Method and device for deriving an algorithmic model, electronic device and storage medium

Abstract

The present application relates to the technical field of computer-aided design, and in particular to a calculation model export method and device, electronic equipment and a storage medium, the method comprising obtaining an export request of a target calculation model, the export request comprising an identifier of a target work unit in a target building model; determining the target work unit based on the identifier of the target work unit; querying a calculation mapping rule corresponding to a to-be-exported component in the target work unit, the calculation mapping rule being used to represent a corresponding relationship between component information and calculation information; obtaining component information of the to-be-exported component from the target building model, and mapping the component information based on the calculation mapping rule to determine the target calculation model. The processing process is implemented in a calculation cloud, without the aid of third-party software, and the export of the calculation model is based on the target building model stored in the calculation cloud, without the need for conversion of the building model before the export of the calculation model, thereby improving the export efficiency of the calculation model.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of computer-aided design, and in particular to a calculation model derivation method and device, an electronic device and a storage medium. BACKGROUND

[0002] In the field of building engineering design, the design of a building model can be realized through building information model tool software. In traditional calculation business, installation cost consultants need to understand the design content of designers and process drawings to meet the subsequent calculation requirements. The design classification is different from the calculation category, and the derivation of the design model needs to be set up to convert the model element classification into a classification that can be recognized by the calculation professional. The attributes connected by different classifications are different, and the component classification mapping can ensure that the connected calculation attributes are correct, so that the calculation and the preparation of the list can be counted and formed according to the specific requirements. The positioning of the components in the design stage is usually based on the building elevation, but the component attribution in the cost stage needs to be based on the structure elevation, so the design model needs to be converted into a calculation model according to the building elevation or the structure elevation. In order to more accurately and quickly meet the calculation derivation analysis of a larger scale of users, a cloud-based calculation derivation analysis technical solution needs to be realized.

[0003] In the prior art, the model calculation software is developed in the form of a plug-in in Revit software, including engineering settings, model mapping, application methods, analysis and calculation, and output reports. In the Revit calculation, the principle of model mapping is to generate a calculation model, and the related operations include floor conversion, component conversion, calculation of component attribution floor, and generation of a calculation model. Based on the Revit software, the calculation is carried out to obtain specific quantities. Since the generation of the calculation model is based on the secondary development of the Revit software, it cannot work independently once it leaves the Revit. SUMMARY

[0004] Therefore, the embodiments of the present application provide a calculation model derivation method and device, an electronic device and a storage medium to solve the problem that the existing calculation model is strongly dependent on the Revit software.

[0005] According to a first aspect, the embodiments of the present application provide a calculation model derivation method applied to a calculation cloud, and the method comprises:

[0006] Obtaining a derivation request of a target calculation model, wherein the derivation request comprises the identification of a target work unit in a target building model;

[0007] Determining the target work unit based on the identification of the target work unit;

[0008] query a calculation mapping rule corresponding to the to-be-exported component in the target work unit, the calculation mapping rule being used to represent a corresponding relationship between component information and calculation information;

[0009] obtain component information of the to-be-exported component from the target building model, and map the component information based on the calculation mapping rule to determine a target calculation model.

[0010] The method for exporting a calculation model provided by the embodiment of the present application is used for querying a calculation mapping rule of a to-be-exported component in a calculation cloud first, preparing data for subsequent calculation rule mapping, and then mapping component information of the to-be-exported component in a target building model based on the calculation mapping rule to obtain a target calculation model. The processing process is implemented in the calculation cloud, and does not need to rely on third-party software. The exporting of the calculation model is based on the target building model stored in the calculation cloud, and the building model does not need to be converted before the exporting of the calculation model, thereby improving the exporting efficiency of the calculation model.

[0011] In some embodiments, the querying of the calculation mapping rule corresponding to the to-be-exported component in the target work unit comprises:

[0012] querying the target work unit in the target building model in parallel to determine the to-be-exported component;

[0013] querying and determining the calculation mapping rule corresponding to the to-be-exported component in a preset rule library.

[0014] The method for exporting a calculation model provided by the embodiment of the present application is used for querying a target work unit in parallel to determine a to-be-exported component, thereby improving the query efficiency of the component.

[0015] In some embodiments, the querying of the calculation mapping rule corresponding to the to-be-exported component in the target work unit further comprises:

[0016] obtaining a quantity of the to-be-exported components in the target work unit;

[0017] determining a mapping query progress of the target work unit based on the quantity of the to-be-exported components and a real-time query processing time of the to-be-exported components.

[0018] The method for exporting a calculation model provided by the embodiment of the present application can make the user know the current query processing progress and improve the user experience, because the process of component query is time-consuming and the waiting time of the user end is long.

[0019] In some embodiments, the mapping of the component information based on the calculation mapping rule determines a target calculation model, comprising:

[0020] The number of calculation nodes for mapping processing is determined based on the number of the component information;

[0021] The calculation nodes are called to map the component information based on the calculation mapping rule to determine the target calculation model.

[0022] The method for deriving a calculation model provided by the embodiments of the present application adjusts the number of calculation nodes by the number of component information when mapping the component information, configures the calculation nodes on demand according to the demand, and improves the processing efficiency of the mapping calculation.

[0023] In some embodiments, the number of calculation nodes for mapping processing is determined based on the number of the component information, comprising:

[0024] The initial length of the derivation message queue of each calculation professional is determined by the component information;

[0025] The initial number of the calculation nodes is determined based on the initial length;

[0026] The real-time length of the derivation message queue of each professional is monitored, and the number of the calculation nodes is adjusted based on the real-time length.

[0027] The method for deriving a calculation model provided by the embodiments of the present application realizes the real-time adjustment of the number of calculation nodes by monitoring the implementation length of the derivation message queue, realizes the intelligent dynamic elastic expansion, and meets the requirements of the calculation processing performance.

[0028] In some embodiments, the calculation nodes are called to map the component information based on the calculation mapping rule to determine the target calculation model, comprising:

[0029] The component information and the corresponding calculation mapping rule are sent to the calculation nodes;

[0030] The target calculation model fed back by the calculation nodes is received, the target calculation model is obtained by mapping the component information based on the calculation mapping rule by the calculation nodes, the associated data is obtained by associating the component data mapping result by using the associated attribute, and the target calculation model is obtained based on the attribute data in the component data mapping result and the associated data.

[0031] The method for deriving the calculation quantity model provided in the embodiment of the present application is used for, after the component information mapping processing, performing association processing on the component data mapping results of the components having the association relationship, obtaining the associated data, and arranging the associated data in the obtained target calculation quantity model, thereby facilitating the derivation of the subsequent calculation quantity list.

[0032] In some embodiments, the derivation request of the target calculation quantity model comprises:

[0033] The derivation request of the target model is sent by the collaborative platform, and the target building model is uploaded by the design end through the collaborative platform.

[0034] The method for deriving the calculation quantity model provided in the embodiment of the present application is used for, after the component information mapping processing, performing association processing on the component data mapping results of the components having the association relationship, obtaining the associated data, and arranging the associated data in the obtained target calculation quantity model, thereby facilitating the derivation of the subsequent calculation quantity list.

[0035] According to a second aspect, the embodiment of the present application further provides a derivation device of a calculation quantity model, applied to a calculation quantity cloud, and the device comprises:

[0036] The obtaining module is configured to obtain a derivation request of a target calculation quantity model, and the derivation request comprises an identifier of a target work unit in a target building model.

[0037] The determining module is configured to determine the target work unit based on the identifier of the target work unit.

[0038] The querying module is configured to query a calculation quantity mapping rule corresponding to a to-be-derived component in the target work unit, and the calculation quantity mapping rule is used to represent a corresponding relationship between component information and calculation quantity information.

[0039] The mapping module is configured to obtain component information of the to-be-derived component from the target building model, and map the component information based on the calculation quantity mapping rule to determine a target calculation quantity model.

[0040] According to a third aspect, the embodiment of the present application provides an electronic device, comprising a memory and a processor, which are in communication connection with each other, the memory stores computer instructions, and the processor executes the computer instructions to perform the derivation method of the calculation quantity model in the first aspect or any one of the embodiments of the first aspect.

[0041] According to a fourth aspect, the embodiment of the present application provides a computer readable storage medium, which stores computer instructions, and the computer instructions are used to make the computer execute the derivation method of the calculation quantity model in the first aspect or any one of the embodiments of the first aspect.

[0042] The corresponding beneficial effects of the algorithm model derivation device, the electronic device and the computer readable storage medium provided by the embodiments of the present application are described above in the corresponding beneficial effect description of the algorithm model derivation method, and will not be described here again. BRIEF DESCRIPTION OF DRAWINGS

[0043] In order to more clearly illustrate the specific embodiments of the present application or the technical solutions in the prior art, the drawings needed to be used in the specific embodiments or the prior art description will be briefly introduced below. Obviously, the drawings in the following description are some embodiments of the present application, and other drawings can also be obtained by those skilled in the art without creative labor on the basis of these drawings.

[0044] Figure 1 is a general technical architecture schematic diagram of the algorithm model derivation according to the embodiments of the present application;

[0045] Figure 2 is a system architecture schematic diagram of the algorithm model derivation according to the embodiments of the present application;

[0046] Figure 3 is a system architecture schematic diagram of the algorithm model derivation according to the embodiments of the present application;

[0047] Figure 4 is a schematic diagram of the data model according to the embodiments of the present application;

[0048] Figure 5 is a flowchart of the algorithm model derivation method according to the embodiments of the present application;

[0049] Figure 6 is a determination schematic diagram of the algorithm mapping rule according to the embodiments of the present application;

[0050] Figure 7 is a flowchart of the algorithm model derivation method according to the embodiments of the present application;

[0051] Figure 8 is a parallel query schematic diagram of the work unit according to the embodiments of the present application;

[0052] Figure 9 is a query progress schematic diagram according to the embodiments of the present application;

[0053] Figure 10 is a flowchart of the algorithm model derivation method according to the embodiments of the present application;

[0054] Figure 11 is a schematic diagram of the algorithm analysis according to the embodiments of the present application;

[0055] Figure 12 is a processing schematic diagram of the algorithm derivation task according to the embodiments of the present application;

[0056] Figure 13 is a schematic diagram of processing a quantity derivation task according to an embodiment of the present invention;

[0057] Figure 14 is a structural block diagram of a device for deriving a quantity calculation model according to an embodiment of the present invention;

[0058] Figure 15 Schematic diagram of the hardware structure of an electronic device provided by an embodiment of the present invention;

[0059] Figure 16 This is a schematic diagram of the format of a GFC file provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0060] To make the purpose, technical solutions, and advantages of the embodiments of the present invention more clear, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are 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 those skilled in the art without making creative efforts shall fall within the scope of protection of the present invention.

[0061] The method for deriving the quantity calculation model provided in the embodiment of the present invention is applied to the quantity calculation cloud, and the method is used to determine the quantity calculation model used for the quantity calculation software to calculate the quantity calculation list. In the embodiment of the present invention, the quantity calculation cloud includes a quantity calculation cloud platform and a data platform. Figure 1 As shown, the design software is responsible for generating the building model, the quantity calculation cloud is responsible for converting the building model into a quantity calculation model, and the quantity calculation software is responsible for generating a quantity calculation list, thus completing the integration of design and quantity calculation, all based on the same 3D building model. Specifically, after the user designs the building model on the desktop, they upload it to the quantity calculation cloud through the collaborative platform. The quantity calculation cloud determines the quantity calculation model based on the quantity calculation model export method provided in the embodiments of the present invention. Subsequently, this modified quantity calculation model is imported into the quantity calculation software to obtain the quantity calculation list.

[0062] Regarding the interactive relationship between the calculation cloud and the collaborative platform, such as Figure 2 as well as Figure 3 As shown in the figure, the entire system adopts a distributed system architecture and is scaled according to the functional characteristics of each service node. Figure 2 The contents involved are described as follows:

[0063] 1) Data Platform: Store Building Information Modeling (BIM) data, responsible for scheduling system processing task queue, invoke computing nodes to convert calculation model, upload General Foundation Classes (GFC) data file and report data file.

[0064] 2) Collaboration Platform: Provide collaboration support between data platform, desktop, and calculation GFC export application, while storing collaboration data such as project information, delivery package, and sub-item information.

[0065] 3) BIMFACE: Lightweight visualization of BIM model, browse BIM three-dimensional model and view.

[0066] 4) Calculation Cloud Platform: Calculation GFC export analysis and data storage, view GFC export results, including calculation application management service, component data query service, and task status query service. The tool-side design software uploads the designed BIM model to the collaboration platform, the user creates a calculation GFC export task on the web page of the calculation GFC export analysis system in the cloud, selects the model delivery package and related professional work units, performs floor elevation conversion, component data and calculation rule conversion, initiates the task, queries the task status, and views the result report and downloads the GFC file.

[0067] 5) WORKER: Calculation GFC export analysis and calculation module, used to calculate and generate output GFC calculation model, and upload the calculated calculation model to the calculation cloud platform for user download and use.

[0068] The calculation model export method provided by the embodiment of the application is implemented based on cloud technology, and model browsing, rule setting and component conversion mapping, export calculation processing, and data storage capabilities of the calculation model export are implemented in the cloud. The analysis and calculation performance and data storage capability support are dynamically expanded on demand in the cloud, and the processing capability is no longer limited by the user-side machine configuration; at the same time, the collaborative processing and real-time sharing of calculation analysis process data are supported, and the user collaboration capability is improved.

[0069] For example, Figure 4The data storage involved includes application data storage MySQL, data cache REDIS, distributed object data storage OBS, model data storage ES and HBASE. The data exported by the cloud computing includes: a work unit, i.e., component basic data, stored in the distributed storage system ES and HBASE of the data platform; a calculation result, i.e., a computing model, stored in the distributed storage system OBS of the data platform; GFC report data stored in the computing cloud database MySQL; other application data, such as computing setting data, rule data and rule mapping data, stored in a structured relational database such as MySQL; some temporary data in the application management process, such as processing progress information, using a distributed cache such as REDIS; and temporary result data generated in the computing process stored in the temporary data directory of each computing node.

[0070] Figure 4 The specific meanings of each data stored in the cloud computing are explained as follows:

[0071] 1) Computing export task: a user creates a GFC export task in the cloud computing application;

[0072] 2) Building model and work unit: used to select a building model and the work unit thereunder in a certain export task;

[0073] 3) Floor elevation conversion: the user selects to calculate according to the structural elevation or the building elevation for all floor elevations of a specified work unit;

[0074] 4) Component mapping rule: each computing specialty has its own component mapping rule;

[0075] 5) Component mapping rule file: the component information of a work unit is matched with the component mapping rule, and the matching result is transmitted to the OBS of the data platform in the form of a json file for storage, so as to be read by the computing node for calculation and analysis;

[0076] 6) Building model: the component and graphic data generated after conversion when the user designs and submits a work unit on the desktop software.

[0077] In combination with the computing model export architecture shown in Figure 2 or Figure 3 the processing steps of the export task are as shown in Figure 12 The processing steps mainly include task initiation, calculation and analysis, result data uploading and task state query. Specifically,

[0078] A1 processing stage: the cloud computing initiates a task, the data platform schedules and queues, downloads the rule file and starts the computing node program.

[0079] A2 processing stage: The calculation node analyzes the input rule data, opens GDOC to organize the component data to be exported, calculates and exports the component type and public attributes according to the mapping rules, exports the component instance and primitive and attribute information, and generates GFC data files and report data files.

[0080] A3 processing stage: The data platform uploads the generated GFC data file to the distributed storage OBS and generates associated data for the task results.

[0081] A4 processing stage: The calculation cloud queries the data platform for the status of the task. If successful, it writes the report data and related result-related data, including the name of the downloaded GFC data file.

[0082] Furthermore, in order to more accurately display the task processing accuracy information to the user, during the task processing process, each service or module will report the processing progress information at different processing stages.

[0083] The method for exporting a quantity calculation model provided by an embodiment of the present invention is a BIM-based cloud-based quantity calculation export mode. This export method no longer relies on Revit. Model data is stored in the cloud. Project deliverables are selected from the cloud, floor elevation conversion and rule conversion are performed, and a quantity calculation export task is initiated from the cloud. The quantity calculation data is exported using a cloud-based computing service node, and the GFC quantity calculation data file is downloaded from the cloud.

[0084] According to an embodiment of the present invention, an embodiment of a method for deriving a quantity calculation model is provided. It should be noted that the steps shown in the flowchart of the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions, and although a logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in an order different from that shown here.

[0085] In this embodiment, a method for deriving a quantity calculation model is provided, which can be used for the above-mentioned quantity calculation cloud, etc. Figure 5 Flowchart of the method for deriving the quantity calculation model according to an embodiment of the present invention. Figure 5 As shown, the process includes the following steps:

[0086] S11, obtaining an export request for a target quantity calculation model.

[0087] The export request includes an identifier of a target work unit in the target building model.

[0088] As described above, the user triggers the export of the target calculation model through the calculation export software installed on the collaboration platform. Specifically, when issuing the export request, the user selects the target work unit in the target building model that needs to be exported through interaction with the calculation export software; and sends the export request to the calculation cloud, encapsulates the identification of the target work unit in the target building model, and obtains the export request. Correspondingly, the calculation cloud obtains the export request and obtains the identification of the target work unit.

[0089] S12, determining the target work unit based on the identification of the target work unit.

[0090] For the target building model, it is stored in the calculation cloud. After the calculation cloud obtains the export request, the identification of the target work unit in the export request can be used to query the target work unit. After each building model is uploaded to the calculation cloud, it will have a unique identification in the calculation cloud to distinguish each building model. The building model includes at least one work unit, and after the building model is determined, the identification of the target work unit can be used to determine the target work unit.

[0091] S13, querying the calculation mapping rule corresponding to the to-be-exported component in the target work unit.

[0092] The calculation mapping rule is used to represent the correspondence between component information and calculation information.

[0093] Because the components of different professions (for example, building, structure, and electromechanical) have different ways of expressing component information, in order to export the subsequent calculation list, it is necessary to uniformly map the component information. For different professions, there are corresponding calculation mapping rules, so it is necessary to query the calculation mapping rule corresponding to the to-be-exported component.

[0094] When querying the calculation mapping rule, the floor elevation needs to be uniformly converted into the structure elevation or the building elevation. As shown in Figure 6 The result of the component conversion is to obtain the calculation mapping rule corresponding to the component, which includes three steps: component query, component mapping rule query, and component and mapping rule matching. After the above processing, the correspondence between the component and the calculation mapping rule is obtained. The correspondence can be represented by the component mapping rule file described above.

[0095] S14, obtaining the component information of the to-be-exported component from the target building model, and mapping the component information based on the calculation mapping rule to determine the target calculation model.

[0096] The quantity mapping rules obtained in the above steps prepare data for the quantity mapping here. The quantity mapping rules are used to determine how component information should be mapped. When performing quantity mapping, the component information of the components to be exported in the target building model needs to be mapped in conjunction with these quantity mapping rules to ultimately determine the target quantity model.

[0097] This step will be described in detail below.

[0098] In the method for exporting a quantity calculation model provided in this embodiment, the target building model is stored in a quantity calculation cloud. The quantity calculation mapping rules for the components to be exported are first queried through the quantity calculation cloud. Data is then prepared for subsequent quantity calculation rule mapping. Based on these quantity calculation mapping rules, the component information of the components to be exported in the target building model is mapped to obtain the target quantity calculation model. This processing is implemented in the quantity calculation cloud, without the need for third-party software. Furthermore, the quantity calculation model is exported based on the target building model stored in the quantity calculation cloud. There is no need to convert the building model before exporting the quantity calculation model, which improves the efficiency of quantity calculation model export.

[0099] In this embodiment, a method for deriving a quantity calculation model is provided, which can be used for the above-mentioned quantity calculation cloud, etc. Figure 7 Flowchart of the method for deriving the quantity calculation model according to an embodiment of the present invention. Figure 7 As shown, the process includes the following steps:

[0100] S21, obtaining an export request for a target quantity calculation model.

[0101] The export request includes an identifier of a target work unit in the target building model.

[0102] For details, please see Figure 5 S11 of the illustrated embodiment will not be described in detail here.

[0103] S22: Determine the target work unit based on the identifier of the target work unit.

[0104] For details, please see Figure 5 S12 of the illustrated embodiment will not be described in detail here.

[0105] S23, querying the quantity calculation mapping rules corresponding to the components to be exported in the target work unit.

[0106] The quantity calculation mapping rule is used to represent the corresponding relationship between component information and quantity calculation information.

[0107] Specifically, the above S23 includes:

[0108] S231, querying target work units in parallel in the target building model to determine components to be exported.

[0109] The target building model includes a plurality of work units, and all work units do not need to be subjected to the export processing of the calculation model for the export request. Therefore, it is necessary to query the target work unit in the target building model. In order to improve the query efficiency, a parallel query mode is used for processing. Each work unit includes a plurality of components. For example, a room can be taken as a work unit, and the work unit includes components such as doors, windows, and walls. Based on this, after the target work unit is queried, the components to be exported in the target work unit can be determined.

[0110] As shown in Figure 8 , the electromechanical installation profession has its own component conversion rule and data filtering rule, and the component conversion is the most time-consuming in the step of generating the mapping rule. The main time-consuming operation is the component query. In order to improve the efficiency of the component query, the hbase data cluster and the parallel query processing mechanism are used to speed up the query of the components.

[0111] S232, the calculation mapping rule corresponding to the component to be exported is queried and determined in the preset rule library.

[0112] After the component to be exported is determined, the calculation mapping rule is queried in the preset rule library. For example, the component is queried according to the profession to which the component belongs.

[0113] In some embodiments, the above S23 includes:

[0114] (1) The number of components to be exported in the target work unit is obtained.

[0115] (2) Based on the number of components to be exported and the real-time query processing time of the components to be exported, the mapping query progress of the target work unit is determined.

[0116] Because the component query is time-consuming, the waiting time of the user end will be longer. Therefore, in order to improve the user experience, the progress bar of the component conversion progress is increased, and the relatively accurate progress value can be prompted to the user according to the number of components of the plurality of work units and the processing situation of the components, and the processing progress of the component query. As shown in Figure 9 , the total time of the mapping query progress = the component query time + the component and rule matching time.

[0117] The calculation of the progress value can be: total time (100%) = component data query time (90%) + component data and rule matching time (10%), and the two time periods are preset according to actual operation. Among the 90%, the percentage of each work unit in the total is estimated according to the number of work units and the percentage of the number of components of each work unit in the total. For example, the number of components of three work units A is 100, the number of components of B is 200, and the number of components of C is 200, so the time for completing A is about 100 / (100+200+200) = 20%*90%.

[0118] As shown in Figure 8 , the component query of the work unit is parallel processing, and the progress information in REDIS is updated immediately after the component data of each work unit is processed. The user side can query the current total progress in real time. When the components of several work units are all queried, the matching of the components and the mapping rules is started, the mapping result is stored in the database, and the progress information in REDIS is updated to 100%. When the user side queries that the progress has reached 100%, the component mapping result data is queried and displayed on the web page.

[0119] Because the component query process is time-consuming, the waiting time of the user side will be longer. By determining the mapping query progress of the target work unit, the user can know the current query processing progress, and the user experience is improved.

[0120] S24, obtaining component information of the to-be-exported component from the target building model, and mapping the component information based on the calculation mapping rule to determine a target calculation model.

[0121] For details, please refer to Figure 5 S14 of the embodiment shown, which will not be repeated here.

[0122] The calculation model export method provided in the embodiment can improve the query efficiency of the component by querying the target work unit in parallel to determine the to-be-exported component.

[0123] In the embodiment, a calculation model export method is provided, which can be used in the calculation cloud and the like, Figure 10 is a flowchart of the calculation model export method according to the embodiment of the application, as shown in Figure 10 , the flowchart includes the following steps:

[0124] S31, obtaining a target calculation model export request.

[0125] Among them, the target work unit identification in the target building model is included in the export request.

[0126] In some embodiments, S31 includes receiving an export request for at least one target model from the collaborative platform, where the target building model is uploaded by the design client via the collaborative platform. The collaborative platform enables simultaneous multi-person collaboration, whereby multiple individuals from different disciplines simultaneously initiate export requests for quantity calculation models for the same project in the cloud.

[0127] That is, multiple people with different expertise can simultaneously request the export of the same building model, or even different building models, in the cloud. Each export request is considered an export task. The Quantity Calculation Cloud analyzes each export task to determine if they are identical. If so, it processes them only once, and the resulting target quantity calculation model is shared across all requests.

[0128] As mentioned above, the Calculation Cloud consists of a computing connection platform and a data platform. The data platform is used to store data, such as building models, intermediate calculation results, and GFC report files. To improve transaction response efficiency, the Calculation Cloud platform and the data platform use an asynchronous messaging mechanism to deliver tasks, and the Calculation Cloud platform uses an asynchronous query mechanism to query the processing status of tasks.

[0129] like Figure 13 As shown, the message middleware queue is divided into a consumption queue, which is a queue for pending messages, and a dead letter queue, which is a queue for messages that cannot be processed or task messages that have failed after three retries. Task receiving service APP1 has a retry mechanism. It will periodically query the status of the task. If it finds that the task has failed, it will resend a message to the message middleware to retry the task. Task processing executor APP2 is responsible for downloading data files, calling the compute node calculation program, pushing the generated GFC data files to OBS storage, and notifying APP1 that the task has been calculated. At the same time, the task executor can also perform monitoring and alarming. When a compute node fails, APP2 can push a message to WeChat for Business to notify the enterprise user administrator, improving the user experience.

[0130] The monitoring service monitors the number of task messages in the message middleware queue. When the number of tasks reaches a certain scale, it dynamically adjusts the number of computing service nodes to ensure the overall system's computing throughput and processing efficiency. To improve computing node utilization, three specialized computing nodes (architecture, structure, and electromechanical) can be deployed simultaneously within the same computing node, meaning that a single computing node can handle up to three tasks simultaneously.

[0131] S32: Determine the target work unit based on the identifier of the target work unit.

[0132] For details, please see Figure 5 S12 of the illustrated embodiment will not be described in detail here.

[0133] S33, querying the calculation mapping rule corresponding to the to-be-exported component in the target work unit.

[0134] The calculation mapping rule is used to represent the corresponding relationship between the component information and the calculation information.

[0135] For details, please refer to Figure 7 S23 of the embodiment shown, which will not be described here.

[0136] S34, obtaining the component information of the to-be-exported component from the target building model, and mapping the component information based on the calculation mapping rule to determine the target calculation model.

[0137] Specifically, the above S34 includes:

[0138] S341, obtaining the component information of the to-be-exported component from the target building model.

[0139] The specific component information of the to-be-exported component is recorded in the target building model, and the specific component information needs to be obtained from the target building model when the calculation mapping processing of the component is performed.

[0140] S342, determining the number of computing nodes for mapping processing based on the number of component information.

[0141] As Figure 11 When the calculation mapping processing of the component is performed, in order to reduce the mapping processing delay, the number of component information is matched with the corresponding number of computing nodes to facilitate timely processing.

[0142] In some embodiments, the above S342 includes:

[0143] (1) determining the initial length of the export message queue of each calculation specialty using the component information.

[0144] (2) determining the initial number of computing nodes based on the initial length.

[0145] (3) monitoring the real-time length of the export message queue of each specialty, and adjusting the number of computing nodes based on the real-time length.

[0146] Specifically, when the computing node starts processing the export task, the calculation cloud needs to first determine the initial length of the export message queue of each calculation specialty using the component information. As described above, different specialties have different component mapping rules, so it is necessary to perform component mapping for different calculation specialties. Based on this, corresponding export message queues are set for different specialties. The initial length of the export message queue is first determined, and the length of the export message queue is real-time changed during the mapping processing, and then the number of computing nodes is adjusted according to the length of the real-time changed message queue.

[0147] Since the computing service is heavy and the computing time is relatively long, in order to improve the overall processing task throughput of the system, the computing worker computing service supports intelligent dynamic elastic scaling. A message middleware monitoring service is used to monitor the total number of messages of each professional computing queue. A professional is taken as an analysis unit, a rule N = MQ / Threshold-n is set, Threshold is a fixed value (usually >= 10, can be modified and configured), MQ is the total number of messages of a professional computing queue that needs to be calculated, n is the number of currently started computing nodes, and N is the number of nodes that need to be dynamically scaled. By using the start and stop capabilities of the public cloud image service, the total number of computing messages is monitored in real time, and when the number of messages rises to trigger the set rule, a batch of nodes are dynamically started, which can also be one; when the number of messages decreases to a certain extent, the batch of nodes are destroyed, but the original computing nodes will be retained. This flexible elastic scheduling method can meet the parallel computing needs of large-scale users in the public cloud platform environment by dynamically creating and destroying elastic ECS resources on demand, and can greatly reduce production costs.

[0148] By monitoring the implementation length of the exported message queue, the number of computing nodes is adjusted in real time, and intelligent dynamic elastic scaling is realized to meet the requirements of computing processing performance.

[0149] S343, calling the computing node to map the component information based on the computing amount mapping rule to determine the target computing amount model.

[0150] When the computing node performs component computing amount mapping processing, it needs to obtain the computing amount mapping rule and the component information, and then perform mapping processing. In some embodiments, the above S343 includes:

[0151] (1) sending the component information and the corresponding computing amount mapping rule to the computing node.

[0152] (2) receiving the target computing amount model fed back by the computing node, wherein the target computing amount model is obtained by the computing node based on the computing amount mapping rule to map the component information to obtain a component mapping result, using the association attribute to associate the component data mapping result to obtain associated data, and based on the attribute data in the component data mapping result and the associated data.

[0153] The calculation processing is data arrangement according to the protocol and writing into the GFC file. The processing process includes: mapping the system type and classification in the design software to the calculation, and determining which types of components to export; traversing the types of components that need to be exported in the current project in the design software, arranging data, arranging and packaging data according to the data format required by the calculation, and arranging and packaging data according to the data format required by the calculation. There are project information, building information, floor information and component information. The component information is divided into component type and component instance, and one component type corresponds to multiple component instances; generating a GFC file. Among them, the GFC file is divided into attribute data and associated data. That is, the data arranged in the second step is written into the GFC file according to the specified format, and the data is associated.

[0154] Specifically, the calculation mapping includes system mapping and classification mapping. For system mapping, it is based on system classification processing, that is, system classification includes pipe class, pipe fitting class and equipment class. Pipe class: obtain system type according to pipe interface, and obtain system classification according to system type; pipe fitting class: obtain system type according to main connection point, and then obtain system classification. If the system classification is not clear, check the system type keyword; equipment class: because the equipment has many connection points, first check whether the equipment can be successfully mapped through the category mapping. If not, check whether it has a water professional connection point. If it has, find the system type or system classification according to the water connection point, so as to achieve the mapping effect. If there is no water connection point, give a default mapping, and the user adjusts it himself.

[0155] The mapping rules of classification mapping can be shown in the following table:

[0156]

[0157] For the calculation node, after the component information mapping processing, the component data mapping results with association relationship are also associated, and the association data is obtained, that is, the association data has been arranged in the obtained target calculation model, which is convenient for subsequent calculation list export.

[0158] After the calculation node calculates the calculation mapping result, it needs to be exported to the calculation cloud for multiple people to download. Specifically, according to the system mapping and classification mapping, all components of the current project document are traversed to obtain component information, and the relevant data is arranged and exported according to the data protocol required by the calculation. Component instance is used as model display data, which is divided into line type component and point type component. The line type component exports the center line and the local coordinate system. The point type component exports the connection point or installation point of the equipment.

[0159] In some embodiments, the final target calculation model can be represented in the form of a GFC file. As shown in Figure 16 The data end starts with DATA and ends with ENDSEC. The format of the GFC file is as follows:

[0160] (1) Number (# line number)

[0161] #: fixed identification, no special processing

[0162] Line number: glodon::objectbuf::EntityRef(int), the number arranged in sequence when writing data, equivalent to the address, in incremental form, 0 or -1, both can be used for error judgment.

[0163] (2) Data information

[0164] Component type, that is, components with the same public attribute. For example, address #9

[0165] Component instance, that is, private attributes and graphic element information of the component. For example, #22

[0166] (3) Data association

[0167] The component type is associated with the component instance, which is a one-to-many relationship. For example, #25

[0168] Building and floor association, one-to-many relationship. For example, #23

[0169] Floor and component association, one-to-many relationship. For example, #24

[0170] In this embodiment, the target quantity model is represented by the GFC file, that is, all component information and graphic element information related to the software group macro are arranged and packaged according to the protocol, and the data is associated.

[0171] The quantity model export method provided in this embodiment adjusts the number of calculation nodes by using the number of component information when mapping the component information, and configures the calculation nodes on demand according to the demand, thereby improving the processing efficiency of the mapping calculation.

[0172] The quantity model export method provided in this embodiment supports multiple people in different professions to simultaneously initiate quantity GFC export analysis in the cloud for the same project, supports collaborative editing processing of data in the export processing process, such as project-level mapping rules, supports online viewing and downloading of export analysis result reports. Relying on the collaborative ability of the collaborative platform to realize the integration of design and quantity, the design software on the desktop can upload the design model to the cloud storage, and the quantity export analysis can read the cloud design model data to calculate and export the quantity model. The design and cost platform is developed based on the same three-dimensional graphics engine, the mapping accuracy and matching degree of different business components are high, and it is more in line with the business operation habits of industry designers and cost engineers.

[0173] According to the actual calculation amount export analysis function, the server configuration can be dynamically adjusted in the cloud to meet the requirements of calculation processing performance; with the growth of the calculation amount export analysis result data, the cloud storage space can be upgraded at any time. The export processing of the calculation amount model is processed in the cloud, the cloud data storage service has the characteristics of stability and high efficiency, and provides data synchronization backup service, reduces the risk of data loss, and is more secure for data security. Since the calculation amount export analysis processing process is performed on the cloud server, the machine configuration requirements of the user client are not high, and the processing capacity of the calculation amount export analysis will not be affected by the difference in user machine configuration, and the user experience is more friendly and consistent.

[0174] In the embodiment, an export device of a calculation amount model is also provided, which is used to implement the above-mentioned embodiments and preferred embodiments, and will not be described again. As used below, the term "module" can be a combination of software and / or hardware that implements a predetermined function. Although the device described in the following embodiments is preferably implemented in software, hardware, or a combination of software and hardware is also possible and contemplated.

[0175] The embodiment provides an export device of a calculation amount model, which is applied to a calculation amount cloud, as shown in the figure, the device comprises: Figure 14

[0176] The acquisition module 41 is configured to acquire an export request of a target calculation amount model, wherein the export request comprises an identifier of a target work unit in a target building model;

[0177] The determination module 42 is configured to determine the target work unit based on the identifier of the target work unit;

[0178] The query module 43 is configured to query a calculation amount mapping rule corresponding to a to-be-exported component in the target work unit, wherein the calculation amount mapping rule is used to represent a corresponding relationship between component information and calculation amount information;

[0179] The mapping module 44 is configured to acquire component information of the to-be-exported component from the target building model, and map the component information based on the calculation amount mapping rule to determine a target calculation amount model.

[0180] In some embodiments, the query module 43 comprises:

[0181] The query unit is configured to query the target work unit in parallel in the target building model to determine the to-be-exported component;

[0182] The first determination unit is configured to query and determine the calculation amount mapping rule corresponding to the to-be-exported component in a preset rule library.

[0183] In some embodiments, the query module 43 further comprises:​

[0184] an acquisition unit, configured to acquire a quantity of the to-be-exported components in the target work unit;

[0185] a second determination unit, configured to determine a mapping query progress of the target work unit based on the quantity of the to-be-exported components and real-time query processing time of the to-be-exported components.

[0186] In some embodiments, the mapping module 44 comprises:

[0187] a third determination unit, configured to determine a quantity of computing nodes for mapping processing based on the quantity of the component information;

[0188] a calling unit, configured to call the computing nodes to map the component information based on the quantity mapping rules, and determine the target quantity model.

[0189] In some embodiments, the third determination unit comprises:

[0190] a first determination sub-unit, configured to determine an initial length of an export message queue of each quantity specialty by using the component information;

[0191] a second determination sub-unit, configured to determine an initial quantity of the computing nodes based on the initial length;

[0192] a monitoring sub-unit, configured to monitor real-time lengths of the export message queues of each specialty, and adjust the quantity of the computing nodes based on the real-time lengths.

[0193] In some embodiments, the calling unit comprises:

[0194] a sending sub-unit, configured to send the component information and the corresponding quantity mapping rules to the computing nodes;

[0195] a receiving sub-unit, configured to receive the target quantity model fed back by the computing nodes, the target quantity model being obtained by the computing nodes based on quantity mapping rules to map the component information to obtain component mapping results, using association attributes to associate the component data mapping results to obtain associated data, and using attribute data in the component data mapping results and the associated data to obtain.

[0196] In some embodiments, the acquisition module comprises:

[0197] a receiving unit, configured to receive an export request of at least one target model sent by a collaboration platform, the target building model being uploaded by a design end through the collaboration platform.

[0198] The device for deriving the quantity calculation model in this embodiment is presented in the form of a functional unit, where the unit refers to an ASIC circuit, a processor and memory that executes one or more software or fixed programs, and / or other devices that can provide the above functions.

[0199] The further functional description of each of the above modules is the same as that of the above corresponding embodiments and will not be repeated here.

[0200] An embodiment of the present invention further provides an electronic device having the above Figure 14 The derivation device of the quantity calculation model shown.

[0201] See also Figure 15 , Figure 15 is a structural diagram of an electronic device provided by an optional embodiment of the present invention, such as Figure 15 As shown, the electronic device may include: at least one processor 51, such as a CPU (Central Processing Unit), at least one communication interface 53, a memory 54, and at least one communication bus 52. The communication bus 52 is used to realize the connection and communication between these components. The communication interface 53 may include a display screen (Display), a keyboard (Keyboard), and the optional communication interface 53 may also include a standard wired interface and a wireless interface. The memory 54 may be a high-speed RAM memory (Random Access Memory, volatile random access memory) or a non-volatile memory (non-volatile memory), such as at least one disk memory. The memory 54 may optionally be at least one storage device located away from the aforementioned processor 51. The processor 51 may be combined with Figure 15 In the described apparatus, the memory 54 stores an application program, and the processor 51 calls the program code stored in the memory 54 to execute any of the above method steps.

[0202] The communication bus 52 may be a peripheral component interconnect (PCI) bus or an extended industry standard architecture (EISA) bus. The communication bus 52 may be divided into an address bus, a data bus, a call bus, etc. For ease of representation, Figure 15 Only one thick line is used in the diagram, but this does not mean that there is only one bus or one type of bus.

[0203] Among them, the memory 54 may include volatile memory (English: volatile memory), such as random-access memory (English: random-access memory, abbreviated: RAM); the memory may also include non-volatile memory (English: non-volatile memory), such as flash memory (English: flash memory), hard disk drive (English: hard disk drive, abbreviated: HDD) or solid-state drive (English: solid-state drive, abbreviated: SSD); the memory 54 may also include a combination of the above types of memory.

[0204] The processor 51 may be a central processing unit (CPU), a network processor (NP), or a combination of a CPU and a NP.

[0205] The processor 51 may further include a hardware chip. The hardware chip may be an application-specific integrated circuit (ASIC), a programmable logic device (PLD), or a combination thereof. The PLD may be a complex programmable logic device (CPLD), a field-programmable gate array (FPGA), a generic array logic (GAL), or any combination thereof.

[0206] Optionally, the memory 54 is further configured to store program instructions. The processor 51 may call the program instructions to implement the method for deriving the quantity calculation model as shown in any embodiment of the present application.

[0207] An embodiment of the present invention further provides a non-transitory computer storage medium storing computer-executable instructions that can execute the method for deriving the quantity calculation model in any of the above method embodiments. The storage medium can be a magnetic disk, an optical disk, a read-only memory (ROM), a random access memory (RAM), a flash memory, a hard disk drive (HDD), or a solid-state drive (SSD); the storage medium can also include a combination of the above types of memory.

[0208] Although the embodiments of the present invention have been described with reference to the accompanying drawings, those skilled in the art may make various modifications and variations without departing from the spirit and scope of the present invention. Such modifications and variations are all within the scope defined by the appended claims.

Claims

1. A method for deriving a quantity calculation model, characterized in that: Applied to the calculation cloud, the method includes: Obtaining an export request for a target quantity calculation model, wherein the export request includes an identifier of a target work unit in the target building model; determining a target work unit based on an identifier of the target work unit; Querying the quantity mapping rule corresponding to the component to be exported in the target work unit, wherein the quantity mapping rule is used to express the correspondence between component information and quantity information, and the quantity mapping rule is used to determine the quantity mapping method of the component information; Component information of the components to be exported is obtained from the target building model, and the component information is mapped based on the quantity calculation mapping rule to determine a target quantity calculation model.

2. The method according to claim 1, characterized in that The querying of the quantity calculation mapping rules corresponding to the components to be exported in the target work unit includes: querying the target work units in parallel in the target building model to determine the components to be exported; The quantity calculation mapping rule corresponding to the component to be exported is searched and determined in the preset rule library.

3. The method according to claim 2, characterized in that The querying of the quantity calculation mapping rules corresponding to the components to be exported in the target work unit also includes: Obtaining the number of components to be exported in the target work unit; The mapping query progress of the target work unit is determined based on the number of the components to be exported and the real-time query processing time of the components to be exported.

4. The method according to claim 1, wherein Mapping the component information based on the quantity calculation mapping rule to determine a target quantity calculation model includes: determining the number of computing nodes used for mapping processing based on the number of component information; The computing node is called to map the component information based on the quantity calculation mapping rule to determine the target quantity calculation model.

5. The method according to claim 4, characterized in that The determining the number of computing nodes for mapping processing based on the number of component information includes: Determine the initial length of the export message queue for each calculation specialty using the component information; determining an initial number of the computing nodes based on the initial length; The real-time length of the export message queue of each profession is monitored, and the number of the computing nodes is adjusted based on the real-time length.

6. The method according to claim 4, characterized in that The calling of the computing node to map the component information based on the quantity calculation mapping rule to determine the target quantity calculation model includes: Sending the component information and the corresponding calculation mapping rule to the computing node; The target quantity calculation model fed back by the computing node is received, wherein the computing node maps the component information based on the quantity calculation mapping rule to obtain a component data mapping result, associates the component data mapping result with associated attributes to obtain associated data, and is obtained based on the attribute data in the component data mapping result and the associated data.

7. The method according to claim 1, characterized in that The request to obtain the target quantity calculation model export includes: An export request of at least one target building model sent by a collaborative platform is received, where the target building model is uploaded by a design end through the collaborative platform.

8. A device for deriving a quantity calculation model, characterized in that: Applied to the cloud computing, the device includes: An acquisition module, configured to acquire an export request of a target quantity calculation model, wherein the export request includes an identifier of a target work unit in a target building model; a determination module, configured to determine a target work unit based on an identifier of the target work unit; A query module, configured to query a quantity mapping rule corresponding to a component to be exported in the target work unit, wherein the quantity mapping rule is used to indicate a correspondence between component information and quantity information, and the quantity mapping rule is used to determine a quantity mapping method for the component information; A mapping module is used to obtain component information of the components to be exported from the target building model, and map the component information based on the quantity calculation mapping rule to determine the target quantity calculation model.

9. An electronic device, characterized in that: include: A memory and a processor, wherein the memory and the processor are communicatively connected to each other, computer instructions are stored in the memory, and the processor executes the method for deriving the quantity calculation model according to any one of claims 1 to 7 by executing the computer instructions.

10. A computer-readable storage medium, characterized in that The computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a computer to execute the method for deriving the quantity calculation model according to any one of claims 1 to 7.

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