-trained machine learning model to generate the comprehensive data quality coefficient and transmit it to the encryption module; The encryption module obtains the data transmission confidentiality level of the target node based on the type of the target node and the generated data comprehensive quality coefficient, and then performs corresponding encryption operations on it according to the preset encryption strategy and transmits it to the holographic image module again.

2. According to the BIM model-based holographic intelligent building energy-saving operation and maintenance management system according to claim 1, it is characterized in that: The holographic image module includes a holographic image unit, a decryption unit, and a temporary storage unit; The temporary storage unit is used to temporarily store the real-time information transmitted by the real-time information acquisition module, delete the real-time information of nodes with hidden dangers, and transmit the real-time information of nodes without hidden dangers to the holographic image unit; The decryption unit is used to decrypt the encrypted transmission data and then transmit the decrypted data to the holographic image unit; The holographic image unit is used to generate a dynamic building holographic image based on the data transmitted by the temporary storage unit, the decryption unit, and the building information model.

3. According to the BIM model-based holographic intelligent building energy-saving operation and maintenance management system according to claim 2, it is characterized in that: The steps for conducting a preliminary analysis based on the monitoring information to determine whether there is a hidden danger of insufficient information quality during the real-time information transmission of each node are as follows: Real-time monitoring and recording of the amount of data transmitted per unit time during the real-time information transmission process of each node, as well as the number of data packets transmitted through the network per unit time, to obtain the preset data volume mean standard value and standard deviation , and the preset standard value of the mean number of packets and standard deviation , set the standard range of data volume transmitted per unit time to [ , ], set the standard range of the number of packets transmitted through the network per unit time to [ , ], which will satisfy the data volume within the standard range [ , ] and the number of packets is within the standard range [ , ] is marked as a normal window, which will not satisfy the data volume within the standard range [ , ] and the number of packets is within the standard range [ , ] is marked as an abnormal window, k1 and k2 are preset proportional coefficients, k1 and k2 are both greater than zero, and each node has a preset abnormal window number threshold. When the total number of abnormal windows marked by the target node is greater than or equal to its preset abnormal window number threshold, it is judged that there is a hidden danger of insufficient information quality in its real-time information transmission process.

4. According to the BIM model-based holographic intelligent building energy-saving operation and maintenance management system of claim 3, it is characterized in that: The logic for obtaining the delayed kurtosis index is: Delay data within the data transmission time window are collected to obtain the delay time series X: (L1, L2, ..., Ln), where n is the total number of delay data. The delay mean of the delay time series is then calculated, and then the fourth power deviation of each data point from the delay mean is calculated and summed. The square deviation of each data point from the delay mean is calculated and summed, and finally substituted into the calculation formula of the delay kurtosis index: ; represents the delayed kurtosis index, Li represents the delayed data with sequence number i, Indicates the mean delay.

5. According to the BIM model-based holographic intelligent building energy-saving operation and maintenance management system of claim 4, it is characterized in that: The logic for obtaining the packet loss rate skewness index is: Collect the packet loss rate data within the data transmission time window to obtain the packet loss rate time series Y: (P1, P2, ..., Pn), where n is the total number of packet loss rate data. Calculate the average and standard deviation of the packet loss rate data in the packet loss rate time series respectively, then calculate the cubic deviation of each data point from the mean, sum them up, and finally substitute them into the calculation formula of the packet loss rate skewness index: ; represents the packet loss rate skewness index, The average value of the packet loss rate data in the packet loss rate time series, represents the standard deviation of the packet loss rate data of the packet loss rate time series, and Pi represents the packet loss rate data with sequence number i.

6. The holographic intelligent building energy-saving operation and maintenance management system based on the BIM model according to claim 4 is characterized in that: According to the generated data comprehensive quality coefficient, the data transmission channel corresponding to the node is divided into high-quality channel and low-quality channel. The encryption module uses fuzzy reasoning to determine the data transmission confidentiality level of the target node.

7. The holographic intelligent building energy-saving operation and maintenance management system based on the BIM model according to claim 6 is characterized in that: The division logic is: obtain the data comprehensive quality coefficient corresponding to the target node and the preset quality reference threshold, compare the data comprehensive quality coefficient corresponding to the target node with the preset quality reference threshold, if the data comprehensive quality coefficient is greater than or equal to the preset quality reference threshold, then the data transmission channel corresponding to the node is divided into a high-quality channel, if the data comprehensive quality coefficient is less than the preset quality reference threshold, then the data transmission channel corresponding to the node is divided into a low-quality channel.

8. The holographic intelligent building energy-saving operation and maintenance management system based on the BIM model according to claim 7 is characterized in that: The fuzzy reasoning logic is: For the target node whose data transmission channel is divided into a low-quality channel, the type of the target node and the generated data comprehensive quality coefficient are obtained, the type of the target node and the generated data comprehensive quality coefficient are used as input variables, the data transmission confidentiality level of the target node is used as the output variable, the input variables are fuzzified, and the values ​​of the input variables are converted into fuzzy sets, the output variables are fuzzified, and the output variables are converted into fuzzy sets, fuzzy rules are formulated to describe the confidentiality requirements under different data type combinations, the fuzzified input variables are inferred through fuzzy rules, and the level of confidentiality required for the data transmission of the target node is determined.

9. The holographic intelligent building energy-saving operation and maintenance management system based on the BIM model according to claim 7 is characterized in that: It also includes an adaptive optimization module, which is used to adaptively optimize various communication parameters of the low-quality channel.

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