BIM-based cable intelligent optimization system and method

Abstract

The invention discloses a BIM-based cable intelligent optimization system and method, the system is composed of an electrical system diagram module, a fast connection model module and a cable laying module, the electrical system diagram module can perform data interaction with an electrical BIM model, cable model basic data is imported, and the fast connection model module is connected with the cable laying module; the method comprises the following steps: identifying and reading imported electrical design drawing information, and analyzing identified electrical information data to identify abnormal data information; the quick connection model module can quickly communicate the component system based on a one-to-one connection rule in the electromechanical model; and the cable laying module can simulate the actual arrangement scene of the cables of each power distribution system in the electrical BIM model, perform comparison and analysis of various schemes according to different design requirements and laying scenes, and generate an optimal cable arrangement scheme. According to the scheme, automatic examination of an electrical system diagram, intelligent planning of a cable path, accurate calculation of a bridge filling rate, automatic export of a cable quantity list and optimization of cable arrangement are realized, and the overall quality and efficiency of building electrical construction are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of building electrical construction, and particularly to a cable intelligent optimization technology based on BIM. Background Art

[0002] In building installation engineering projects, electrical construction management is a crucial link, and cable deepening and optimization work is indispensable. However, traditional cable deepening and optimization work faces many difficulties. On the one hand, it mostly relies on pure manual operation. Staff need to manually sort out a large amount of data in electrical drawings, such as electrical box numbers, switch setting values, cable model specifications, etc. This is not only inefficient but also prone to human errors. On the other hand, due to the complexity of the electrical system, involving numerous circuits and equipment, it is difficult to comprehensively consider all aspects of factors during manual deepening, resulting in difficulties in carrying out optimization work.

[0003] Thus, when selecting cables for building installation projects, it is difficult to quickly and accurately judge the matching rationality between current transformers, switches, and cables; and in the cable laying path planning, it is impossible to efficiently find the optimal path, which is prone to cable crossing and collision, affecting the construction quality and aesthetics, and increasing the construction cost and time cost.

[0004] Therefore, how to effectively solve the problems existing in traditional cable deepening and optimization work, such as low efficiency, high error rate, and difficult optimization, and improve the overall quality and efficiency of building electrical construction, is a technical problem that urgently needs to be solved in this field. Summary of the Invention

[0005] The present invention aims to provide a cable intelligent optimization solution based on BIM, which realizes the automatic review of electrical system diagrams, intelligent planning of cable paths, accurate calculation of bridge filling rates, automatic export of cable quantity lists, and optimization of cable layout by integrating BIM technology, effectively solving the problems existing in traditional cable deepening and optimization work, such as low efficiency, high error rate, and difficult optimization, and improving the overall quality and efficiency of building electrical construction.

[0006] To achieve the above object, the present invention provides a cable intelligent optimization system based on BIM. The system includes an electrical system diagram module, a quick connection model module, and a cable laying module.

[0007] The electrical system diagram module can perform data interaction with the electrical BIM model, import basic cable model data, can identify and read the information of the imported electrical design drawings, and analyze the identified electrical information data to identify abnormal data information.

[0008] The quick connection model module can quickly connect component systems in the mechanical and electrical model based on a one-to-one connection rule.

[0009] The cable laying module can simulate the actual layout scenario of the cables in each power distribution system in the electrical BIM model, compare and analyze multiple schemes according to different design requirements and laying scenarios, and generate the optimal cable layout scheme.

[0010] In some embodiments of the present invention, the electrical system diagram module includes a data intelligent acquisition and import unit and a parameter intelligent analysis and adjustment unit.

[0011] The data intelligent acquisition and import unit can automatically read and collect the text and symbol information in the electrical design drawings, and during the acquisition process, based on the electrical industry terminology library and grammar rules, perform semantic analysis on the recognized information to identify incorrect electrical information data.

[0012] The parameter intelligent analysis and adjustment unit can perform in-depth analysis on the electrical information data collected and analyzed by the data intelligent acquisition and import unit based on machine learning algorithms to identify electrical information data with unreasonable selection types and generate corresponding adjustment suggestion information.

[0013] Furthermore, the electrical system diagram module further includes a data real-time synchronization and sharing unit, and the data real-time synchronization and sharing unit can establish a data synchronization and sharing connection with electrical systems outside the system.

[0014] In some embodiments of the present invention, the quick connection model module includes an automatic path planning and connection unit and a connection quality intelligent evaluation and maintenance unit.

[0015] The automatic path planning and connection unit can automatically plan the best connection path for the detected unconnected components based on the path search algorithm of graph theory and the artificial intelligence optimization algorithm.

[0016] The connection quality intelligent evaluation and maintenance unit can evaluate the connection point quality of the completed connected components, and automatically generate a maintenance plan for the connection points that do not meet the standards.

[0017] In some embodiments of the present invention, the cable laying module includes a virtual laying and scheme optimization unit, an intelligent construction guidance and monitoring unit, and a data real-time acquisition and feedback adjustment unit.

[0018] The virtual laying and scheme optimization unit can build a cable laying virtual environment that can simulate cable laying operations based on the BIM model, record the simulation cable laying operation process and data in real time, and use simulation analysis algorithms to evaluate various cable laying simulation schemes, and generate the optimal cable laying scheme according to the simulation results.

[0019] The intelligent construction guidance and monitoring unit can generate real-time cable laying guidance information based on the actual scenario for the construction site according to the cable laying plan generated by the virtual laying and scheme optimization unit;

[0020] The data real-time acquisition and feedback adjustment unit can, during the actual cable laying process, real-time collect the cable laying status data, and perform real-time processing and analysis on the collected data, and can automatically generate a cable laying adjustment plan for abnormal data.

[0021] To achieve the above object, the present invention provides a BIM-based intelligent cable optimization method, which includes:

[0022] Data sorting and import, import the basic cable model data through the electrical BIM model, identify and read the electrical design drawing information, and analyze the identified electrical information data to identify abnormal data information;

[0023] Connectivity check and quick connection repair, identify and select the components that have achieved system connectivity through the connectivity check, and quickly connect the unconnected components in the mechanical and electrical model based on the one-to-one connection rule;

[0024] Cable laying simulation and optimization, for the cables of each power distribution system in the electrical BIM model, simulate the actual arrangement scenario of the cables, and conduct comparative analysis of multiple schemes according to different design requirements and laying scenarios to generate the optimal cable arrangement plan.

[0025] In some embodiments of the present invention, the data sorting and import step of the method includes:

[0026] First, read and collect the text and symbol information in the electrical design drawing, and during the collection process, based on the electrical industry terminology library and grammar rules, perform semantic analysis on the identified information to identify incorrect electrical information data;

[0027] Then, conduct in-depth analysis on the read and collected electrical information data based on machine learning algorithms to identify the electrical information data with unreasonable selection, and generate corresponding adjustment suggestion information.

[0028] In some embodiments of the present invention, in the connectivity check and quick connection repair step of the method, for the detected unconnected components, based on the path search algorithm of graph theory and the artificial intelligence optimization algorithm, automatically plan the best connection path.

[0029] In some embodiments of the present invention, in the connectivity check and quick connection repair step of the method, the connection point quality of the completed components is also evaluated, and for the connection points that do not meet the standards, a maintenance plan is automatically generated.

[0030] In some embodiments of the present invention, the cable laying simulation and optimization steps of the method include:

[0031] First, based on the BIM model, a cable laying virtual environment capable of simulating cable laying operations is constructed, and the process and data of the simulated cable laying operations are recorded in real time. The simulation analysis algorithm is used to evaluate various cable laying simulation schemes, and the optimal cable laying scheme is generated according to the simulation results.

[0032] Next, based on the generated cable laying scheme, real-time guidance information for cable laying based on the actual scene is generated for the construction site.

[0033] In some embodiments of the present invention, in the cable laying simulation and optimization steps of the method, during the actual cable laying process, the cable laying state data is collected in real time, and the collected data is processed and analyzed in real time, and a cable laying adjustment scheme can be automatically generated for abnormal data.

[0034] The solution provided by the present invention has the following beneficial effects compared with the prior art:

[0035] Improve work efficiency: By checking all circuits with one key, the drawing review time is greatly shortened, and the work efficiency is improved. Functions such as automatically finding the cable path and calculating the bridge filling rate with one key avoid the complexity of manual calculation and planning, reduce human errors, and further improve the work efficiency.

[0036] Optimize construction quality: The cable layout optimization function based on the principle of avoiding cross collisions ensures the reasonable layout of cables in the bridge, reduces the situation of cable cross collisions, and improves the appearance and safety of cable laying. At the same time, accurate cable selection and parameter verification ensure the stability and reliability of the electrical system and improve the overall construction quality.

[0037] Reduce costs: The accurate cable quantity list export function makes material procurement more accurate, avoids material waste, and reduces construction costs. The optimized cable laying scheme reduces rework and adjustment during the construction process, saving time costs and labor costs.

[0038] Enhance visualization effect: The system provides clear and intuitive information for construction personnel through the visual display of the bridge filling rate, the visual presentation of the cable path, and the automatically generated cable cross-section layout diagram, facilitating their understanding and execution of construction tasks, and reducing communication costs and construction errors. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] The present invention will be further described below in conjunction with the accompanying drawings and specific embodiments.

[0040] Figure 1 It is the principle block diagram of the BIM-based cable intelligent optimization system in the present invention;

[0041] Figure 2 This is the schematic diagram of the composition of the electrical system diagram module in the present invention;

[0042] Figure 3 This is the schematic diagram of the composition of the quick connection model module in the present invention;

[0043] Figure 4 This is the schematic diagram of the composition of the cable laying module in the present invention. Detailed implementation manners

[0044] In order to make the technical means, creative features, achieved purposes and effects realized by the present invention easy to understand, the present invention will be further described below with reference to specific drawings.

[0045] In view of the difficulties faced in cable deepening and optimization in electrical construction management, the present invention forms a set of cable intelligent optimization solutions by integrating advanced BIM technology and intelligent algorithms, and can realize automatic review of electrical system diagrams, intelligent planning of cable routes, accurate calculation of bridge filling rates, automatic export of cable quantity lists, and optimization of cable layouts.

[0046] See Figure 1 , which shows a composition example of a BIM-based cable intelligent optimization system given by the present invention.

[0047] Based on the illustration, the BIM-based cable intelligent optimization system 100 mainly consists of an electrical system diagram module 110, a quick connection model module 120, and a cable laying module 130 that cooperate with each other.

[0048] The electrical system diagram module 110 in this system can specifically perform data interaction with the electrical BIM model, can import basic cable model data, and can identify and read the electrical design drawing information imported, and analyze the identified electrical information data to identify abnormal data information.

[0049] Specifically, the electrical system diagram module 110 analyzes the identified and read electrical drawing information based on the built-in review rules to achieve comprehensive checking and filling of gaps in the electrical drawing information.

[0050] As an example, the electrical system diagram module 110 can specifically automatically check whether the selection of current transformers, switches, and cables meets the design specifications and actual requirements.

[0051] As a further supplementary explanation, during the analysis and review process of the identified and read electrical drawing information by the electrical system diagram module 110, corresponding prompt information can be generated for the identified abnormal data information. The presentation form of the prompt information is not limited here and can be determined according to actual needs. For example, the abnormal data information can be marked with errors, colored, etc.

[0052] As an example, during the review process of this electrical system diagram module 110, for error situations such as the number not matching the distribution box, the corresponding cable not found for the model specification, conflicts between the main and standby circuits, etc., and abnormal situations such as one number corresponding to multiple distribution boxes, it will give a prominent prompt with a red or yellow background.

[0053] As a further supplementary explanation, this electrical system diagram module 110 can perform custom settings of parameters for the identified and read electrical drawing information, thereby realizing the verification and adjustment of electrical parameters to ensure the accuracy and rationality of the data.

[0054] As an example, this electrical system diagram module 110 can perform custom settings for parameters such as cable specifications, reference outer diameters, cable cross-sectional areas, cable weights, and construction costs in the identified electrical drawing information, and perform verification and adjustment of electrical parameters such as switch setting values, cable cross-sectional dimensions, and pipe diameters for threading, thereby ensuring the accuracy and rationality of the data.

[0055] The quick connection model module 120 in this system can quickly connect component systems in the mechanical and electrical model based on the one-to-one connection rule.

[0056] Specifically, this quick connection model module 120 can quickly connect component systems such as cable trays, cable tray fittings, and pipe conduits in the mechanical and electrical model according to the one-to-one connection rule.

[0057] The mechanical and electrical model here is a BIM model containing mechanical and electrical equipment such as electrical, water supply and drainage, and HVAC, as well as pipeline routes. The three-dimensional information and attribute data of each mechanical and electrical system component are integrated in this model.

[0058] As an example, this quick connection model module 120 can only perform a quick path connection between one cable tray fitting and one cable tray fitting or cable tray, thereby avoiding the situation of chaotic connections.

[0059] In this way, by configuring this quick connection model module 120, when it is found that the cable tray system of the same line is not connected during the connection check, the connection work can be quickly completed to ensure the integrity and continuity of the entire electrical system.

[0060] The cable laying module 130 in this system can simulate the actual layout scenario of the cables in each power distribution system of the electrical BIM model, conduct a comparative analysis of multiple schemes according to different design requirements and laying scenarios, and generate the optimal cable layout scheme.

[0061] Specifically, based on the recognition result of the electrical system diagram module 110, after completing the connectivity check and quickly connecting the component system through the quick connection model module 120, the cable laying module 130 performs corresponding operations on the cables of each power distribution system in the electrical BIM model. By simulating the actual layout scenario of the cables and comparing and analyzing multiple schemes according to different design requirements and laying scenarios, the optimal cable layout scheme is generated.

[0062] On this basis, the cable laying module 130 can also automatically calculate the cable filling during the simulation layout process, accurately calculating the filling rate of the cables in the cable tray.

[0063] As a further supplementary explanation, the cable laying module 130 can automatically generate a cable cross-section layout diagram containing key information such as the filling rate and cable loop number, providing clear and intuitive construction guidance for construction personnel.

[0064] On this basis, the present invention further clearly gives the possible specific composition schemes of each component module of the BIM-based cable intelligent optimization system 100.

[0065] Combined with Figure 2 As shown, in the specific implementation of the electrical system diagram module 110 in this system, it is mainly composed of a data intelligent acquisition and import unit 111 and a parameter intelligent analysis and adjustment unit 112 cooperating with each other.

[0066] Among them, the data intelligent acquisition and import unit 111 is used to automatically read and collect the text and symbol information in the electrical design drawings, and during the collection process, according to the electrical industry terminology library and grammar rules, perform semantic analysis on the recognized information to identify incorrect electrical information data.

[0067] Specifically, the data intelligent acquisition and import unit 111 preferably uses the optical character recognition (OCR) method and the intelligent semantic parsing algorithm to automatically read the text and symbol information in the electrical design drawings.

[0068] Furthermore, the data intelligent acquisition and import unit 111 first scans the electrical design drawings by OCR to convert the image into text characters; then, for the converted text characters, through the intelligent semantic parsing algorithm, according to the electrical industry terminology library and grammar rules, perform lexical and syntactic analysis on the text to construct a semantic tree; then, based on the constructed semantic tree, by identifying keywords and semantic relationships, judge the rationality of the electrical information, such as analyzing the grammar structure of the cable model, associated equipment information, etc., to identify incorrect data.

[0069] On this basis, for the tabular data in the drawings, the data intelligent acquisition and import unit 111 further uses a table structure recognition algorithm to accurately locate and extract key data such as electrical box numbers, switch setting values, cable model specifications, etc.

[0070] Furthermore, during the acquisition process, the data intelligent acquisition and import unit 111 performs semantic analysis on the recognized information based on the built-in electrical industry terminology library and grammar rules to correct possible recognition errors.

[0071] Accordingly, a professional library is built within the data intelligent acquisition and import unit 111. This professional library collects the terms and grammar in electrical industry standards and design manuals and builds a structured database covering various electrical terms, symbol meanings, markings, and statement specifications.

[0072] On this basis, for the recognized information, the data intelligent acquisition and import unit 111 first performs word segmentation and part-of-speech tagging. It uses a word segmentation algorithm to split the text recognized by OCR into individual words, and then tags the part of speech through a part-of-speech tagging model to clarify the word category.

[0073] Next, it performs semantic parsing and error recognition. It performs syntactic analysis on the segmented text according to grammar rules, constructs a syntactic tree to clarify the grammatical relationship, compares the words with the built-in terminology library, and regards those that do not match or have abnormal grammatical structures as errors, such as the situation where the cable model does not conform to the naming rules.

[0074] Finally, it performs context reasoning and correction. For the marked errors, it extracts the text before and after the error words and analyzes the semantic association. For example, according to the surrounding electrical equipment information, it screens the correct cable model from the terminology library; or adjusts the statement errors according to the context logic and grammar rules to complete the error correction.

[0075] As an example, when the data intelligent acquisition and import unit 111 recognizes a fuzzy cable model, it determines the correct model by comparing it with similar models in the electrical industry terminology library and making a context semantic judgment.

[0076] Furthermore, after the data intelligent acquisition and import unit 111 completes the recognition and acquisition of the text and symbol information in the electrical design drawings, it performs format standardization processing on the acquired data to ensure that the data meets the requirements of subsequent processing.

[0077] The parameter intelligent analysis and adjustment unit 112 is set to perform effective data interaction with the data intelligent acquisition and import unit 111. It can perform in-depth analysis on the electrical information data collected and analyzed by the data intelligent acquisition and import unit based on machine learning algorithms to identify electrical information data with unreasonable selections and generate corresponding adjustment suggestion information.

[0078] Specifically, the parameter intelligent analysis and adjustment unit 112 deeply analyzes the imported data such as cable specifications and electrical parameters by introducing a deep learning model based on machine learning algorithms. By comparing with a large amount of historical project data and industry standard specifications, it automatically identifies potential problems with unreasonable selection.

[0079] As a supplementary explanation, the parameter intelligent analysis and adjustment unit 112 specifically constructs a deep learning model, such as a neural network model. On this basis, data such as cable specifications and electrical parameters are used as inputs, and at the same time, a large amount of historical project data and industry standard specifications are imported as training data; the constructed deep learning model is trained through multiple iterations to learn the characteristics and patterns of reasonable selection.

[0080] On this basis, the trained deep learning model is further called to deeply analyze the imported data such as cable specifications and electrical parameters to identify potential problems with unreasonable selection.

[0081] As an example, the parameter intelligent analysis and adjustment unit 112 can use the deep learning model to judge whether the cable cross-sectional area meets the actual requirements according to parameters such as current and power, as well as the cable laying environment.

[0082] On this basis, the parameter intelligent analysis and adjustment unit 112 further generates corresponding intelligent adjustment suggestions for the identified problems based on big data analysis and optimization algorithms.

[0083] As a further explanation, when the parameter intelligent analysis and adjustment unit 112 generates intelligent adjustment suggestions, based on big data analysis, adjustment cases of similar unreasonable selection problems are extracted from historical project data, and screened and matched in combination with the current project parameters and actual situation; then, optimization algorithms, such as genetic algorithms, are used to optimize the selected cases under the constraints of electrical performance, cost, etc. to generate adjustment suggestions.

[0084] Furthermore, the parameter intelligent analysis and adjustment unit 112 can push the generated intelligent adjustment suggestions to the corresponding personnel. The push method here can adopt various solutions, such as voice, direct display on a visual interface, short message, etc. As an example, it is preferably to display the content of the intelligent adjustment suggestion scheme through a visual interface, so that relevant personnel can quickly and intuitively obtain the corresponding information.

[0085] On this basis, the parameter intelligent analysis and adjustment unit 112 can also receive operation instructions to adjust the corresponding parameters, such as adjusting the parameters of the electrical information data with unreasonable selection identified. The implementation scheme for the corresponding parameter adjustment is not limited here and can be determined according to actual needs.

[0086] Furthermore, when the parameter intelligent analysis and adjustment unit 112 adjusts the corresponding parameters, it can display the impact on the entire electrical system in real time after the parameter adjustment.

[0087] Specifically, the parameter intelligent analysis and adjustment unit 112 combines the basic data of the electrical system obtained by the data intelligent acquisition and import unit 111 and the analysis results of the parameter intelligent analysis and adjustment unit 112. When adjusting the parameters, through a pre-established electrical system performance calculation model, it calculates the changes in indicators such as cost and power transmission efficiency, and displays relevant data change charts or text information on the visualization interface. As an example, it can display information such as cost changes and power transmission efficiency improvement in real time, thereby assisting project personnel in making decisions.

[0088] As a further illustration, a data real-time synchronization and sharing unit 113 can also be set in the electrical system diagram module 110 of this system. This data real-time synchronization and sharing unit 113 can establish a data synchronization and sharing connection with an electrical system outside the system.

[0089] As an example, the data real-time synchronization and sharing unit 113 can specifically adopt data interface technologies such as RESTful API to establish a connection with an external electrical system, and use a message queue mechanism such as Kafka. When data changes, it encapsulates the changed data into a message and sends it to the message queue. At the same time, the external system is configured to be able to obtain messages from the queue, parse and update its own data; similarly, the data real-time synchronization and sharing unit 113 can also obtain messages from the message queue, parse and update its own data based on the same method, thereby realizing data real-time synchronization and sharing.

[0090] Accordingly, through this data real-time synchronization and sharing unit 113, the electrical system diagram module 110 can automatically synchronize changes to relevant platforms and models when the data of the current electrical system changes, such as design changes or parameter adjustments; at the same time, the data updates in other professional models related to the current electrical system can also be timely fed back to the electrical system diagram module to ensure data consistency and integrity.

[0091] As an example, the electrical system diagram module 110 establishes a data synchronization and sharing connection with the building structure model through the data real-time synchronization and sharing unit 113. In this way, if the spatial layout of a certain area in the building structure model is adjusted, the electrical system diagram module can automatically obtain the information, re-evaluate the cable laying path and equipment installation position, and adjust the data accordingly to achieve the high efficiency of multi-disciplinary collaborative work.

[0092] Combined with Figure 3As shown in the figure, the quick connection model module 120 in this system is mainly composed of an automatic path planning and connection unit 121 and a connection quality intelligent evaluation and maintenance unit 122 when specifically implemented.

[0093] The automatic path planning and connection unit 121 in this module can automatically plan the best connection path for the unconnected components detected by the connectivity check, based on the path search algorithm of graph theory and the artificial intelligence optimization algorithm.

[0094] Specifically, when the automatic path planning and connection unit 121 automatically plans the best connection path, it first abstracts the unconnected components and the surrounding environment into a graph structure based on the path search algorithm of graph theory, with the nodes being the components and the edges being the connection paths. On this basis, it then uses the Dijkstra algorithm to search for paths, and combines with the artificial intelligence optimization algorithm, such as the genetic algorithm, to transform the path planning problem into an optimization problem, sets a fitness function to evaluate the quality of the paths, and finds the optimal path through genetic operations.

[0095] Furthermore, when the automatic path planning and connection unit 121 automatically plans the best connection path, it will fully consider factors such as the spatial position of the components, material characteristics, construction process requirements, and avoiding conflicts with other pipelines.

[0096] Specifically, here the information such as the spatial position of the components, material characteristics, and construction process requirements is transformed into constraint conditions and cost functions in the corresponding algorithms. For example, for material characteristics, different materials have different connection difficulties, and different connection costs are assigned; the spatial position is used to limit the path search range; the construction process requirements are transformed into connection rules and sequence restrictions of the paths to avoid conflicts with other pipelines.

[0097] As an example, the automatic path planning and connection unit 121 can select the optimal path by simulating the construction difficulty under different paths and the impact on the surrounding pipelines.

[0098] The connection quality intelligent evaluation and maintenance unit 122 in the quick connection model module 120 can perform effective data interaction with the automatic path planning and connection unit 121, and runs after the automatic path planning and connection unit 121. It can evaluate the quality of the connection points of the connected components, and automatically generate a maintenance plan for the connection points that do not meet the standards.

[0099] Specifically, the connection quality intelligent evaluation and maintenance unit 122 first detects the electrical performance parameters of the connection points, such as detecting parameters such as the resistance and current transmission stability of the connection points, and mechanical performance parameters such as connection strength and fastening degree for quality evaluation.

[0100] On this basis, the connection quality intelligent evaluation and maintenance unit 122 further judges whether the connection point quality of the connected components is up to standard based on a set threshold by establishing a quality evaluation model.

[0101] Finally, for unqualified connection points, according to the type of quality problems, a maintenance plan is matched from the preset maintenance plan library or generated by using rule reasoning.

[0102] The maintenance plan generated by the connection quality intelligent evaluation and maintenance unit 122 for unqualified connection points includes, but is not limited to, information such as repair locations, repair methods, and required tools.

[0103] Furthermore, the connection quality intelligent evaluation and maintenance unit 122 further establishes a full-life cycle management file for connection points, records the detailed information of each connection, detection, and maintenance, and provides data support for subsequent equipment maintenance and upgrading.

[0104] As an example, the connection quality intelligent evaluation and maintenance unit 122 can predict the service life of connection points based on the historical maintenance data and equipment operation status recorded in the full-life cycle management file of connection points, arrange maintenance plans in advance, and ensure the stable operation of the electromechanical system.

[0105] Combined with Figure 4 As shown, the cable laying module 130 in this system is mainly composed of a virtual laying and scheme optimization unit 131, an intelligent construction guidance and monitoring unit 132, and a data real-time acquisition and feedback adjustment unit 133 that cooperate with each other when specifically implemented.

[0106] The virtual laying and scheme optimization unit 131 in this module can build a cable laying virtual environment that can simulate cable laying operations based on a BIM model, record the simulation cable laying operation process and data in real time, and use simulation analysis algorithms to evaluate various cable laying simulation schemes, and generate an optimal cable laying scheme according to the simulation results.

[0107] Specifically, the virtual laying and scheme optimization unit 131 builds a virtual cable laying environment based on virtual reality (VR) and augmented reality (AR) technologies, and this virtual cable laying environment can support simulation operations of cable laying in this virtual environment and record the operation process and data in real time; on this basis, different cable laying schemes are simulated by simulating different laying sequences, path selections, and fixing methods, and the simulation analysis algorithm is used to evaluate the impacts of various cable laying schemes on aspects such as cable tray filling rate, cable heat dissipation, and electromagnetic interference, and finally an optimal cable laying scheme is generated according to the simulation results.

[0108] As a further explanation, the virtual laying and scheme optimization unit 131 uses the three-dimensional data of the BIM model to build a virtual environment, and then combines VR / AR technology to achieve immersive interactive operations; and further sets cable laying rules and physical property simulation in the virtual environment.

[0109] On this basis, the laying strategy is dynamically adjusted through intelligent algorithms. For example, based on reinforcement learning algorithms, the virtual laying process can autonomously learn and optimize according to environmental feedback to improve the efficiency and quality of solution generation.

[0110] As an example, the virtual laying and scheme optimization unit 131 constructs a virtual cable laying environment and performs simulated cable laying operations in the virtual environment. By changing the arrangement of cables in the bridge, the simulation analysis algorithm evaluates the differences in the effects of different arrangements of cables in the bridge on the fill rate change and heat dissipation effect. Then, based on the simulation results, a multi-objective optimization algorithm is used to generate the optimal cable laying scheme.

[0111] The intelligent construction guidance and monitoring unit 132 in the cable laying module 130 can conduct effective data interaction with the virtual laying and scheme optimization unit 131, and can generate real-time cable laying guidance information based on actual scenarios for the construction site based on the cable laying scheme generated by the virtual laying and scheme optimization unit 131.

[0112] Specifically, the intelligent construction guidance and monitoring unit 132 realizes real-time guidance of cable laying based on actual scenes by cooperating with AR devices (such as AR smart helmets or AR tablet devices, etc.). The intelligent construction guidance and monitoring unit 132 analyzes the cable laying scheme generated by the virtual laying and scheme optimization unit 131 to obtain the corresponding cable laying path and operation step information, and then accurately superimposes the virtual cable laying path and operation steps on the real scene based on the AR device, thereby intuitively displaying the direction, connection position and fixing method of the cable, and forming real-time guidance information for cable laying based on the actual scene; this makes it easy for construction personnel to intuitively and accurately obtain relevant cable laying information, and can directly perform corresponding construction operations on the construction site according to the cable laying path and operation steps superimposed by the AR device in the construction site scene. At the same time, the intelligent construction guidance and monitoring unit 132 also monitors the construction site in real time based on computer vision technology, and judges whether the construction personnel's operations meet the specifications by analyzing the image data collected by the camera.

[0113] Furthermore, when the intelligent construction guidance and monitoring unit 132 is specifically implemented, it can optimize the parsing of the cable laying plan based on semantic understanding methods to more accurately obtain construction information. Then, in combination with the multi-sensor fusion technology, such as cameras and gyroscopes, it can improve the monitoring accuracy of the operations at the construction site. On this basis, a construction operation behavior prediction model can be established, and in combination with multi-sensors (such as cameras), it can monitor the operations at the construction site, thereby being able to detect potential errors in advance and intervene and guide in a timely manner.

[0114] As an example, the intelligent construction guidance and monitoring unit 132 generates real-time cable laying guidance information based on the actual scene for the construction site using AR devices. When the construction workers directly perform corresponding construction operations at the construction site based on this real-time cable laying guidance information, it simultaneously monitors the construction site in real time. If it is identified and judged that there is a deviation in the position where the construction workers lay the cable, it will promptly form a prompt message, such as issuing a voice prompt and an image annotation, to guide the construction workers to correct it and ensure the construction quality and progress.

[0115] The data real-time acquisition and feedback adjustment unit 133 in the cable laying module 130 can, during the actual cable laying process, real-time collect cable laying status data, and perform real-time processing and analysis on the collected data, and can automatically generate a cable laying adjustment plan for abnormal data.

[0116] Specifically, the data real-time acquisition and feedback adjustment unit 133 cooperates with several sensors to real-time collect cable laying status data.

[0117] The sensors here specifically include position sensors, tension sensors, etc. Among them, the position sensors are deployed at key nodes of the cable laying path, and determine the cable position through signal transmission and reception; the tension sensors are installed on cable laying equipment or key connection points, and measure the tension using principles such as strain gauges. The sensors deployed in this way collect data at a certain frequency, and establish a data transmission link with the data real-time acquisition and feedback adjustment unit 133 through wireless transmission (such as Bluetooth, ZigBee) or wired connection methods, so that the real-time collected data can be synchronously transmitted to the sensors and the data real-time acquisition and feedback adjustment unit 133.

[0118] Corresponding data analysis algorithm models and strategy adjustment models are deployed in the sensor and data real-time acquisition and feedback adjustment unit 133. Among them, the data analysis algorithm model can perform real-time processing and analysis on the collected data to judge whether the cable laying process is normal; and corresponding construction adjustment strategies are preset in the strategy adjustment model, and it can retrieve the corresponding construction adjustment strategies according to the analysis and judgment results of the data analysis algorithm model, and form construction adjustment information based on the construction adjustment strategies.

[0119] The data analysis algorithm model here preferably adopts a time series analysis algorithm and a threshold judgment algorithm to perform real-time processing and analysis on the collected data. Specifically, the time series analysis algorithm is used to analyze the changing trends of data such as cable position and tension over time, and combined with the normal data range set by the threshold judgment algorithm for judgment. If the data exceeds the range, it is determined as abnormal.

[0120] The corresponding construction adjustment strategy in the strategy adjustment model deployed in this sensor and the data real-time acquisition and feedback adjustment unit 133 can be determined according to actual needs and will not be elaborated here.

[0121] Furthermore, the construction adjustment information generated based on the construction adjustment strategy includes but is not limited to automatically adjusting the parameters of construction equipment or sending adjustment instructions to construction personnel.

[0122] As an example, when the construction personnel are laying cables at the construction site under the real-time guidance of the intelligent construction guidance and monitoring unit 132, this sensor and the data real-time acquisition and feedback adjustment unit 133 collect data such as the laying position and tensile tension of the cables in real time through position sensors, tension sensors, etc. At the same time, the deployed data analysis algorithm model performs real-time processing and analysis on the collected data to judge whether the cable laying process is normal; if data anomalies are found, such as excessive cable tension that may cause damage to the outer skin, the strategy adjustment model in this unit will immediately generate an alarm message and automatically adjust the parameters of the construction equipment or send adjustment instructions to the construction personnel according to the preset adjustment strategy.

[0123] As a further illustration, while this sensor and the data real-time acquisition and feedback adjustment unit 133 perform real-time processing and analysis on the collected data, it also compares the collected data with the virtual laying plan in real time. If there are deviations between the actual laying situation and the plan, it can automatically generate an adjustment plan according to the degree of deviation and the on-site situation, thereby ensuring that the final cable laying result meets the design requirements.

[0124] For the BIM-based cable intelligent optimization system solution given in this example scheme, in specific applications, it can form a corresponding software program to form a corresponding cable intelligent optimization software program system. This software program, in cooperation with AR devices and several corresponding sensors, realizes the above-mentioned cable intelligent optimization function during operation; at the same time, it is stored in a corresponding storage medium for the processor to retrieve and execute.

[0125] The following further illustrates the implementation process of the BIM-based cable intelligent optimization solution given by the present invention through corresponding application examples.

[0126] In this example, based on the framework of the present invention, a corresponding BIM-based cable intelligent optimization software program is constructed, a hardware architecture capable of running the software program is built, and corresponding AR devices and several corresponding sensors are configured.

[0127] For the AR device and several sensors, they are configured to be data-connected to the running entity of the cable intelligent optimization software program.

[0128] On this basis, for the corresponding electrical construction management project, first deploy corresponding sensors at the construction site. The electrical construction management process of cable intelligent optimization in this example is as follows:

[0129] Data sorting and import: After receiving the design drawings, the project electrical engineer details and sorts out the key information such as the electrical box numbers, switch setting values, and cable model specifications in the electrical system. After completion, through the electrical system diagram module in the cable intelligent optimization software system, the sorted data is imported into the software system. During the import process, the system will automatically conduct a preliminary check on the data format and integrity to ensure the accuracy and availability of the data.

[0130] Parameter verification and adjustment: Enter the electrical system diagram module, and the system will conduct a preliminary review of the cable specifications and electrical parameter results automatically exported by the plug-in according to the built-in rules. The user needs to confirm again with the project electrical engineer the key information such as "switch setting value", "cable cross-sectional area", "pipe diameter for threading", and "transformer specification". If any unreasonable parameters are found, they can be manually adjusted in this module to ensure that the parameters meet the design requirements and actual construction conditions.

[0131] Connection check and quick connection repair: Before running the cable laying function, start the connection check function. The software will automatically identify and select components such as cable trays that have achieved system connection. If any unconnected situations are found, the quick connection model module can be immediately used. The software will automatically complete the connection operation according to the one-to-one connection rule to ensure the connectivity of the electrical system.

[0132] Cable laying simulation and optimization: After the above steps are completed, run the cable laying module. The system will automatically find a laying path for the cable based on the imported electrical system data and the processed BIM model. By default, the system will give priority to the shortest path; alternatively, the user can also manually adjust the path selection rule according to actual needs, such as considering construction difficulty, cost, etc. After determining the laying path, the system will automatically calculate the filling rate of all cable trays and color the cable trays according to the filling rate size and the preset color settings, facilitating the user to intuitively view the filling rate situation. At the same time, the system will conduct an automatic pipeline comprehensive layout of all the cables in the cable tray based on the principle of avoiding cross collisions to generate the optimal cable layout plan.

[0133] Data Export and Application: After determining the final cable laying plan, the user can quickly export the cable information of all circuits. This information includes the cable starting and terminal boxes, cable specifications and models, length (the cable length has considered the bending radius and the half perimeter of the box), etc. The exported cable quantity list can be directly applied to construction links such as material procurement and cost accounting. In addition, the cable cross-section layout diagram generated by the system can also be used to guide on-site construction to ensure that the construction personnel can accurately lay the cables according to the optimized plan.

[0134] As can be seen from the above examples, the solution of the present invention integrates advanced BIM technology and intelligent algorithms to achieve automatic review of electrical system diagrams, intelligent planning of cable routes, accurate calculation of cable tray filling rates, automatic export of cable quantity lists, and optimization of cable layouts, which can effectively solve the problems of low efficiency, high error rate, and difficult optimization existing in traditional cable deepening and optimization work, and improve the overall quality and efficiency of building electrical construction.

[0135] For the above-mentioned BIM-based cable intelligent optimization solution, the embodiment of the present invention also provides a computer-readable storage medium, on which a program is stored, and when the program is executed by a processor, the steps of the above-mentioned cable intelligent optimization method are implemented.

[0136] The embodiment of the present invention also provides a processor, and the processor is used to run a program, wherein when the program runs, the steps of the above-mentioned cable intelligent optimization method are executed.

[0137] The embodiment of the present invention also provides a terminal device, which includes a processor, a memory, and a program stored on the memory and executable on the processor. The program code is loaded and executed by the processor to implement the steps of the above-mentioned cable intelligent optimization method.

[0138] The present invention also provides a computer program product, which is suitable for executing the steps of the above-mentioned cable intelligent optimization method when executed on a data processing device.

[0139] In the above embodiments, the descriptions of each embodiment have their own focuses. For the parts not detailed in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0140] Those skilled in the art can clearly understand that for the convenience and conciseness of description, the specific working processes of the above-described systems, devices, and modules can refer to the corresponding processes in the foregoing method embodiments, and will not be described herein again.

[0141] Those skilled in the art should understand that the embodiments of the present invention can be provided as a method, a system, or a computer program product. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present invention can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that contain computer-usable program code.

[0142] The present invention is described with reference to the flowcharts and / or block diagrams of methods, apparatuses (systems), and computer program products according to embodiments of the present invention. It should be understood that each flow and / or block in the flowchart and / or block diagram, as well as the combination of flows and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to the processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing devices to generate a machine, such that the instructions executed by the processor of the computer or other programmable data processing devices generate means for implementing the functions specified in Figure 1 one flow or multiple flows and / or blocks Figure 1 one block or multiple blocks.

[0143] These computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to work in a specific manner, such that the instructions stored in the computer-readable memory generate a manufactured article including instruction means that implement the functions specified in Figure 1 one flow or multiple flows and / or blocks Figure 1 one block or multiple blocks.

[0144] These computer program instructions can also be loaded onto a computer or other programmable data processing device, such that a series of operation steps are performed on the computer or other programmable device to generate a computer-implemented process, and thus the instructions executed on the computer or other programmable device provide steps for implementing the functions specified in Figure 1 one flow or multiple flows and / or blocks Figure 1 one block or multiple blocks.

[0145] In a typical configuration, a computing device includes one or more processors (CPUs), an input / output interface, a network interface, and memory.

[0146] The memory may include non-permanent memory in the form of computer-readable media, random access memory (RAM), and / or non-volatile memory, such as read-only memory (ROM) or flash memory (flash RAM). The memory is an example of computer-readable media.

[0147] Computer readable media include permanent and non-permanent, removable and non-removable media that can be implemented by any method or technology to store information. Information can be computer readable instructions, data structures, program modules or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, compact disk read-only memory (CD-ROM), digital versatile disk (DVD) or other optical storage, magnetic cassettes, magnetic tape disk storage or other magnetic storage devices or any other non-transmission media that can be used to store information that can be accessed by a computing device.

[0148] It should also be noted that the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, commodity or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, commodity or device. In the absence of more restrictions, the elements defined by the sentence "comprises a ..." do not exclude the existence of other identical elements in the process, method, commodity or device including the elements.

[0149] It will be appreciated by those skilled in the art that embodiments of the present invention may be provided as methods, systems or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment or an embodiment combining software and hardware. Moreover, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program codes.

[0150] The above-mentioned method of the present invention, or a specific system unit, or a part of the same, is a pure software architecture, which can be arranged on a physical medium, such as a hard disk, an optical disk, or any electronic device (such as a smart phone, a computer-readable storage medium) through program code. When the machine loads the program code and executes it (such as a smart phone loading and executing it), the machine becomes a device for implementing the present invention. The above-mentioned method and device of the present invention can also be transmitted in the form of program code through some transmission media, such as cables, optical fibers, or any transmission mode. When the program code is received, loaded and executed by a machine (such as a smart phone), the machine becomes a device for implementing the present invention.

[0151] The basic principles, main features and advantages of the present invention have been shown and described above. Those skilled in the art should understand that the present invention is not limited by the above embodiments. What is described in the above embodiments and the specification only illustrates the principles of the present invention. Without departing from the spirit and scope of the present invention, the present invention will have various changes and improvements, and these changes and improvements all fall within the scope of the present invention claimed. The scope of the present invention claimed is defined by the appended claims and their equivalents.

Claims

1. A BIM-based cable intelligent optimization system, characterized in that: The system includes an electrical system diagram module, a quick-connect model module, and a cable laying module. The electrical system diagram module can interact with the electrical BIM model, import basic cable model data, identify and read the imported electrical design drawing information, and analyze the identified electrical information data to identify abnormal data information; The quick connection model module can quickly connect the component system based on a one-to-one connection rule in the electromechanical model; The cable laying module can simulate the actual cable layout scenario for each distribution system cable in the electrical BIM model, compare and analyze multiple schemes according to different design requirements and laying scenarios, and generate the optimal cable layout plan.

2. The BIM-based cable intelligent optimization system according to claim 1 is characterized in that: The electrical system diagram module includes a data intelligent collection and import unit and a parameter intelligent analysis and adjustment unit. The data intelligent collection and import unit can automatically read and collect text and symbol information in the electrical design drawings, and in the collection process, according to the electrical industry terminology library and grammatical rules, perform semantic analysis on the identified information to identify erroneous electrical information data; The parameter intelligent analysis and adjustment unit can perform in-depth analysis on the electrical information data collected and analyzed by the data intelligent collection and import unit based on a machine learning algorithm to identify electrical information data with unreasonable selection and generate corresponding adjustment suggestion information.

3. The BIM-based cable intelligent optimization system according to claim 2 is characterized in that: The electrical system diagram module also includes a real-time data synchronization and sharing unit, which can establish a data synchronization and sharing connection with an electrical system outside the system.

4. The BIM-based cable intelligent optimization system according to claim 1, characterized in that: The fast connection model module includes an automatic path planning and connection unit and a connection quality intelligent evaluation and maintenance unit. The automatic path planning and connection unit can automatically plan the best connection path for the detected unconnected components based on the path search algorithm of graph theory and the artificial intelligence optimization algorithm; The connection quality intelligent evaluation and maintenance unit can evaluate the quality of connection points of connected components and automatically generate maintenance plans for connection points that do not meet the connection standards.

5. The BIM-based cable intelligent optimization system according to claim 1, characterized in that: The cable laying module includes a virtual laying and scheme optimization unit, an intelligent construction guidance and monitoring unit, and a real-time data collection and feedback adjustment unit. The virtual laying and scheme optimization unit can build a cable laying virtual environment that can simulate cable laying operations based on the BIM model, and record the simulated cable laying operation process and data in real time, and use simulation analysis algorithms to evaluate various cable laying simulation schemes, and generate an optimal cable laying scheme based on the simulation results; The intelligent construction guidance and monitoring unit can generate real-time guidance information for cable laying based on actual scenarios for the construction site based on the cable laying scheme generated by the virtual laying and scheme optimization unit; The real-time data collection and feedback adjustment unit can collect cable laying status data in real time during the actual cable laying process, and perform real-time processing and analysis on the collected data, and can automatically generate a cable laying adjustment plan for abnormal data.

6. A BIM-based cable intelligent optimization method, characterized in that: Said include: Data sorting and importing: importing basic cable model data through the electrical BIM model, identifying and reading electrical design drawing information, and analyzing the identified electrical information data to identify abnormal data information; Connectivity check and quick connection repair: through connectivity check, components that have been connected to the system are identified and selected, and unconnected components are quickly connected based on a one-to-one connection rule in the electromechanical model; Cable laying simulation and optimization: for each distribution system cable in the electrical BIM model, simulate the actual cable layout scenario, conduct comparative analysis of multiple schemes based on different design requirements and laying scenarios, and generate the optimal cable layout plan.

7. The BIM-based cable intelligent optimization method according to claim 6, characterized in that: The data combing and importing steps of the method include: First, read and collect the text and symbol information in the electrical design drawings, and in the collection process, perform semantic analysis on the recognized information based on the electrical industry terminology library and grammatical rules to identify erroneous electrical information data; Next, the read and collected electrical information data is deeply analyzed based on the machine learning algorithm to identify the unreasonable electrical information data and generate corresponding adjustment suggestion information.

8. The BIM-based cable intelligent optimization method according to claim 6, characterized in that: In the connectivity check and quick connection repair steps of the method, the best connection path is automatically planned for the detected unconnected components based on a graph theory path search algorithm and an artificial intelligence optimization algorithm.

9. The BIM-based cable intelligent optimization method according to claim 6, characterized in that: In the connectivity check and quick connection repair steps of the method, the quality of the connection points of the connected components is also evaluated, and a maintenance plan is automatically generated for the connection points that do not meet the connection standards.

10. The BIM-based cable intelligent optimization method according to claim 6, characterized in that: The cable laying simulation and optimization steps of the method include: Firstly, based on the BIM model, a virtual cable laying environment is constructed that can simulate cable laying operations, and the simulated cable laying operation process and data are recorded in real time. Simulation analysis algorithms are used to evaluate various cable laying simulation schemes, and the optimal cable laying scheme is generated based on the simulation results. Next, based on the generated cable laying plan, real-time guidance information for cable laying based on actual scenarios is generated for the construction site.

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