Data modeling method based on dynamic combination and attribute extension

Abstract

The invention provides a dynamic combination and attribute extension-based data modeling method, which comprises the following steps of: creating an extension attribute group which comprises a plurality of extension attributes, and associating each attribute with a measurement model through dynamic binding; creating a measurement model which comprises static attributes, dynamic attributes and associated extended attribute groups and supports nested combination; generating a hierarchical structure according to the measurement model type and the nesting relationship; and instantiating the measurement model, generating an instantiated primary equipment hierarchy tree and an instantiated secondary equipment hierarchy tree, automatically associating static and dynamic attributes of the extension attribute group, and generating a mapping relation of the dynamic attributes. According to the invention, the efficiency of data acquisition and equipment adjustment and control can be improved, the manual configuration work is reduced, and the engineering implementation cost is reduced.

Description

Technical Field

[0001] The present invention relates to the technical field of new energy and energy storage modeling, and more specifically, the present invention relates to a data modeling method based on dynamic combination and attribute extension. Background Art

[0002] With the continuous increase in the global demand for clean energy, the development and utilization of new energy (such as solar energy, wind energy, etc.) and energy storage have become the key to energy transformation. The application scenarios of new energy and energy storage are complex and changeable, including centralized or distributed solar photovoltaic power generation, industrial and commercial energy storage applications based on peak-valley arbitrage models, photovoltaic energy storage charging scenarios, and grid-connected and off-grid microgrid systems. These scenarios require the establishment of effective data models to achieve rapid data acquisition, calculation, and device regulation and control, helping researchers and engineers better understand system behavior and optimize system design and operation. However, existing data modeling technologies usually face problems such as insufficient model granularity, limited combination and reuse capabilities, insufficient dynamic expansion capabilities of attribute information, and imperfect design of the primary and secondary electrical characteristics. For example, the granularity of the model usually only reaches the device level, making it impossible to effectively combine and reuse; the static and dynamic attribute expansion capabilities of the model are insufficient, making it difficult to cope with the differences of different devices and project sites; the dynamic attribute mapping of primary and secondary devices in the traditional power automation field relies on manual configuration, resulting in low engineering efficiency.

[0003] In the process of implementing the embodiments of the present invention, the inventors found that there are at least the following problems or defects in the prior art: the combination and reuse capabilities of the model are insufficient, making it difficult to meet the requirements of complex scenarios; the dynamic expansion capabilities of attribute information are limited, and it is impossible to flexibly adapt to the differences of different devices and projects; the dynamic attribute mapping of primary and secondary devices relies on manual configuration, resulting in low data acquisition and application efficiency. Summary of the Invention

[0004] The present invention provides a data modeling method based on dynamic combination and attribute extension, including:

[0005] Step 1: Create at least one extended attribute group, the extended attribute group includes multiple extended attributes, each extended attribute is associated with a measurement model through dynamic binding, and the extended attributes include name, code, form type, data type, default value, and remarks;

[0006] Step 2: Create at least one measurement model, the measurement model includes static attributes, dynamic attributes, and associated extended attribute groups, the type of the measurement model is a primary device type, a secondary device type, or a primary and secondary device type, the measurement model supports nested combination, where the power station measurement model aggregates the interval measurement model, and the interval measurement model aggregates the device measurement model;

[0007] Step 3: Generate a hierarchical structure by combining according to the type and nesting relationship of the measurement model, where the hierarchical structure includes a primary equipment hierarchical tree and a secondary equipment hierarchical tree;

[0008] Step 4: Instantiate the measurement model to generate an instantiated primary equipment hierarchical tree and secondary equipment hierarchical tree. During the instantiation process, the static attributes and dynamic attributes of the extended attribute group are automatically associated, and a mapping relationship of the dynamic attributes is generated.

[0009] Further, the specific steps for creating the extended attribute group in Step 1 include:

[0010] Step 1.1: Define the name and coding of the extended attribute group;

[0011] Step 1.2: Add multiple extended attributes to the extended attribute group. The form type of each extended attribute includes an input box, a dropdown list, or a checkbox;

[0012] Step 1.3: Dynamically bind the extended attribute group to the measurement model, and the measurement model associates with the extended attributes in all or part of the extended attribute groups.

[0013] Further, the specific steps for creating the measurement model in Step 2 include:

[0014] Step 2.1: Define the name, type, and nesting rules of the measurement model, where the nesting rules include that the power station measurement model can only aggregate interval measurement models, and the interval measurement model can only aggregate equipment measurement models;

[0015] Step 2.2: Configure static attributes for the measurement model, and the static attributes include equipment production information and basic parameters;

[0016] Step 2.3: Configure dynamic attributes for the measurement model, and the dynamic attributes include time series data indicators and communication protocol parameters;

[0017] Step 2.4: Associate the extended attribute group with the static attributes and dynamic attributes of the measurement model.

[0018] Further, the specific steps for combining and generating the hierarchical structure in Step 3 include:

[0019] Step 3.1: Select the primary equipment type or secondary equipment type as the combination benchmark according to the type of the measurement model;

[0020] Step 3.2: Aggregate the measurement models layer by layer according to the nesting rules to generate a primary equipment hierarchical tree and a secondary equipment hierarchical tree;

[0021] Step 3.3: In the hierarchical structure, the nodes of the primary equipment hierarchical tree correspond to physical devices, and the nodes of the secondary equipment hierarchical tree correspond to control or monitoring devices.

[0022] Further, the specific steps for instantiating the measurement model in Step 4 include:

[0023] Step 4.1: Based on the hierarchical structure, generate an instantiated primary equipment hierarchical tree and secondary equipment hierarchical tree;

[0024] Step 4.2: According to the binding relationship of the extended attribute group, automatically fill the static attributes and dynamic attributes of each node in the instantiated hierarchical tree;

[0025] Step 4.3: Generate a mapping relationship for the dynamic attributes, and the mapping relationship includes the association configuration of time series data and communication protocols.

[0026] Further, the data types of the extended attributes in Step 1.2 include integer type, floating-point type, string type or boolean type, and the default value is automatically assigned or manually configured according to the data type.

[0027] Further, the communication protocol parameters of the dynamic attributes in Step 2.3 include register address, function code and data type, and the time series data metrics include voltage, current or temperature.

[0028] Further, the aggregation quantity of the hierarchical structure in Step 3.2 is obtained by multiplying the aggregation quantities of each hierarchical measurement model to get the total aggregation quantity.

[0029] Further, the mapping relationship in Step 4.3 is a one-to-one mapping set formed by the identifiers of the dynamic attributes and the corresponding communication protocol parameters or time series data addresses.

[0030] Further, the instantiation of the measurement model in Step 4 further includes:

[0031] Through the device type and nesting rules of the measurement model, the nodes of the primary equipment hierarchical tree are automatically associated with the nodes of the secondary equipment hierarchical tree to form a corresponding relationship between physical devices and control devices.

[0032] The above embodiments of the present invention have at least the following beneficial effects: Through the data modeling method of dynamic combination and attribute extension, the present invention can improve the flexibility and efficiency of data modeling in new energy and energy storage scenarios. This method supports the dynamic combination and nesting of models, and can flexibly construct hierarchical structures according to actual needs, such as the primary equipment hierarchical tree and the secondary equipment hierarchical tree, so as to meet the requirements of complex and changing energy application scenarios. At the same time, through the dynamic binding of extended attribute groups, the static and dynamic attributes of the model can be flexibly extended to adapt to the differences of different devices and projects, and reduce the workload of manual configuration.

[0033] In addition, during the model instantiation process of the present invention, the hierarchical structures of primary equipment and secondary equipment can be automatically generated, and the static and dynamic attributes of the extended attribute group can be automatically associated to generate the mapping relationship of dynamic attributes. This automated mechanism can reduce the engineering implementation cost, improve the efficiency of data collection and device control, and provide strong support for the planning and design, operation management of new energy and energy storage systems, and the collaborative work with other energy or dispatching systems. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] By reading the following detailed description with reference to the accompanying drawings, the above and other objects, features and advantages of the exemplary embodiments of the present invention will become readily understood. In the drawings, several embodiments of the present invention are shown by way of example and not by way of limitation, wherein:

[0035] Figure 1 is a schematic flowchart of a data modeling method based on dynamic combination and attribute extension provided by an embodiment of the present invention;

[0036] Figure 2 is a schematic flowchart of generating a primary equipment hierarchical structure by model instantiation provided by an embodiment of the present invention;

[0037] Figure 3 is a schematic flowchart of generating a secondary equipment hierarchical structure by model instantiation provided by an embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0038] The principles and spirit of the present invention will be described below with reference to several exemplary embodiments. It should be understood that these embodiments are given only to enable those skilled in the art to better understand and then implement the present invention, and are not intended to limit the scope of the present invention in any way. On the contrary, these embodiments are provided to make the present invention more thorough and complete, and to be able to fully convey the scope of the present invention to those skilled in the art.

[0039] Those skilled in the art know that the embodiments of the present invention can be implemented as a system, a device, equipment, a method, or a computer program product. Therefore, the present invention can be specifically implemented in the following forms, namely: complete hardware, complete software (including firmware, resident software, microcode, etc.), or a combination of hardware and software.

[0040] It should be noted that any number of elements in the drawings is for illustration rather than limitation, and any naming is only for distinction and does not have any limiting meaning.

[0041] The following refers to Figure 1 , Figure 1 which is a schematic flowchart of a data modeling method based on dynamic combination and attribute extension provided for an embodiment of the present invention. As Figure 1 shown, a data modeling method based on dynamic combination and attribute extension includes:

[0042] Step 1: Create at least one extended attribute group, where the extended attribute group includes multiple extended attributes, and each extended attribute is associated with a measurement model through dynamic binding. The extended attributes include name, code, form type, data type, default value, and remarks.

[0043] Step 2: Create at least one measurement model, where the measurement model includes static attributes, dynamic attributes, and an associated extended attribute group. The type of the measurement model is a primary device type, a secondary device type, or a primary-secondary device type. The measurement model supports nested combination, where the power station measurement model aggregates the interval measurement model, and the interval measurement model aggregates the device measurement model.

[0044] Step 3: According to the type and nested relationship of the measurement model, combine to generate a hierarchical structure, where the hierarchical structure includes a primary device hierarchical tree and a secondary device hierarchical tree.

[0045] Step 4: Instantiate the measurement model to generate an instantiated primary device hierarchical tree and a secondary device hierarchical tree. During the instantiation process, automatically associate the static attributes and dynamic attributes of the extended attribute group, and generate a mapping relationship of the dynamic attributes.

[0046] It should be noted that the present invention relates to a data modeling method based on dynamic combination and attribute extension. The core lies in dynamically binding extended attributes to measurement models to achieve flexible extension and combination of the models. The extended attribute group refers to a group of attribute information that can be dynamically added to the model, including name, code, form type, data type, default value, remarks, etc. The measurement model refers to a model used to describe equipment, intervals or power stations, including static attributes, dynamic attributes and associated extended attribute groups. Static attributes usually include basic information of the equipment, such as manufacturer, production date, etc., while dynamic attributes include time series data indicators and communication protocol parameters. Through nested combination, the measurement model can form a hierarchical structure, such as the primary equipment hierarchical tree and the secondary equipment hierarchical tree, so as to meet the requirements of complex scenarios.

[0047] Specifically, each extended attribute in the extended attribute group can be configured through a form type (such as input box, dropdown list or checkbox), and the data type can include integer type, floating point type, string type or boolean type. The type of the measurement model can be primary equipment type, secondary equipment type or primary-secondary equipment type. Among them, primary equipment usually refers to physical equipment, such as battery cells, battery packs, etc., and secondary equipment refers to control or monitoring equipment, such as BMS (Battery Management System). The nesting rule of the measurement model stipulates that the power station measurement model can only aggregate interval measurement models, and the interval measurement model can only aggregate equipment measurement models. Through this nested combination, a primary equipment hierarchical tree and a secondary equipment hierarchical tree can be generated, corresponding to physical equipment and control equipment respectively.

[0048] Preferably, during the model instantiation process, the hierarchical structures of primary equipment and secondary equipment can be automatically generated, and the static attributes and dynamic attributes of the extended attribute group can be automatically associated. For example, the instantiated primary equipment hierarchical tree can include a multi-layer structure from the power station to the battery cell, while the secondary equipment hierarchical tree can include a structure from the power station to the BMS. The mapping relationship of dynamic attributes can be automatically generated without manual configuration, thus reducing the workload of project implementation. More specifically, the mapping relationship of dynamic attributes can include the associated configuration of time series data and communication protocols, such as the corresponding relationship between register address, function code and data type, to ensure the accuracy of data collection and control.

[0049] In some embodiments, the specific steps of creating the extended attribute group in step 1 include:

[0050] Step 1.1: Define the name and code of the extended attribute group;

[0051] Step 1.2: Add multiple extended attributes to the extended attribute group, and the form type of each extended attribute includes an input box, a dropdown list or a checkbox;

[0052] Step 1.3: Dynamically bind the extended attribute group to the measurement model, where the measurement model is associated with the extended attributes in all or part of the extended attribute groups.

[0053] It should be noted that the specific steps for creating an extended attribute group in the present invention include defining the name and code of the extended attribute group and adding multiple extended attributes to the group. An extended attribute group refers to a set of attribute information that can be dynamically added to a model, and its name and code are used to uniquely identify the group. An extended attribute refers to an attribute that can be dynamically bound to a measurement model, and each extended attribute includes information such as name, code, form type, data type, default value, and remarks. The form type is used to define the input method of the user interface, such as an input box, a dropdown list, or a checkbox, and the data type is used to define the value type of the attribute, such as integer, floating point, string, or boolean.

[0054] Specifically, the name and code of the extended attribute group can be customized according to actual needs. For example, it can be named production information or Modbus communication configuration. The form type of each extended attribute can be selected according to the data type and user input requirements. For example, for the production date, an input box can be selected, while for the function code of the communication protocol, a dropdown list can be selected. The selection of the data type determines the value range of the attribute. For example, integer is used to represent integers, floating point is used to represent decimals, string is used to represent text, and boolean is used to represent true or false. The default value can be automatically assigned according to the data type. For example, the default value of an integer can be set to 0, and the default value of a string can be set to an empty string.

[0055] Preferably, the form type of the extended attribute can be flexibly configured according to user needs. For example, for the register address of the communication protocol, an input box can be selected so that the user can manually enter the specific address; for the function code, a dropdown list can be selected to provide predefined options and reduce user input errors. More specifically, the default value of the extended attribute can be automatically assigned according to the data type or manually configured. For example, for a boolean attribute, the default value can be set to true or false, and for a string attribute, the default value can be set to an empty string or a specific text prompt. Through this flexible configuration method, it can be ensured that the extended attribute group can adapt to the needs of different devices and projects, and improve the scalability and applicability of the model.

[0056] In some embodiments, the specific steps for creating the measurement model in step 2 include:

[0057] Step 2.1: Define the name, type, and nesting rules of the measurement model, where the nesting rules include that the power station measurement model can only aggregate interval measurement models, and the interval measurement model can only aggregate device measurement models;

[0058] Step 2.2: Configure static attributes for the measurement model, where the static attributes include device production information and basic parameters;

[0059] Step 2.3: Configure dynamic attributes for the measurement model, where the dynamic attributes include time-series data metrics and communication protocol parameters;

[0060] Step 2.4: Associate the extended attribute group with the static and dynamic attributes of the measurement model.

[0061] It should be noted that the specific steps for creating a measurement model in the present invention include defining the name, type, and nesting rules of the measurement model, and configuring static and dynamic attributes for the model. A measurement model refers to a model used to describe a device, interval, or power station, and its name and type are used to identify the category of the model, such as primary device type, secondary device type, or primary-secondary device type. The nesting rules define the aggregation relationship between models. For example, a power station measurement model can only aggregate interval measurement models, and an interval measurement model can only aggregate device measurement models. Static attributes refer to the basic information of the device, such as the manufacturer, production date, etc., while dynamic attributes include time-series data metrics and communication protocol parameters.

[0062] Specifically, the name and type of the measurement model can be defined according to the actual application scenario. For example, it can be named as cell or air-cooled Pack, and the type can be set as a primary device or a secondary device. The static attributes can include the production information of the device (such as production number, manufacturer, production date, etc.) and basic parameters (such as voltage, current, etc.). The dynamic attributes can include time-series data metrics (such as voltage, current, temperature, etc.) and communication protocol parameters (such as register address, function code, data type, etc.). By associating the extended attribute group with the static and dynamic attributes of the measurement model, flexible expansion of the model can be achieved.

[0063] Preferably, the nesting rules of the measurement model can be adjusted according to actual needs. For example, a power station measurement model can aggregate multiple interval measurement models, and each interval measurement model can aggregate multiple device measurement models, thus forming a complex hierarchical structure. More specifically, the communication protocol parameters of the dynamic attributes can be configured according to the specific communication protocol. For example, the register address of the Modbus protocol can be set in hexadecimal format, and the function code can be set for read or write operations. Through this flexible configuration method, it can be ensured that the measurement model can adapt to the needs of different devices and projects, and improve the applicability and expandability of the model.

[0064] In some embodiments, the specific steps for combining and generating the hierarchical structure in step 3 include:

[0065] Step 3.1: Select the primary device type or secondary device type as the combination benchmark according to the type of the measurement model;

[0066] Step 3.2: Aggregate the measurement models layer by layer according to the nested rules to generate a primary equipment hierarchy tree and a secondary equipment hierarchy tree;

[0067] Step 3.3: In the hierarchical structure, the nodes of the primary equipment hierarchy tree correspond to physical devices, and the nodes of the secondary equipment hierarchy tree correspond to control or monitoring devices.

[0068] It should be noted that the specific steps of combining and generating the hierarchical structure in the present invention include selecting the primary equipment type or the secondary equipment type as the combination basis according to the type of the measurement model, and aggregating the measurement models layer by layer according to the nested rules to generate a primary equipment hierarchy tree and a secondary equipment hierarchy tree. The hierarchical structure refers to a tree-like structure formed by the nested combination of measurement models, which is used to describe the aggregation relationship between devices. The nodes of the primary equipment hierarchy tree correspond to physical devices, such as battery cells, battery packs, etc., while the nodes of the secondary equipment hierarchy tree correspond to control or monitoring devices, such as BMS (Battery Management System). Through this hierarchical structure, the physical and control relationships between devices can be clearly represented.

[0069] Specifically, the type of the measurement model can be selected according to the actual application scenario. For example, the primary equipment type can be used to describe physical devices, and the secondary equipment type can be used to describe control devices. The nested rules define the aggregation relationship between models. For example, the power station measurement model can only aggregate the bay measurement model, and the bay measurement model can only aggregate the device measurement model. By aggregating layer by layer, a primary equipment hierarchy tree and a secondary equipment hierarchy tree can be generated. The nodes of the primary equipment hierarchy tree can include power stations, bays, battery clusters, battery packs, battery cells, etc., while the nodes of the secondary equipment hierarchy tree can include power stations, bays, and BMS, etc.

[0070] Preferably, the generation of the hierarchical structure can be adjusted according to actual needs. For example, the primary equipment hierarchy tree can be designed according to the physical structure of the equipment, such as a multi-layer structure from the power station to the battery cell; the secondary equipment hierarchy tree can be designed according to the logical structure of the control system, such as a multi-layer structure from the power station to the BMS. More specifically, the total aggregation quantity of the hierarchical structure can be obtained by multiplying the aggregation quantities of the measurement models at each layer. For example, a power station can contain multiple bays, each bay can contain multiple battery clusters, each battery cluster can contain multiple battery packs, and each battery pack can contain multiple battery cells. Through this flexible hierarchical structure design, it can be ensured that the model can accurately describe the aggregation relationship between complex devices, and improve the applicability and scalability of the model.

[0071] Such as Figure 2 and Figure 3As shown in the figure, a schematic flow chart of generating a primary device hierarchy and a secondary device hierarchy for a model instantiation in some embodiments includes:

[0072] Step 4.1: Generate an instantiated primary device hierarchy tree and a secondary device hierarchy tree based on the hierarchy structure;

[0073] Step 4.2: Automatically fill the static and dynamic attributes of each node in the instantiated hierarchy tree according to the binding relationship of the extended attribute group;

[0074] Step 4.3: Generate a mapping relationship for the dynamic attributes, where the mapping relationship includes the associated configuration of time-series data and communication protocols.

[0075] It should be noted that the specific steps for instantiating the measurement model in the present invention include generating an instantiated primary device hierarchy tree and a secondary device hierarchy tree based on the hierarchy structure, and automatically filling the static and dynamic attributes of each node in the instantiated hierarchy tree according to the binding relationship of the extended attribute group. Instantiation refers to converting the measurement model into specific device instances and generating a primary device hierarchy tree and a secondary device hierarchy tree. The nodes of the primary device hierarchy tree correspond to physical devices, such as battery cells, battery packs, etc., while the nodes of the secondary device hierarchy tree correspond to control or monitoring devices, such as BMS (Battery Management System). During the instantiation process, the static and dynamic attributes of the extended attribute group will be automatically associated with each node, and a mapping relationship for the dynamic attributes will be generated to ensure the accuracy of data collection and control.

[0076] Specifically, the instantiation process can automatically generate the hierarchy trees of primary and secondary devices according to the hierarchy structure. For example, the primary device hierarchy tree can include a multi-layer structure from the power station to the battery cells, while the secondary device hierarchy tree can include a multi-layer structure from the power station to the BMS. The binding relationship of the extended attribute group determines the filling method of the static and dynamic attributes of each node. The static attributes can include production information of the device (such as production number, manufacturer, production date, etc.), and the dynamic attributes can include time-series data indicators (such as voltage, current, temperature, etc.) and communication protocol parameters (such as register address, function code, data type, etc.). The mapping relationship of the dynamic attributes can be automatically generated without manual configuration.

[0077] Preferably, the dynamic attribute mapping relationship during the instantiation process can be configured according to specific communication protocols. For example, for the Modbus protocol, the register address can be set in hexadecimal format, and the function code can be set for read or write operations. More specifically, the hierarchical tree after instantiation can be adjusted according to actual requirements. For example, the primary equipment hierarchical tree can be designed according to the physical structure of the equipment, such as a multi-layer structure from the power station to the battery cell; the secondary equipment hierarchical tree can be designed according to the logical structure of the control system, such as a multi-layer structure from the power station to the BMS. Through this flexible instantiation method, it can be ensured that the model can accurately describe the aggregation relationship between complex devices, improving the applicability and scalability of the model.

[0078] In some embodiments, the data types of the extended attributes in step 1.2 include integer type, floating point type, string type or boolean type, and the default value is automatically assigned or manually configured according to the data type.

[0079] It should be noted that the data types of the extended attributes in the present invention include integer type, floating point type, string type or boolean type, and the default value can be automatically assigned or manually configured according to the data type. The data type refers to the format of the extended attribute value. The integer type is used to represent integers, the floating point type is used to represent decimals, the string type is used to represent text, and the boolean type is used to represent true or false. The default value refers to the initial value automatically assigned to the attribute when there is no user input. By supporting multiple data types and flexible default value configuration, it can be ensured that the extended attributes can adapt to the needs of different devices and projects, improving the scalability and applicability of the model.

[0080] Specifically, the data types of the extended attributes can be selected according to actual requirements. For example, the production number can be selected as the string type, the production date can be selected as the integer type or the string type, and the register address of the communication protocol can be selected as the integer type or the string type. The default value can be automatically assigned according to the data type. For example, the default value of the integer type can be set to 0, and the default value of the string type can be set to an empty string. For boolean attributes, the default value can be set to true or false. Through this flexible configuration method, it can be ensured that the extended attributes can adapt to the needs of different devices and projects, reducing the workload of user input.

[0081] Preferably, the default values of the extended attributes can be manually configured according to actual requirements. For example, for the default value of the production date, it can be set to the current date; for the register address of the communication protocol, it can be set to a common address range. More specifically, the data types of the extended attributes can be flexibly configured according to user input requirements. For example, for attributes that require user input, an input box can be selected as the form type; for attributes that require selecting predefined options, a dropdown list or checkbox can be selected as the form type. Through this flexible configuration method, it can be ensured that the extended attributes can adapt to the needs of different devices and projects, improving the applicability and scalability of the model.

[0082] In some embodiments, the communication protocol parameters of the dynamic attributes in step 2.3 include register address, function code, and data type, and the time series data metrics include voltage, current, or temperature.

[0083] It should be noted that in the present invention, the communication protocol parameters of the dynamic attributes include register address, function code, and data type, and the time series data metrics include voltage, current, or temperature. Communication protocol parameters refer to the configuration information for device communication. The register address is used to identify the data storage location in the device, the function code is used to define the type of communication operation (such as read or write), and the data type is used to define the format of the communication data. Time series data metrics refer to the time series data generated during the operation of the device, such as voltage, current, or temperature, etc. By configuring the communication protocol parameters and time series data metrics, accurate acquisition and control of device data can be ensured.

[0084] Specifically, the communication protocol parameters can be configured according to the specific communication protocol. For example, for the Modbus protocol, the register address can be set in hexadecimal format, the function code can be set to read (such as function code 03) or write (such as function code 06) operations, and the data type can be set to integer, floating-point, or string type. The time series data metrics can be configured according to the actual operating conditions of the device. For example, the voltage can be set to floating-point type, the current can be set to integer or floating-point type, and the temperature can be set to integer or floating-point type. Through this flexible configuration method, it can be ensured that the dynamic attributes can adapt to the needs of different devices and projects, improving the applicability and scalability of the model.

[0085] Preferably, the communication protocol parameters can be flexibly configured according to actual requirements. For example, for different devices, the register address can be set to different ranges, and the function code can be selected according to communication requirements. More specifically, the time series data metrics can be adjusted according to the actual operating conditions of the device. For example, for voltage and current, different sampling frequencies can be set; for temperature, different alarm thresholds can be set. Through this flexible configuration method, it can be ensured that the dynamic attributes can accurately reflect the operating state of the device, improving the accuracy of data acquisition and control.

[0086] In some embodiments, the total number of aggregations of the hierarchical structure in step 3.2 is obtained by multiplying the numbers of aggregations of the measurement models at each level.

[0087] It should be noted that in the present invention, the total number of aggregations of the middle-level structure is obtained by multiplying the numbers of aggregations of the measurement models at each level. The hierarchical structure refers to a tree-like structure formed by nested combinations of measurement models and is used to describe the aggregation relationship between devices. The number of aggregations refers to the number of instances of the measurement model in each level. For example, a power station may include multiple bays, and each bay may include multiple devices. By multiplying the numbers of aggregations at each level, the total number of aggregations of the entire hierarchical structure can be obtained, thereby accurately describing the aggregation relationship between complex devices.

[0088] Specifically, the number of aggregations of the hierarchical structure can be configured according to the actual application scenario. For example, a power station may include multiple bays, each bay may include multiple battery clusters, each battery cluster may include multiple battery packs, and each battery pack may include multiple battery cells. By multiplying the numbers of aggregations at each level, the total number of aggregations of the entire hierarchical structure can be obtained. For example, if a power station includes 2 bays, each bay includes 3 battery clusters, each battery cluster includes 4 battery packs, and each battery pack includes 16 battery cells, then the total number of aggregations is 2×3×4×16 = 384 battery cells. Through this calculation method, it can be ensured that the hierarchical structure can accurately describe the aggregation relationship between complex devices.

[0089] Preferably, the number of aggregations of the hierarchical structure can be adjusted according to actual needs. For example, for different power stations, different numbers of bays can be set; for different bays, different numbers of battery clusters can be set. More specifically, the calculation of the number of aggregations can be optimized according to the physical structure of the device. For example, for the numbers of aggregations of the battery cluster and the battery pack, they can be set according to the capacity and cooling method of the device. Through this flexible configuration method, it can be ensured that the hierarchical structure can accurately reflect the physical and control relationships of the devices, and improve the applicability and scalability of the model.

[0090] In some embodiments, in step 4.3, the mapping relationship is a one-to-one mapping set formed by the identifiers of dynamic attributes and the corresponding communication protocol parameters or time-series data addresses.

[0091] It should be noted that in the present invention, the mapping relationship forms a one-to-one mapping set between the identifiers of dynamic attributes and the corresponding communication protocol parameters or timing data addresses. The mapping relationship refers to the corresponding relationship between dynamic attributes and communication protocol parameters or timing data addresses, ensuring the accuracy of data acquisition and control. The identifier of a dynamic attribute refers to the code or name used to uniquely identify the dynamic attribute. The communication protocol parameters include register addresses, function codes, and data types. The timing data address refers to the storage location of the timing data in the device. By forming a one-to-one mapping set, it can be ensured that each dynamic attribute can accurately correspond to the corresponding communication protocol parameter or timing data address.

[0092] Specifically, the configuration of the mapping relationship can be set according to the specific communication protocol and timing data. For example, for the Modbus protocol, the identifier of the dynamic attribute can be set as the register address, and the communication protocol parameters can include the function code and data type. The timing data address can be set as the storage location in the device, such as the storage addresses of voltage, current, or temperature. By corresponding the identifier of the dynamic attribute with the communication protocol parameter or timing data address one by one, the accuracy of data acquisition and control can be ensured. For example, the identifier of the voltage dynamic attribute can correspond to the register address 40001, the function code is 03, and the data type is floating point.

[0093] Preferably, the configuration of the mapping relationship can be flexibly adjusted according to actual needs. For example, for different devices, different register addresses and function codes can be set; for different timing data, different storage addresses can be set. More specifically, the generation of the mapping relationship can be optimized according to the communication protocol and timing data of the device. For example, for voltage and current dynamic attributes, different register addresses and function codes can be set; for temperature dynamic attributes, different storage addresses and data types can be set. Through this flexible configuration method, it can be ensured that the mapping relationship can accurately reflect the communication and timing data requirements of the device, improving the accuracy of data acquisition and control.

[0094] In some embodiments, the instantiation of the measurement model in step 4 further includes:

[0095] Through the device type and nesting rules of the measurement model, the nodes of the primary device hierarchy tree are automatically associated with the nodes of the secondary device hierarchy tree, forming the corresponding relationship between physical devices and control devices.

[0096] It should be noted that in the process of instantiating the measurement model in the present invention, through the device type and nesting rules of the measurement model, the nodes of the primary device hierarchy tree can be automatically associated with the nodes of the secondary device hierarchy tree, forming the corresponding relationship between the physical device and the control device. Instantiation refers to converting the measurement model into specific device instances, generating the primary device hierarchy tree and the secondary device hierarchy tree. The nodes of the primary device hierarchy tree correspond to physical devices, such as battery cells, battery packs, etc., while the nodes of the secondary device hierarchy tree correspond to control or monitoring devices, such as BMS (Battery Management System). By automatically associating the nodes of the primary device and the secondary device, the corresponding relationship between the physical device and the control device can be ensured to be accurate and error-free, improving the efficiency of data collection and control.

[0097] Specifically, the automatic association in the instantiation process can be configured according to the device type and nesting rules. For example, the battery cell node in the primary device hierarchy tree can be automatically associated with the BMS node in the secondary device hierarchy tree, and the battery pack node can be automatically associated with the BMS node. The nesting rules define the aggregation relationship between the models. For example, the power station measurement model can only aggregate the interval measurement model, and the interval measurement model can only aggregate the device measurement model. Through this automatic association mechanism, the corresponding relationship between the primary device and the secondary device can be ensured to be accurate and error-free, reducing the workload of manual configuration.

[0098] Preferably, the configuration of the automatic association can be flexibly adjusted according to actual needs. For example, different association rules can be set for different device types; different aggregation relationships can be set for different nesting rules. More specifically, the generation of the automatic association can be optimized according to the physical structure of the device. For example, different association rules can be set for the nodes of the battery cluster and the battery pack; different control logics can be set for the BMS node. Through this flexible configuration method, it can be ensured that the automatic association can accurately reflect the corresponding relationship between the physical device and the control device, improving the accuracy of data collection and control.

[0099] The above-mentioned embodiments of the present invention have the following beneficial effects: Through the data modeling method of dynamic combination and attribute extension, the present invention can improve the flexibility and efficiency of data modeling in new energy and energy storage scenarios. This method supports creating extended attribute groups and dynamically binding them to the measurement model, and can flexibly expand the static and dynamic attributes of the model to adapt to the differences of different devices and projects. At the same time, through the nested combination of the measurement model, the primary device hierarchy tree and the secondary device hierarchy tree can be generated to meet the requirements of complex scenarios. During the model instantiation process, the static and dynamic attributes of the extended attribute group can be automatically associated, and the mapping relationship of the dynamic attributes can be generated, reducing the workload of manual configuration.

[0100] In addition, the present invention can automatically generate the hierarchical structures of primary equipment and secondary equipment, and automatically populate static attributes and dynamic attributes during the instantiation process to generate the mapping relationships of dynamic attributes. This automation mechanism can significantly reduce the engineering implementation cost and improve the efficiency of data collection and equipment control. Through dynamic combination and attribute extension, it can flexibly cope with the differences of different equipment and projects, providing strong support for the planning and design, operation management of new energy and energy storage systems, and their collaborative work with other energy or dispatching systems.

[0101] Furthermore, the storage medium of the embodiment of the present application stores program instructions capable of implementing all the above methods. Among them, the program instructions can be stored in the above storage medium in the form of a software product, including several instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) or a processor to execute all or part of the steps of the methods described in various embodiments of the present application. The aforementioned storage medium includes: various media that can store program codes such as USB flash drives, mobile hard disks, read-only memories (ROMs), random access memories (RAMs), magnetic disks, or optical discs, or terminal devices such as computers, servers, mobile phones, and tablets.

[0102] The above description is only some preferred embodiments of the present invention and an explanation of the applied technical principles. Those skilled in the art should understand that the scope of the invention involved in the embodiments of the present invention is not limited to the technical solutions formed by the specific combination of the above technical features, and should also cover other technical solutions formed by any combination of the above technical features or their equivalent features without departing from the above inventive concept. For example, the technical solutions formed by mutually replacing the above features with the (but not limited to) technical features with similar functions disclosed in the embodiments of the present invention.

Claims

1. A data modeling method based on dynamic combination and attribute extension, characterized in that, It includes the following steps: Step 1: Create at least one extended attribute group, where the extended attribute group includes multiple extended attributes, and each extended attribute is associated with a measurement model through dynamic binding. The extended attributes include name, code, form type, data type, default value, and remarks; Step 2: Create at least one measurement model, where the measurement model includes static attributes, dynamic attributes, and an associated extended attribute group. The type of the measurement model is primary equipment type, secondary equipment type, or primary-secondary equipment type. The measurement model supports nested combination, where the power station measurement model aggregates interval measurement models, and the interval measurement model aggregates equipment measurement models; Step 3: According to the type and nesting relationship of the measurement model, combine and generate a hierarchical structure, where the hierarchical structure includes a primary equipment hierarchical tree and a secondary equipment hierarchical tree; Step 4: Instantiate the measurement model to generate an instantiated primary equipment hierarchical tree and a secondary equipment hierarchical tree. During the instantiation process, automatically associate the static attributes and dynamic attributes of the extended attribute group, and generate a mapping relationship of the dynamic attributes.

2. The method according to claim 1, wherein The specific steps for creating the extended attribute group in Step 1 include: Step 1.1: Define the name and code of the extended attribute group; Step 1.2: Add multiple extended attributes to the extended attribute group, and the form type of each extended attribute includes input box, dropdown list, or checkbox; Step 1.3: Dynamically bind the extended attribute group to the measurement model, and the measurement model associates with all or part of the extended attributes in the extended attribute group.

3. The method according to claim 1, characterized in that, The specific steps for creating the measurement model in Step 2 include: Step 2.1: Define the name, type, and nesting rules of the measurement model, where the nesting rules include that the power station measurement model can only aggregate interval measurement models, and the interval measurement model can only aggregate equipment measurement models; Step 2.2: Configure static attributes for the measurement model, and the static attributes include equipment production information and basic parameters; Step 2.3: Configure dynamic attributes for the measurement model, and the dynamic attributes include time series data indicators and communication protocol parameters; Step 2.4: Associate the extended attribute group with the static attributes and dynamic attributes of the measurement model.

4. The method according to claim 1, wherein The specific steps for combining and generating the hierarchical structure in Step 3 include: Step 3.1: According to the type of the measurement model, select the primary equipment type or the secondary equipment type as the combination benchmark; Step 3.2: Aggregate the measurement models layer by layer according to the nesting rules to generate a primary equipment hierarchical tree and a secondary equipment hierarchical tree; Step 3.3: In the hierarchical structure, the nodes of the primary equipment hierarchical tree correspond to physical equipment, and the nodes of the secondary equipment hierarchical tree correspond to control or monitoring equipment.

5. The method according to claim 1, characterized in that, The specific steps for instantiating the measurement model in Step 4 include: Step 4.1: Generate an instantiated primary equipment hierarchical tree and a secondary equipment hierarchical tree based on the hierarchical structure; Step 4.2: Automatically fill the static attributes and dynamic attributes of each node in the instantiated hierarchical tree according to the binding relationship of the extended attribute group. Step 4.3: Generate a mapping relationship for the dynamic attribute, where the mapping relationship includes the associated configuration of time-series data and communication protocols.

6. The method according to claim 2, characterized in that The data types of the extended attributes in Step 1.2 include integer type, floating-point type, string type, or boolean type, and the default value is automatically assigned or manually configured according to the data type.

7. The method according to claim 3, wherein The communication protocol parameters of the dynamic attributes in Step 2.3 include register address, function code, and data type, and the time-series data metrics include voltage, current, or temperature.

8. The method according to claim 4, characterized in that The total aggregation quantity of the hierarchical structure in Step 3.2 is obtained by multiplying the aggregation quantities of each hierarchical measurement model.

9. The method according to claim 5, characterized in that, The mapping relationship in Step 4.3 is a one-to-one mapping set formed by the identifier of the dynamic attribute and the corresponding communication protocol parameter or time-series data address.

10. The method according to claim 5, characterized in that Step 4 for instantiating the measurement model further includes: Through the device type and nesting rules of the measurement model, the nodes of the primary device hierarchical tree are automatically associated with the nodes of the secondary device hierarchical tree to form the corresponding relationship between physical devices and control devices.

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