Equipment asset delivery data evaluation method and device, medium and electronic equipment
By obtaining classified attributes according to the device category, reading instantiated management data and performing diversified evaluation, the problem of low efficiency in evaluating equipment asset delivery data is solved, and the rapid and scientific evaluation of equipment asset delivery data is achieved.
Patent Information
- Application Number
- CN202510629943.9
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-05-16
- Publication Date
- 2025-06-27
AI Technical Summary
There is a lack of an effective and efficient method for evaluating equipment asset delivery data in the prior art, which makes it difficult to effectively evaluate the quality of equipment delivery data.
By obtaining its classification attributes according to the category of the target device, reading instantiated management data under the classification tree leaf node corresponding to the classification attribute, obtaining delivery data, and performing diversified evaluation, adjusting the index weight of the indicator score, and finally obtaining the evaluation score of the equipment.
It realizes rapid and scientific evaluation of equipment asset payment data, improves processing efficiency, avoids inefficiency in manual verification, and can more comprehensively reflect the overall quality of equipment delivery.
Smart Images
Figure CN120218753A_ABST
Abstract
Description
Technical Field
[0001] This application relates to the technical field of equipment management, and particularly relates to a method, device, medium and electronic device for evaluating equipment asset delivery data. Background Art
[0002] In the engineering field, digital delivery mainly occurs in the stage of engineering completion and transfer to operation, which is limited to the responsible unit of delivery management. When managing the quality of delivery data, the data catalog of the engineering entity is used as the inspection basis. Generally, text recognition is carried out through electronic scanning, and manual inspection is performed on the data converted into electronic form, mainly checking whether it is consistent with the catalog, whether the document content is complete and accurate, and whether it conforms to the document compilation specification, etc. Each verification work is carried out separately and the efficiency is low, and the verification results cannot reflect the overall quality of equipment delivery, making it difficult to effectively evaluate the delivery data. Summary of the Invention
[0003] The main purpose of this application is to provide a method, device, medium and electronic device for evaluating equipment asset delivery data, aiming to solve the problem that there is a lack of an effective and efficient method for evaluating equipment asset delivery data in the prior art.
[0004] To achieve the above object, the technical solutions adopted in the embodiments of this application are as follows:
[0005] In a first aspect, an embodiment of this application provides a method for evaluating equipment asset delivery data, including the following steps:
[0006] Obtain the classification attributes of the target equipment according to the category of the target equipment;
[0007] Under the leaf node of the classification tree corresponding to the classification attribute, read the instantiated management data of the target equipment to obtain the delivery data of the target equipment; wherein, the delivery data is used to represent the static attributes of the target equipment;
[0008] According to the delivery data, conduct a diversified evaluation of the target equipment to obtain index scores in multiple dimensions;
[0009] Adjust the index weights of the index scores according to the category of the target equipment, and obtain the evaluation score of the target equipment according to the index scores.
[0010] In a possible implementation manner of the first aspect, under the leaf node of the classification tree corresponding to the classification attribute, reading the instantiated management data of the target equipment to obtain the delivery data of the target equipment includes:
[0011] Under the leaf node of the classification tree corresponding to the classification attribute, read the instantiated management data of the target equipment;
[0012] Obtain a static attribute model according to the instantiated management data;
[0013] According to the static attribute model, read the static attributes of the target device to obtain the delivery data of the target device.
[0014] In a possible implementation of the first aspect, before obtaining the static attribute model according to the instantiation management data, the method further includes:
[0015] Register the object of the target device under the leaf node of the classification tree;
[0016] Perform spatial positioning on the object of the target device;
[0017] Fill the static attributes of the object of the target device according to the static attribute model;
[0018] Configure a data docking channel for the object of the target device to establish the instantiation management data of the target device.
[0019] In a possible implementation of the first aspect, performing spatial positioning on the object of the target device includes:
[0020] Obtain the reference point and three-dimensional point cloud data of the target device;
[0021] According to the application site of the target device, convert the reference point and three-dimensional point cloud data to coordinate systems with different precisions to perform spatial positioning on the object of the target device.
[0022] In a possible implementation of the first aspect, performing spatial positioning on the object of the target device includes:
[0023] According to the location area where the target device is located, perform building location coding on the target device according to the coding generation rule;
[0024] Embed the building location coding into the IfcSpace feature dimension of the BIM model and establish a spatial indexing rule to perform spatial positioning on the object of the target device.
[0025] In a possible implementation of the first aspect, the static attribute model includes:
[0026] The basic attribute model, which is used to characterize the inherent attributes of the device;
[0027] The status monitoring model, which is used to characterize the operation status data;
[0028] The performance index model, which is used to characterize the key performance indicators;
[0029] The maintenance record model, which is used to characterize the maintenance data.
[0030] In a possible implementation of the first aspect, the diversified evaluation includes:
[0031] Integrity assessment, used to characterize the filling rate of required fields;
[0032] Accuracy assessment, used to characterize the passing rate of data verification;
[0033] Timeliness assessment, used to characterize the data update latency;
[0034] Consistency assessment, used to characterize the matching degree of associated data;
[0035] Validity assessment, used to characterize the compliance rate of data value ranges.
[0036] In a second aspect, an embodiment of the present application provides a device asset delivery data evaluation device, including:
[0037] A classification module, configured to obtain classification attributes of a target device according to the category of the target device;
[0038] A reading module, configured to read instantiated management data of the target device under the leaf node of the classification tree corresponding to the classification attribute, and obtain delivery data of the target device; wherein, the delivery data is used to characterize static attributes of the target device;
[0039] An evaluation module, configured to perform diversified evaluation on the target device according to the delivery data, and obtain index scores in multiple dimensions;
[0040] An adjustment module, configured to adjust the index weights of the index scores according to the category of the target device, and obtain an evaluation score of the target device according to the index scores.
[0041] In a third aspect, an embodiment of the present application provides a computer-readable storage medium, storing a computer program, which when loaded and executed by a processor, implements the device asset delivery data evaluation method provided in any one of the above first aspects.
[0042] In a fourth aspect, an embodiment of the present application provides an electronic device, including a processor and a memory, wherein,
[0043] The memory is used to store a computer program;
[0044] The processor is used to load and execute the computer program, so that the electronic device executes the device asset delivery data evaluation method provided in any one of the above first aspects.
[0045] Compared with the prior art, the beneficial effects of the present application are:
[0046] A method, device, medium, and electronic device for evaluating device asset delivery data proposed in an embodiment of the present application. The method includes: obtaining classification attributes of a target device according to the category of the target device; reading instantiated management data of the target device under the leaf node of the classification tree corresponding to the classification attributes to obtain delivery data of the target device, where the delivery data is used to characterize static attributes of the target device; performing diversified evaluation on the target device according to the delivery data to obtain index scores in multiple dimensions; adjusting the index weights of the index scores according to the category of the target device, and obtaining an evaluation score of the target device according to the index scores. In this application, devices are managed in an instantiated manner, and the verification and evaluation of delivery data no longer rely on manual operations, which can improve processing efficiency and the evaluation is more scientific than manual evaluation. First, confirm its classification attributes through the category of the device, and only need to read the instantiated management data corresponding to the leaf node of the classification tree of the classification attributes to quickly obtain the delivery data of the device. These delivery data are used to characterize static attributes with strong universality, and these static data are also common attributes of most devices, which can use artificial intelligence algorithms to quickly complete the verification and evaluation in multiple dimensions. Finally, adjust the weights of the index score fusion according to the differences in device categories to obtain an evaluation score with more comprehensive characterization ability, realizing the overall effective evaluation of device asset delivery data. Description of the Drawings
[0047] Figure 1 Schematic structural diagram of an electronic device for the hardware operating environment involved in an embodiment of the present application;
[0048] Figure 2 Schematic flowchart of the method for evaluating device asset delivery data provided by an embodiment of the present application;
[0049] Figure 3 Schematic module diagram of the device for evaluating device asset delivery data provided by an embodiment of the present application;
[0050] Reference numerals in the figure: 101 - processor, 102 - communication bus, 103 - network interface, 104 - user interface, 105 - memory. Detailed Embodiment
[0051] 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.
[0052] Refer to the attached Figure 1 attachment, the attached Figure 1Schematic diagram of the electronic device structure for the hardware operating environment involved in the solution of the embodiment of the present application. The electronic device may include: a processor 101, such as a central processing unit (CPU), a communication bus 102, a user interface 104, a network interface 103, and a memory 105. Among them, the communication bus 102 is used to realize the connection and communication between these components. The user interface 104 may include a display screen (Display) and an input unit such as a keyboard (Keyboard). Optionally, the user interface 104 may further include a standard wired interface and a wireless interface. The network interface 103 may optionally include a standard wired interface and a wireless interface (such as a wireless fidelity (WI-FI) interface). The memory 105 may optionally be a storage device independent of the aforementioned processor 101. The memory 105 may be a high-speed random access memory (Random Access Memory, RAM) or a stable non-volatile memory (Non-Volatile Memory, NVM), such as at least one disk memory; the processor 101 may be a general-purpose processor, including a central processor, a network processor, etc., or may also be a digital signal processor, an application-specific integrated circuit, a field programmable gate array, or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components.
[0053] Those skilled in the art can understand that the structure shown in the appendix Figure 1 does not constitute a limitation on the electronic device, and may include more or fewer components than shown in the figure, or combine certain components, or have different component arrangements.
[0054] As shown in the appendix Figure 1 the memory 105, as a storage medium, may include an operating system, a network communication module, a user interface module, and a device asset delivery data evaluation device.
[0055] In the electronic device shown in the appendix Figure 1 the network interface 103 is mainly used for data communication with a network server; the user interface 104 is mainly used for data interaction with a user; in the present application, the processor 101 and the memory 105 may be provided in the electronic device. The electronic device calls the device asset delivery data evaluation device stored in the memory 105 through the processor 101 and executes the device asset delivery data evaluation method provided by the embodiment of the present application.
[0056] Referring to the appendix Figure 2 based on the hardware device of the foregoing embodiment, an embodiment of the present application provides a method for evaluating device asset delivery data, including the following steps:
[0057] S10: Obtain the classification attributes of the target device according to the category of the target device.
[0058] In the specific implementation process, the target device is the device asset to be delivered. A classification system can be designed in advance for all relevant devices, and a unique classification code can be determined. For example, a classification architecture of large category - medium category - small category can be established according to three - level classification. The large category is divided according to the device function field, such as power equipment, power transmission and transformation equipment, control equipment, etc.; the medium category is divided according to the device professional attributes, such as transformer category, switch category, motor category, etc.; the small category is divided according to the specific device model and technical specifications, etc. Structurally coded in the form of three - level classification, then each device corresponds to a classification attribute. For example, power equipment - switch category - specific model and number. Usually, each classification will be expressed in letters, numbers, etc., so the aforementioned classification attribute can be expressed as P - K - LS001.
[0059] S20: Read the instantiated management data of the target device under the leaf node of the classification tree corresponding to the classification attribute to obtain the delivery data of the target device; wherein, the delivery data is used to characterize the static attributes of the target device.
[0060] In the specific implementation process, the classification attribute is unique. In the way of tree - shaped classification, there will be a unique corresponding leaf node of the classification tree. The instantiated management data of the corresponding device is stored under this node. The delivery data established with static data is stored in the instantiated management data. Each time of delivery, there is no need for manual scanning of materials, and it can be retrieved from the pre - established classification tree. And the generated delivery data only needs to be created and entered into the classification tree in advance.
[0061] In one embodiment, reading the instantiated management data of the target device under the leaf node of the classification tree corresponding to the classification attribute to obtain the delivery data of the target device includes:
[0062] Read the instantiated management data of the target device under the leaf node of the classification tree corresponding to the classification attribute;
[0063] Obtain the static attribute model according to the instantiated management data;
[0064] Read the static attributes of the target device according to the static attribute model to obtain the delivery data of the target device.
[0065] In the specific implementation process, since the delivery data to be read characterizes the static attributes, and the static attributes are data with strong universality of the device. When evaluating, a set of evaluation systems can be used to complete evaluations in different dimensions. The index data of each static attribute is stored in a static attribute model, and the corresponding static attribute data can be retrieved from the corresponding static attribute model when reading. That is, the static attribute model includes:
[0066] The basic attribute model is used to represent the inherent attributes of the device, such as model number, manufacturer, factory date, etc.;
[0067] The status monitoring model is used to represent the operation status data, such as temperature, pressure, vibration, etc.;
[0068] The performance index model is used to represent the key performance indicators, such as efficiency, energy consumption, output, etc. KPIs;
[0069] The maintenance record model is used to represent the maintenance data, such as maintenance, inspection, fault records, etc.
[0070] In one embodiment, before obtaining the static attribute model according to the instantiated management data, the method further includes:
[0071] Register the object of the target device under the leaf node of the classification tree;
[0072] Perform spatial positioning on the object of the target device;
[0073] Fill in the static attributes of the object of the target device according to the static attribute model;
[0074] Configure a data docking channel for the object of the target device to establish the instantiated management data of the target device.
[0075] In the specific implementation process, during the instantiation of the device assets, first create the structure of the classification tree. The branches represent the subdivision from the large category to the small category. Each device corresponds to a code marked to the smallest category, which corresponds to the smallest classification unit of the classification tree, that is, its leaf node. First, create an instance under the corresponding leaf node to complete object registration. After registration, perform spatial positioning to express the spatial location of the device. Then, fill in the attributes according to the requirements of the static attribute model, enter the corresponding data into the registered object. Finally, configure a data docking channel for the registered object to achieve accurate data transmission and invocation. Through the above implementation method, the instantiated management of the target device is completed.
[0076] In one embodiment, performing spatial positioning on the object of the target device includes:
[0077] Obtain the reference point and three-dimensional point cloud data of the target device;
[0078] According to the application site of the target device, convert the reference point and three-dimensional point cloud data to coordinate systems with different precisions to perform spatial positioning on the object of the target device.
[0079] In the specific implementation process, for some equipment assets, obtaining the location information is very important. It may be related to the layout of other relevant equipment and also affect wiring, network construction, etc. Therefore, spatial positioning of the target equipment is an important task during instantiation management. In the embodiments of the present application, a reference point and three-dimensional point cloud data are first obtained. The reference point is the point position of the entire equipment fitting. For example, a total station or RTK surveying equipment is used to measure the reference point of the equipment, which is used to represent the equipment position with point data. The three-dimensional point cloud data can be obtained according to the design drawing of the equipment or a laser scanning device. By improving the accuracy of the spatial data, the accuracy of the position representation of the equipment itself is improved, and then different precision coordinate system conversions are performed according to different application sites. For example, outdoor equipment does not require overly high-precision positioning, and the global coordinate system can be used to convert the reference point and three-dimensional point cloud data into longitude, latitude, and elevation for expression; for indoor equipment such as precise positioning in a factory area or a park area, the local engineering coordinate system can be used to convert the reference point and three-dimensional point cloud data to the accurate position in the coordinate system.
[0080] In one embodiment, spatial positioning of the object of the target equipment includes:
[0081] Encoding the building position of the target equipment according to the encoding generation rule according to the location area where the target equipment is located;
[0082] Embedding the building position code into the IfcSpace feature dimension of the BIM model and establishing a spatial indexing rule to perform spatial positioning on the object of the target equipment.
[0083] In the specific implementation process, for the equipment assets involved in the embodiments of the present application, most of them are applicable to indoor situations. Therefore, a more accurate equipment position positioning method is provided. Using the BIM model, that is, the building information model, with a three-dimensional model as the core, integrating data such as geometric information, physical properties, and engineering rules of a building project to support full life cycle management. First, a unique position code is generated according to the location area of the equipment, and a multi-level position code is adopted, such as the mode of factory area level - building level - room level - equipment level. The factory area level is like the park number P006, the building level is like the building number + floor, such as B11 F14, the room level is like the room number + sub-area, such as R001A01, and the equipment level is like the installation position code, L01XXXX. The encoding generation rule is the multi-level position encoding rule described above, and encoding is performed according to the location area level where the target equipment is located.
[0084] Using the BIM model as the query scope and embedding the building location code of the device to be located and queried into it, the device location can be located in the BIM model using the spatial indexing rules. It should be noted that the building location code is embedded in the IfcSpace feature dimension of the BIM model, which is different from the IfcZone feature dimension of the BIM model. The IfcSpace feature dimension has a clear geometric boundary definition, such as walls and floors in building information, which can delimit the actual physical space. The IfcZone feature dimension is a logical set without physical boundaries and is mainly used for functional differentiation, such as delimiting safety areas and responsibility areas. In actual use, if the location of the device in the IfcZone feature dimension needs to be located, the building location code can also be embedded in the IfcZone feature dimension of the BIM model.
[0085] S30: According to the delivery data, conduct a diversified evaluation of the target device to obtain the index scores under multiple dimensions.
[0086] In the specific implementation process, the delivery data is obtained, which is the representation of the static attributes of the target device, and a diversified evaluation can be carried out on it, that is, independent evaluation based on the index data under different dimensions. To make the evaluation of static attributes more comprehensive, the diversified evaluation can include integrity evaluation, accuracy evaluation, timeliness evaluation, consistency evaluation, and effectiveness evaluation. Among them, the integrity evaluation is used to represent the filling rate of required fields, the accuracy evaluation is used to represent the passing rate of data verification, the timeliness evaluation is used to represent the data update delay, the consistency evaluation is used to represent the matching degree of associated data, and the effectiveness evaluation is used to represent the compliance rate of data value ranges.
[0087] S40: Adjust the index weights of the index scores according to the category of the target device, and obtain the evaluation score of the target device based on the index scores.
[0088] In the specific implementation process, after independently evaluating the indexes under each dimension, multiple index scores are obtained, and then their fusion is considered with different weight distributions. The weight distribution is adjusted according to the category of the device. For example, the results of timeliness evaluation and consistency evaluation are more important for devices belonging to category A, and more weights are considered to be assigned during fusion. For example, if the index scores of the above five dimensions are calculated on a percentile basis, the evaluation score is also calculated on a percentile basis, and the sum of the index weights assigned to the index data of the five dimensions is 1. The index weights of integrity evaluation, accuracy evaluation, timeliness evaluation, consistency evaluation, and effectiveness evaluation can be assigned as 0.1, 0.1, 0.3, 0.4, and 0.1.
[0089] This embodiment mainly describes the evaluation part of static attributes. In actual use, there may be some special attribute parts of the target device that need to be included in the evaluation. During the instantiation management process, a new extended model parallel to the static attribute model can be added at the establishment of the static attribute model. The static attribute model is used to define common attributes, while the extended model is used to define the special attributes of the device. According to the extension of the model, subsequent evaluations only need to evaluate one more index characterized by special attributes, and then fuse the index scores by assigning index weights according to actual needs.
[0090] In this embodiment, the device is managed through instantiation. The verification and evaluation of the delivered data no longer rely on manual operation, which can improve the processing efficiency and the evaluation is more scientific than manual operation. First, the classification attributes of the device are confirmed through its category, and only the instantiation management data under the corresponding leaf nodes of the classification tree of this classification attribute need to be read correspondingly to quickly obtain the delivered data of the device. These delivered data are used to represent static attributes with strong universality, and these static data are also attributes common to the vast majority of devices. The verification and evaluation can be quickly completed in multiple dimensions using artificial intelligence algorithms. Finally, the weights of the index score fusion are adjusted according to the different device categories to obtain a more comprehensive evaluation score representing the ability, realizing the overall effective evaluation of the device asset delivery data.
[0091] Refer to the appendix Figure 3 , based on the same inventive concept as the foregoing embodiment, the embodiment of the present application also provides an evaluation device for device asset delivery data, including:
[0092] A classification module, configured to obtain the classification attributes of the target device according to the category of the target device;
[0093] A reading module, configured to read the instantiation management data of the target device under the leaf nodes of the classification tree corresponding to the classification attributes to obtain the delivered data of the target device; wherein, the delivered data is used to represent the static attributes of the target device;
[0094] An evaluation module, configured to perform a diversified evaluation on the target device according to the delivered data to obtain index scores in multiple dimensions;
[0095] An adjustment module, configured to adjust the index weights of the index scores according to the category of the target device, and obtain the evaluation score of the target device according to the index scores.
[0096] Those skilled in the art should understand that the division of each module in the embodiments is only a division of logical functions. In actual applications, they can be fully or partially integrated into one or more actual carriers, and these modules can all be implemented in the form of software called by a processing unit, or all be implemented in the form of hardware, or be implemented in the form of a combination of software and hardware. It should be noted that each module in the device asset delivery data evaluation device in this embodiment corresponds one by one to each step in the device asset delivery data evaluation method in the foregoing embodiment. Therefore, the specific implementation manner of this embodiment can refer to the implementation manner of the foregoing device asset delivery data evaluation method, and will not be elaborated here.
[0097] Based on the same inventive concept as in the foregoing embodiment, an embodiment of the present application further provides a computer-readable storage medium storing a computer program, which, when loaded and executed by a processor, implements the device asset delivery data evaluation method provided by the embodiment of the present application.
[0098] Based on the same inventive concept as in the foregoing embodiment, an embodiment of the present application further provides an electronic device, including a processor and a memory, wherein,
[0099] The memory is used to store a computer program;
[0100] The processor is used to load and execute the computer program so that the electronic device executes the device asset delivery data evaluation method provided by the embodiment of the present application.
[0101] In some embodiments, the computer-readable storage medium may be a memory such as FRAM, ROM, PROM, EPROM, EEPROM, flash memory, magnetic surface memory, optical disc, or CD-ROM; or it may be various devices including one or any combination of the foregoing memories. The computer may be various computing devices including intelligent terminals and servers.
[0102] In some embodiments, the executable instructions may be in the form of a program, software, software module, script, or code, written in any form of programming language (including compiled or interpreted languages, or declarative or procedural languages), and may be deployed in any form, including being deployed as an independent program or being deployed as a module, component, subroutine, or other unit suitable for use in a computing environment.
[0103] As an example, executable instructions may or may not correspond to files in a file system, and may be stored as part of a file that holds other programs or data, for example, in one or more scripts in a Hyper Text Markup Language (HTML) document, in a single file dedicated to the program being discussed, or in multiple cooperating files (such as files that hold one or more modules, subroutines, or code portions).
[0104] As an example, executable instructions may be deployed to execute on one computing device, or on multiple computing devices at one location, or on multiple computing devices distributed at multiple locations and interconnected by a communication network.
[0105] It should be noted that, in this document, the terms "including", "comprising", or any other variation thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or system that includes a series of elements includes not only those elements but also other elements not expressly listed, or elements that are inherent to such process, method, article, or system. Without further limitation, an element qualified by the statement "including a..." does not exclude the presence of additional identical elements in the process, method, article, or system that includes such element.
[0106] The serial numbers of the embodiments of the present application above are only for description and do not represent the superiority or inferiority of the embodiments.
[0107] From the description of the above embodiments, those skilled in the art can clearly understand that the above embodiment methods can be implemented by means of software plus a necessary general hardware platform. Of course, they can also be implemented by hardware, but in many cases the former is a better implementation. Based on such an understanding, the technical solution of the present application, in essence, or the part that contributes to the prior art, can be embodied in the form of a software product. The computer software product is stored in a storage medium (such as a read-only memory / random access memory, magnetic disk, optical disc), and includes several instructions to enable a multimedia terminal device (which may be a mobile phone, computer, television receiver, or network device, etc.) to execute the methods described in the various embodiments of the present application.
[0108] In summary, an equipment asset delivery data evaluation method, device, medium, and electronic device provided by an embodiment of the present application, the method includes: obtaining classification attributes of a target device according to the category of the target device; reading instantiated management data of the target device under the leaf node of the classification tree corresponding to the classification attributes to obtain delivery data of the target device; wherein the delivery data is used to characterize static attributes of the target device; performing diversified evaluation on the target device according to the delivery data to obtain index scores in multiple dimensions; adjusting the index weights of the index scores according to the category of the target device, and obtaining an evaluation score of the target device according to the index scores. The present application performs instantiated management on the equipment, and the verification and evaluation of the delivery data no longer rely on manual operation, which can improve the processing efficiency and the evaluation is more scientific than manual operation. First, the classification attributes of the equipment are confirmed through the category of the equipment, and only the instantiated management data under the leaf node of the classification tree corresponding to the classification attributes needs to be read correspondingly to quickly obtain the delivery data of the equipment. These delivery data are used to characterize static attributes with strong universality, and these static data are also attributes common to most equipment. The verification and evaluation can be quickly completed in multiple dimensions by using artificial intelligence algorithms. Finally, the weights of the index score fusion are adjusted according to the different categories of the equipment to obtain an evaluation score with more comprehensive characterization ability, realizing the overall effective evaluation of the equipment asset delivery data.
[0109] The foregoing are only the preferred embodiments of the present application and are not intended to limit the present application. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principle of the present application shall be included within the protection scope of the present application.
Claims
1. A method for evaluating equipment asset delivery data, characterized in that: The following steps are involved: According to the category of the target device, obtaining a classification attribute of the target device; Under the classification tree child node corresponding to the classification attribute, read the instantiation management data of the target device to obtain the delivery data of the target device; wherein the delivery data is used to characterize the static attributes of the target device; Based on the delivery data, a diversified evaluation is performed on the target device to obtain indicator scores in multiple dimensions; The indicator weight of the indicator score is adjusted according to the category of the target device, and the evaluation score of the target device is obtained according to the indicator score.
2. The equipment asset delivery data evaluation method according to claim 1, characterized in that: The step of reading the instantiation management data of the target device under the classification tree child node corresponding to the classification attribute to obtain the delivery data of the target device includes: Under the classification tree child node corresponding to the classification attribute, read the instantiation management data of the target device; Obtaining a static attribute model according to the instantiation management data; According to the static attribute model, the static attribute of the target device is read to obtain the delivery data of the target device.
3. The equipment asset delivery data evaluation method according to claim 2, characterized in that: Before obtaining the static attribute model according to the instantiation management data, the method further includes: Registering the object of the target device under the tree child node of the classification tree; spatially locating an object of the target device; Filling the static attributes of the object of the target device according to the static attribute model; A data docking channel is configured for the object of the target device to establish instantiation management data of the target device.
4. The equipment asset delivery data evaluation method according to claim 3, characterized in that: The spatially locating the object of the target device includes: Obtaining reference points and three-dimensional point cloud data of the target device; According to the application site of the target device, the reference point and the three-dimensional point cloud data are converted into coordinate systems of different precisions to spatially locate the object of the target device.
5. The equipment asset delivery data evaluation method according to claim 3, characterized in that: The spatially locating the object of the target device includes: According to the location area of the target device, the building location coding of the target device is performed according to the coding generation rule; The building location code is embedded in the IfcSpace feature dimension of the BIM model, and a spatial index rule is established to spatially locate the object of the target device.
6. The equipment asset delivery data evaluation method according to claim 2, characterized in that: The static attribute model includes: Basic attribute model, used to characterize the inherent attributes of the device; Condition monitoring model, used to characterize operating status data; Performance indicator models, used to characterize key performance indicators; Maintenance record model, used to represent maintenance data.
7. The equipment asset delivery data evaluation method according to claim 1, characterized in that: The diversity assessment includes: Completeness assessment, used to characterize the filling rate of required fields; Accuracy assessment, used to characterize the data verification pass rate; Timeliness evaluation, used to characterize the delay in data updates; Consistency assessment, used to characterize the matching degree of linked data; Validity evaluation is used to characterize the compliance rate of data value range.
8. An equipment asset delivery data evaluation device, characterized in that: include: A classification module, used to obtain a classification attribute of the target device according to the category of the target device; A reading module, used to read the instantiation management data of the target device under the classification tree child node corresponding to the classification attribute, and obtain the delivery data of the target device; wherein the delivery data is used to characterize the static attributes of the target device; An evaluation module, used to perform diversified evaluation on the target device according to the delivery data to obtain index scores under multiple dimensions; An adjustment module is used to adjust the indicator weight of the indicator score according to the category of the target device, and obtain the evaluation score of the target device according to the indicator score.
9. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is loaded and executed by a processor, the equipment asset delivery data evaluation method according to any one of claims 1 to 7 is implemented.
10. An electronic device, characterized in that: comprising a processor and a memory, wherein: The memory is used to store computer programs; The processor is used to load and execute the computer program so that the electronic device executes the equipment asset delivery data evaluation method according to any one of claims 1 to 7.