A data statistics and processing method, device and equipment and readable storage medium

Abstract

The application discloses a data statistics and processing method, device and equipment and a readable storage medium. The method comprises the following steps: obtaining a to-be-counted model, and obtaining instance objects under a target category from the to-be-counted model; forming an initial statistical table according to attribute values of each instance object under each attribute item; wherein each row of the initial statistical table represents an instance object, and each column represents an attribute item; obtaining a preset merging rule, and merging multiple rows in the initial statistical table into one row according to the merging rule to form an intermediate statistical table; and merging columns corresponding to attribute items with the same semantics into one column according to semantics of each attribute item in the intermediate statistical table to form a final statistical table. The application can realize the counting of the use amount data of different categories of components in multiple building models, and can intelligently process the counted use amount data.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of data processing, in particular to a data statistics and processing method, device and equipment and readable storage medium. BACKGROUND

[0002] When the building design work is completed, the quantity data of various building components need to be counted according to the design drawings; for example, the number of doors and windows, the length of the skirting, the area of use, the volume of the thermal insulation layer, etc. In the actual business scenario, a building is completed by multiple designers, and only one designer summarizes the results of other designers to combine all the statistical data into one statistical table. The existing building design platform requires designers to read information from drawings and manually calculate the quantity and manually draw the statistical table. Although some plug-ins of the building design platform can support automatic statistics of the quantity data of specified components of a single floor, they cannot count the quantity data of specified floors and cannot count the quantity data of other components. In addition, the existing building design platform can only count the quantity data by component category, and the quantity data of different categories are independent of each other and cannot be combined and processed. Therefore, how to automatically count the quantity data of building components and more intelligently process the counted quantity data has become a technical problem that needs to be solved by those skilled in the art. SUMMARY

[0003] The purpose of the present application is to provide a data statistics and processing method, device, equipment and readable storage medium, which can count the quantity data of different categories of components in multiple building models and intelligently process the counted quantity data.

[0004] According to one aspect of the present application, a data statistics and processing method is provided, which comprises:

[0005] Obtaining a model to be counted, and obtaining instance objects under a target category from the model to be counted;

[0006] According to the attribute value of each instance object under each attribute item, an initial statistical table is formed; each row of the initial statistical table represents an instance object, and each column represents an attribute item;

[0007] A preset merging rule is obtained, and multiple rows in the initial statistical table are merged into one row according to the merging rule to form an intermediate statistical table;

[0008] According to the semantics of each attribute item in the intermediate statistical table, the columns corresponding to the attribute items with the same semantics are merged into one column to form a final statistical table.

[0009] Optionally, the obtaining the to-be-counted models and obtaining instance objects under a target category from the to-be-counted models comprises:

[0010] obtaining one or more to-be-counted models from a local model library and / or a cloud model library;

[0011] obtaining a preset category number of each target category and obtaining all instance objects associated with each preset category number from all to-be-counted models.

[0012] Optionally, the forming an initial counting table according to attribute values of each instance object under each attribute item comprises:

[0013] determining attribute items corresponding to each target category;

[0014] obtaining attribute values of each instance object under each attribute item corresponding to each target category;

[0015] forming the initial counting table according to all instance objects under all target categories and attribute values of each instance object under the corresponding attribute item.

[0016] Optionally, the obtaining a preset merging rule and merging multiple rows in the initial counting table into one row according to the merging rule to form an intermediate counting table comprises:

[0017] analyzing a target attribute item contained in the merging rule;

[0018] determining instance objects having the same attribute value under the target attribute item from the initial counting table;

[0019] merging rows corresponding to the determined instance objects having the same attribute value into one row to form the intermediate counting table.

[0020] Optionally, after the obtaining a preset merging rule and merging multiple rows in the initial counting table into one row according to the merging rule to form an intermediate counting table, the method further comprises:

[0021] counting a number of the instance objects having the same attribute value within a preset counting range and adding the counted number as an attribute value of an additional attribute item to the intermediate counting table.

[0022] Optionally, the merging columns corresponding to attribute items having the same semantics into one column according to semantics of each attribute item in the intermediate counting table to form a final counting table comprises:

[0023] obtaining a preset semantic number of each attribute item in the intermediate counting table;

[0024] Determine attribute items with same preset semantic number under different target categories from the intermediate statistical table;

[0025] Display all attribute values in columns corresponding to the determined attribute items with same preset semantic number through a column.

[0026] To achieve the above object, the present application further provides a data statistics and processing device, which specifically comprises the following components:

[0027] An acquisition module is configured to acquire a to-be-processed model and acquire instance objects under a target category from the to-be-processed model;

[0028] An initial module is configured to form an initial statistical table according to attribute values of each instance object under each attribute item; each row of the initial statistical table represents an instance object, and each column represents an attribute item;

[0029] An intermediate module is configured to acquire a preset merging rule and merge multiple rows in the initial statistical table into one row according to the merging rule to form an intermediate statistical table;

[0030] A final module is configured to merge columns corresponding to attribute items with same semantic into one column according to semantics of each attribute item in the intermediate statistical table to form a final statistical table.

[0031] Optionally, the acquisition module is configured to:

[0032] acquire one or more to-be-processed models from a local model library and / or a cloud model library;

[0033] acquire preset category numbers of each target category and all instance objects associated with each preset category number from all to-be-processed models.

[0034] To achieve the above object, the present application further provides a computer device, which specifically comprises a memory, a processor and a computer program stored in the memory and executable on the processor; the processor executes the computer program to realize steps of the above-mentioned data statistics and processing method.

[0035] To achieve the above object, the present application further provides a computer readable storage medium, which stores a computer program; the computer program is executed by a processor to realize steps of the above-mentioned data statistics and processing method.

[0036] The data statistical processing method, device, equipment and readable storage medium provided by the present application can count the use data of instance objects of different categories in multiple building models, and display the attribute information of each instance object under different categories through an initial statistical table. In addition, multiple rows in the initial statistical table can be combined into one row according to a preset combination rule, so as to combine multiple instance objects with the same attribute information. In addition, multiple columns corresponding to attribute items with the same semantics can be combined into one column by identifying the semantics of the attribute items, so as to simplify the statistical table and form a statistical table meeting the needs of a business scenario. BRIEF DESCRIPTION OF DRAWINGS

[0037] Various other advantages and benefits will become apparent to those of ordinary skill in the art upon reading the following detailed description of the preferred embodiments. The accompanying drawings are included to provide a description of the preferred embodiments and are not intended to limit the scope of the present application. Moreover, the same reference numerals are used throughout the same figures. In the drawings:

[0038] Figure 1 An optional flowchart of the data statistical processing method provided for embodiment one;

[0039] Figure 2 An optional schematic diagram of parameter setting of a user in the process of creating a door and window statistical table in embodiment one;

[0040] Figure 3 An optional schematic diagram of a final statistical table generated in a door and window scenario in embodiment one

[0041] Figure 4 An optional component structure schematic diagram of the data statistical processing device provided for embodiment two;

[0042] Figure 5 An optional hardware architecture schematic diagram of the computer equipment provided for embodiment three. DETAILED DESCRIPTION

[0043] In order to make the objectives, technical solutions and advantages of the present application clearer, the present application is further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative labor fall within the scope of the present application.

[0044] Embodiment one

[0045] The embodiment of the present application provides a data statistical processing method, as shown in the figure, which specifically includes the following steps: Figure 1 ​

[0046] Step S101: Obtain a to-be-counted model, and obtain instance objects under a target category from the to-be-counted model.

[0047] The to-be-counted model is one or more building information models (BIMs), and the target category at least includes one of the following: a building component category (for example, a door, a window, a wall, a beam, or a column), a space component category (for example, a room or a fire compartment), or a building concept category (for example, a project or a drawing).

[0048] Specifically, step S101 includes the following steps.

[0049] Step A1: Obtain one or more to-be-counted models from a local model library and / or a cloud model library.

[0050] In this embodiment, a user is allowed to obtain multiple building information models from the local model library and the cloud model library to count the usage data in the multiple building information models at one time.

[0051] Step A2: Obtain a preset category number of each target category, and obtain all instance objects associated with each preset category number from all to-be-counted models.

[0052] In this embodiment, a user can select one or more target categories that need to be counted from a plurality of categories according to a requirement, for example, as shown in FIG. 2, a user selects to count doors and windows by checking the doors and the windows in the selected categories, to count the usage data of the doors and the windows in all building information models at the same time. Figure 2 In this embodiment, a category number for uniquely identifying each category is preset, and each instance object in a building information model is associated with a corresponding category number. Because instance objects belonging to the same category in different building information models are associated with the same category number, all instance objects belonging to the same target category can be obtained from multiple to-be-counted models by using the preset category number.

[0053] For example, when the target categories are doors and windows, and the preset category number of the doors is “51310900” and the preset category number of the windows is “51311000”, all door instance objects with the preset category number “51310900” are obtained from all to-be-counted models, and all window instance objects with the preset category number “51311000” are obtained from all to-be-counted models.

[0054] It should be noted that the instance object in the embodiment can be an instance object under the building component category, for example, an instance object under the door, window, wall, beam, column category; the instance object can also be an instance object under the space component category, for example, an instance object under the specified room, specified fire compartment; the instance object can also be an instance object under the building concept category, for example, an instance object under the specified project, specified drawing; therefore, compared with the building design platform in the prior art, the embodiment can count more types of objects, and in addition, by using the category number, the statistical process in the embodiment is also more concise.

[0055] Further, after step S101, the method further comprises:

[0056] Obtaining a filtering rule, filtering all obtained instance objects according to the filtering rule, and executing step S102 according to the filtered instance objects.

[0057] In the embodiment, the user can set a filtering rule according to the statistical requirement to only count the use data in the specified range and filter out the use data in other ranges, so as to more targetedly perform data statistics. For example, only the instance objects on the specified floor are counted, and the instance objects on other floors are filtered out.

[0058] Step S102: forming an initial statistical table according to the attribute values of each instance object under each attribute item; wherein each row of the initial statistical table represents an instance object, and each column represents an attribute item.

[0059] In the embodiment, a plurality of attribute items corresponding to each category are set in advance, and in step S102, the attribute values of the instance objects under each attribute item corresponding to the category to which the instance object belongs are obtained from the building information model, and all instance objects under each target category are displayed through an initial statistical table. attribute values of each target category corresponding to each attribute item.

[0060] Specifically, step S102 comprises:

[0061] Step B1: determining the attribute item corresponding to each target category;

[0062] Step B2: obtaining the attribute values of each instance object under the corresponding attribute item in each target category;

[0063] Step B3: forming the initial statistical table according to all instance objects under all target categories and the attribute values of each instance object under the corresponding attribute item.

[0064] All instance objects under all target categories are displayed through all rows of the initial statistical table, and all attribute items under all target categories are displayed through all columns of the initial statistical table.

[0065] It should be noted that, since a plurality of attribute items are set for each category in advance, in step S102, the user can select the specified attribute item to be counted from all the attribute items corresponding to the target category according to the requirement, so that only the attribute value of the target category under the specified attribute item needs to be obtained in step S102, and the attribute value of the target category under the non-specified attribute does not need to be obtained. For example, as shown in FIG. 6, when the user needs to generate a door and window statistics table, the user can select one or more selected fields (i.e., the specified attribute item) from the selectable fields (i.e., the attribute item) corresponding to the door and window, and arrange the selected fields in the order of checking. Figure 2 Figure 2 As shown in FIG. 6, the user checks the selectable fields: category name, type name, width, height, quantity, and note in turn from the selectable fields: serial number, quantity, note, category name, component name, type name, height, width, frame thickness, and frame width.

[0066] Step S103: Obtain a preset merging rule, and merge a plurality of rows in the initial statistics table into one row according to the merging rule to form an intermediate statistics table.

[0067] Since the attribute information of all the obtained instance objects is detailed in the initial statistics table, the amount of data contained in the initial statistics table is large; however, many instance objects have the same attribute information, so in order to simplify the statistics table and reduce the number of statistics, in the embodiment, the user can set a merging rule according to the requirement in advance, so as to merge a plurality of rows with the same attribute information in the initial statistics table into one row through the merging rule; for example, the plurality of rows corresponding to the instance objects with the same type name or the same type number can be merged into one row through the merging rule; or the plurality of rows corresponding to the instance objects with the same size information can be merged into one row. It should be noted that there are different types of instance objects under the same category, for example, there are different types of door instances such as solid wood door, glass door, sliding door, and rolling door under the door category.

[0068] Specifically, step S103 includes:

[0069] Step C1: Analyzing the target attribute item contained in the merging rule;

[0070] Step C2: Determining the instance objects with the same attribute value under the target attribute item from the initial statistics table;

[0071] Step C3: Merging the rows corresponding to the determined instance objects with the same attribute value into one row to form an intermediate statistics table.

[0072] ​In the embodiment, the merging rule includes one or more target attribute items; multiple instance objects having the same attribute value under the target attribute item are merged into one row for statistics by the merging rule, for example, when the target attribute item is "type name", if the type names of multiple instance objects are the same, the multiple instance objects are merged.

[0073] It should be noted that merging the multiple rows corresponding to the determined instance objects having the same attribute value into one row is to fuse the attribute values of the multiple rows under the same attribute item to form one attribute value.

[0074] Further, after the rows corresponding to the determined instance objects having the same attribute value are merged into one row to form the intermediate statistical table, the method further includes:

[0075] The number of the instance objects having the same attribute value is counted in a preset statistical range, and the counted number is added to the intermediate statistical table as an attribute value of an additional attribute item.

[0076] Preferably, the preset statistical range is a specified floor or a specified project; for example, Figure 2 As shown, when the user needs to generate a door and window statistical table, the user can determine whether to merge objects of the same type at the selection of the merging rule, and when the user needs to merge objects of the same type, the user can also count the number of objects of the same type in a specified range according to the project or the floor; for example, when counting the number according to the floor, the number of doors of the same type or the number of windows of the same type on each floor from the first floor to the eighth floor can be counted respectively to form a door and window statistical table as shown. Figure 3 As shown, the door and window statistical table can show the number of various types of doors and windows on each floor.

[0077] In the existing building design platform, it is difficult to count the number according to the project or the floor when counting the number (number, length, area, volume); the embodiment allows the user to select whether to count the number of instance objects of the same type according to the floor or the project by refining the merging rule, thereby compressing the statistical table space and being more consistent with the actual business scenario.

[0078] Further, the merging rule further includes an additional attribute item.

[0079] After the rows corresponding to the determined instance objects having the same attribute value are merged into one row to form the intermediate statistical table, the method further includes:

[0080] The additional attribute item is added to the intermediate statistical table, and the attribute value of each row under the additional attribute item in the intermediate statistical table is determined.

[0081] Preferably, the additional attribute item includes at least one of the following: total quantity, total length, total area, and total volume.

[0082] In the embodiment, the merging rule includes one or more target attribute items and one or more additional attribute items; multiple instance objects having the same attribute value under the target attribute item are merged in a row for statistics according to the merging rule, and the additional attribute item is automatically added in the initial statistical table. For example, when the target attribute item is “type name”, if the type names of multiple instance objects are the same, the multiple instance objects are merged, and the additional attribute item “total quantity” is added in the row formed after the merging, and the value of the total quantity is automatically counted.

[0083] Step S104: according to the semantics of each attribute item in the intermediate statistical table, columns corresponding to attribute items having the same semantics are merged into one column to form a final statistical table.

[0084] In the existing building design platform, the use amount data is counted by category, and different category attribute items are displayed through different columns, and the attribute values of multiple instance objects of different categories cannot be displayed in the same column. However, in actual applications, many attribute items of different categories have the same semantics, and the attribute values of instance objects of different categories need to be displayed through one column in a business scenario. For example, in the scenario of forming a door and window statistical table, the attribute items of the door include width and height, and the attribute items of the window also include width and height. If the existing building design platform rule is followed, the width of the door and the width of the window are displayed through different columns. However, in the door and window scenario, the width of the door and the width of the window need to be displayed through one column, and the height of the door and the height of the window also need to be displayed through one column. Therefore, the embodiment can merge multiple columns corresponding to attribute items having the same semantics under different target categories into one column, so as to simplify the size of the statistical table and meet the needs of the business scenario.

[0085] Specifically, step S104 includes:

[0086] Step D1: obtaining a preset semantic number of each attribute item in the intermediate statistical table;

[0087] In the embodiment, a corresponding semantic number is set for each attribute item of each category in advance, and if the semantic numbers of multiple attribute items of different categories are the same, it means that the semantics of the multiple attribute items are the same. It should be noted that the semantic coding of the attribute item is defined by the data standard, and is associated with the semantics in reality.

[0088] Step D2: determining attribute items having the same preset semantic number under different target categories from the intermediate statistical table;

[0089] Step D3: all attribute values in the column corresponding to the attribute item with the same preset semantic number are displayed by a column.

[0090] For example, as shown in FIG. 6, the width and height of doors and windows are displayed by a column. Figure 3

[0091] It should be noted that in step S103, in the process of merging multiple rows into one row, multiple attribute values of each instance object under the same attribute item are fused into one attribute value to achieve the effect of merging multiple rows into one row; in step S104, in the process of merging multiple columns into one column, no data fusion is performed on the attribute values under the multiple columns, but all attribute values under the multiple columns are displayed by a column. That is, in the process of merging multiple rows into one row, the attribute values may be changed, but in the process of merging multiple columns into one column, the attribute values are not changed.

[0092] The embodiment can count the quantity data of instance objects of different categories in multiple building information models, and display attribute information of each instance object under different categories through an initial statistical table; in addition, multiple rows in the initial statistical table can be merged into one row according to a preset merging rule to merge multiple instance objects with the same attribute information; in addition, multiple columns corresponding to attribute items with the same semantic can be merged into one column by identifying the semantics of the attribute items, so as to simplify the statistical table and form a statistical table meeting the needs of a business scenario.

[0093] Embodiment two

[0094] The embodiment of the application provides a data statistics and processing device, as shown in FIG. 6, which specifically comprises the following components: Figure 4 The acquisition module 401 is configured to acquire a to-be-counted model and acquire instance objects under a target category from the to-be-counted model.

[0095] The initial module 402 is configured to form an initial statistical table according to attribute values of each instance object under each attribute item; each row of the initial statistical table represents an instance object, and each column represents an attribute item.

[0096] The intermediate module 403 is configured to acquire a preset merging rule, and merge multiple rows in the initial statistical table into one row according to the merging rule to form an intermediate statistical table.

[0097] The final module 404 is configured to merge columns corresponding to attribute items with the same semantic into one column according to the semantics of each attribute item in the intermediate statistical table to form a final statistical table.

[0098]

[0099] ​​Specifically, the acquisition module 401 is configured to:

[0100] acquire one or more to-be-counted models from a local model library and / or a cloud model library;

[0101] acquire a preset category number of each target category, and acquire all instance objects associated with each preset category number from all to-be-counted models.

[0102] Further, the initial module 402 is configured to:

[0103] determine an attribute item corresponding to each target category;

[0104] acquire an attribute value of each instance object in the corresponding attribute item under each target category;

[0105] form the initial statistical table according to all instance objects under all target categories and the attribute value of each instance object in the corresponding attribute item.

[0106] Further, the intermediate module 403 is configured to:

[0107] parse a target attribute item contained in the merging rule;

[0108] determine, from the initial statistical table, instance objects having the same attribute value under the target attribute item;

[0109] merge rows corresponding to the determined instance objects having the same attribute value into one row to form an intermediate statistical table.

[0110] Further, the intermediate module 403 is further configured to:

[0111] count the number of the instance objects having the same attribute value within a preset statistical range, and add the counted number as an attribute value of an additional attribute item to the intermediate statistical table.

[0112] Still further, the final module 404 is configured to:

[0113] acquire a preset semantic number of each attribute item in the intermediate statistical table;

[0114] determine, from the intermediate statistical table, attribute items having the same preset semantic number under different target categories;

[0115] display all attribute values in columns corresponding to the determined attribute items having the same preset semantic number through one column.

[0116] Embodiment Three

[0117] The embodiment also provides a computer device, such as a smart phone, a tablet computer, a notebook computer, a desktop computer, a rack server, a blade server, a tower server, or a cabinet server (including a single server or a server cluster composed of multiple servers), which can execute programs. Figure 5 As shown in the figure, the computer device 50 of the embodiment at least includes, but is not limited to, a memory 501 and a processor 502 which can be connected to each other through a system bus. Figure 5 Only the computer device 50 with the components 501-502 is shown, but it should be understood that all the shown components are not required to be implemented, and more or fewer components can be alternatively implemented.

[0118] In the embodiment, the memory 501 (i.e., a readable storage medium) includes a flash memory, a hard disk, a multimedia card, a card-type memory (e.g., an SD or DX memory, etc.), a random access memory (RAM), a static random access memory (SRAM), a read-only memory (ROM), an electrically erasable programmable read-only memory (EEPROM), a programmable read-only memory (PROM), a magnetic memory, a magnetic disk, an optical disk, etc. In some embodiments, the memory 501 can be an internal storage unit of the computer device 50, such as a hard disk or a memory of the computer device 50. In other embodiments, the memory 501 can also be an external storage device of the computer device 50, such as a plug-in hard disk, a smart media card (SMC), a secure digital (SD) card, a flash card, etc. equipped on the computer device 50. Of course, the memory 501 can include both the internal storage unit and the external storage device of the computer device 50. In the embodiment, the memory 501 is generally used to store an operating system and various application software installed on the computer device 50. In addition, the memory 501 can also be used to temporarily store various data that have been output or will be output.

[0119] The processor 502 can be a central processing unit (CPU), a controller, a microcontroller, a microprocessor, or other data processing chips in some embodiments. The processor 502 is generally used to control the overall operation of the computer device 50.

[0120] Specifically, in the embodiment, the processor 502 is configured to execute a program of a data statistical and processing method stored in the memory 501, and the program of the data statistical and processing method, when executed, implements the following steps:

[0121] obtaining a to-be-counted model, and obtaining an instance object under a target category from the to-be-counted model;

[0122] According to the attribute value of each instance object under each attribute item, an initial statistical table is formed; wherein each row of the initial statistical table represents an instance object, and each column represents an attribute item;

[0123] A preset merging rule is obtained, and according to the merging rule, multiple rows in the initial statistical table are merged into one row to form an intermediate statistical table;

[0124] According to the semantics of each attribute item in the intermediate statistical table, columns corresponding to attribute items with the same semantics are merged into one column to form a final statistical table.

[0125] The specific embodiment process of the above method steps can be referred to the first embodiment, which will not be repeated here.

[0126] Embodiment Four

[0127] The embodiment also provides a computer readable storage medium, such as a flash memory, a hard disk, a multimedia card, a card memory (for example, an SD or DX memory, etc.), a random access memory (RAM), a static random access memory (SRAM), a read only memory (ROM), an electrically erasable programmable read only memory (EEPROM), a programmable read only memory (PROM), a magnetic memory, a magnetic disk, an optical disk, a server, an App application mall, etc., which stores a computer program, and the computer program is executed by a processor to implement the following method steps:

[0128] An instance object under a target category is obtained from a to-be-counted model;

[0129] According to the attribute value of each instance object under each attribute item, an initial statistical table is formed; wherein each row of the initial statistical table represents an instance object, and each column represents an attribute item;

[0130] A preset merging rule is obtained, and according to the merging rule, multiple rows in the initial statistical table are merged into one row to form an intermediate statistical table;

[0131] According to the semantics of each attribute item in the intermediate statistical table, columns corresponding to attribute items with the same semantics are merged into one column to form a final statistical table.

[0132] The specific embodiment process of the above method steps can be referred to the first embodiment, which will not be repeated here.

[0133] It should be noted that, in this text, the term "comprising", "including" or any other variant thereof is intended to cover non-exclusive inclusion, so that the process, method, article or device including a series of elements not only includes those elements, but also includes other elements not explicitly listed, or includes elements inherent to such process, method, article or device. Without more limitations, the element defined by the statement "including a" does not exclude the presence of other identical elements in the process, method, article or device including the element.

[0134] The above-mentioned embodiment numbers of the application are only for description, not representing the advantages and disadvantages of the embodiments.

[0135] Through the above description of the embodiments, those skilled in the art can clearly understand that the above-mentioned embodiment methods can be realized by software and the necessary general hardware platform, of course, they can also be realized by hardware, but in many cases the former is a better embodiment.

[0136] The above is only the preferred embodiment of the application, and does not limit the patent scope of the application, and any equivalent structure or equivalent process transformation using the content of the application specification and drawings, or direct or indirect application in other related technical fields, are also included in the patent protection scope of the application.

Claims

1. A data statistics and processing method, characterized in that, The method comprises: acquiring a to-be-counted model by using an acquisition module, and acquiring instance objects under a target category from the to-be-counted model; wherein the to-be-counted model is a building information model, and different types of instance objects exist under the same target category; forming an initial statistical table according to attribute values of each instance object under each attribute item by using an initial module; wherein each row of the initial statistical table represents an instance object, and each column represents an attribute item; acquiring a preset merging rule by using an intermediate module, and merging multiple rows in the initial statistical table into one row according to the merging rule to form an intermediate statistical table; merging columns corresponding to attribute items with the same semantics into one column according to semantics of each attribute item in the intermediate statistical table by using a final module to form a final statistical table; wherein the step of acquiring the preset merging rule by using the intermediate module, and merging multiple rows in the initial statistical table into one row according to the merging rule to form the intermediate statistical table comprises: merging multiple rows corresponding to instance objects with the same type name into one row by using the merging rule, and counting the number of instance objects with the same type name to add the counted number as an attribute value of a first additional attribute item to the intermediate statistical table; wherein attribute values of multiple rows under the same attribute item that need to be merged are fused to form one attribute value; adding a second additional attribute item included in the merging rule to the intermediate statistical table, and determining attribute values of each row under the second additional attribute item in the intermediate statistical table; wherein the second additional attribute item at least includes one of the following: total number, total length, total area, and total volume; the step of merging columns corresponding to attribute items with the same semantics into one column according to semantics of each attribute item in the intermediate statistical table to form a final statistical table comprises: acquiring preset semantic numbers of each attribute item in the intermediate statistical table by using the final module; wherein a corresponding semantic number is set for each attribute item of each category in advance, and multiple attribute items with the same semantic number have the same semantics; determining attribute items with the same preset semantic number under different target categories from the intermediate statistical table by using the final module; displaying all attribute values in columns corresponding to the determined attribute items with the same preset semantic number by using the final module.

2. The data statistics and processing method of claim 1, wherein, the step of acquiring a to-be-counted model, and acquiring instance objects under a target category from the to-be-counted model comprises: acquiring one or more to-be-counted models from a local model library and / or a cloud model library; acquiring preset category numbers of each target category, and acquiring all instance objects associated with each preset category number from all to-be-counted models.

3. The method of claim 1, wherein, the step of forming an initial statistical table according to attribute values of each instance object under each attribute item comprises: determining attribute items corresponding to each target category; acquiring attribute values of each instance object under the corresponding attribute item under each target category; The initial statistical table is formed according to all instance objects under all target categories and attribute values of each instance object under corresponding attribute items.

4. The method of claim 1, wherein, The merging rule is obtained, and multiple rows in the initial statistical table are merged into one row according to the merging rule to form an intermediate statistical table. The target attribute item included in the merging rule is parsed out; Instance objects having the same attribute value under the target attribute item are determined from the initial statistical table; The rows corresponding to the determined instance objects having the same attribute value are merged into one row to form the intermediate statistical table.

5. A data statistics and processing apparatus, characterized by comprising: The device comprises: An obtaining module is configured to obtain a to-be-counted model and obtain instance objects under a target category from the to-be-counted model; the to-be-counted model is a building information model, and different types of instance objects exist under the same target category; An initial module is configured to form an initial statistical table according to attribute values of each instance object under each attribute item; each row of the initial statistical table represents an instance object, and each column represents an attribute item; An intermediate module is configured to obtain a preset merging rule and merge multiple rows in the initial statistical table into one row according to the merging rule to form an intermediate statistical table; A final module is configured to merge columns corresponding to attribute items having the same semantics into one column according to semantics of each attribute item in the intermediate statistical table to form a final statistical table; The intermediate module is configured to: merge multiple rows corresponding to instance objects having the same type name into one row through the merging rule, count the number of instance objects having the same type name, and add the counted number as an attribute value of a first additional attribute item to the intermediate statistical table; attribute values of multiple rows under the same attribute item that need to be merged are fused to form one attribute value; add a second additional attribute item included in the merging rule to the intermediate statistical table and determine attribute values of each row under the second additional attribute item in the intermediate statistical table; the second additional attribute item at least includes one of the following: total number, total length, total area, and total volume; The final module is configured to: obtain a preset semantic number of each attribute item in the intermediate statistical table; a corresponding semantic number is set for each attribute item of each category in advance, and multiple attribute items having the same semantic number have the same semantics; determine attribute items having the same preset semantic number under different target categories from the intermediate statistical table; all attribute values in columns corresponding to the determined attribute items having the same preset semantic number are displayed through one column.

6. The data statistics and processing apparatus according to claim 5, wherein, The obtaining module is configured to: obtain one or more to-be-counted models from a local model library and / or a cloud model library; obtain a preset category number of each target category and all instance objects associated with each preset category number from all to-be-counted models.

7. A computer device comprising: Memory, a processor, and a computer program stored on the memory and loadable on the processor, characterized in that the processor implements the steps of the method according to any one of claims 1 to 4 when executing the computer program.

8. A computer-readable storage medium having stored thereon a computer program, characterized in that The computer program is executed by the processor to implement the steps of the method according to any one of claims 1 to 4.

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