An Internet of Things ontology management method, device, equipment, medium and product

Abstract

The present invention discloses an Internet of Things ontology management method, device, equipment, medium and product. The method comprises: obtaining indicator information, classifying each of the indicator information based on preset tags to obtain at least one indicator set, and constructing each Internet of Things ontology according to at least one of the indicator sets; obtaining the ontology type corresponding to each Internet of Things ontology, the spatial position information of each Internet of Things ontology in the physical space, and the physical subordinate relationship between every two Internet of Things ontologies, and constructing a space matrix; managing each of the Internet of Things ontology according to the space matrix and early warning rules. Through the technical solution of the present invention, it is possible to provide an efficient and flexible parameter modeling method for the Internet of Things ontology and its related massive indicators, so as to improve the management efficiency of the Internet of Things ontology and meet diverse application needs.

Description

Technical Field

[0001] Embodiments of the present invention relate to the field of Internet of Things digital twin technology, and in particular, to an Internet of Things ontology management method, device, equipment, medium and product. Background Art

[0002] With the rapid development of Internet of Things technology, a large number of physical devices, sensors and control systems are widely used in various industries, such as smart manufacturing, smart cities, smart transportation, etc. The core of the Internet of Things system is to collect various data in the physical world in real time through sensors, including equipment status, environmental parameters, operating behavior and other information, and transmit and process them through the network to provide real-time monitoring, remote control and data analysis services.

[0003] However, as the scale of IoT systems continues to expand, the complexity of physical space and equipment is also increasing significantly. Faced with a large amount of equipment and data, how to efficiently digitally model physical space and equipment and accurately analyze and predict the collected indicators has become a difficult problem that needs to be solved. Traditional modeling methods often have difficulty coping with the fusion processing of multi-dimensional and multi-type data, and it is difficult to achieve accurate modeling and efficient analysis of complex physical systems.

[0004] As an emerging technology concept, digital twin technology provides strong support for IoT applications by establishing real-time mapping between the physical world and the digital world. Digital twin technology can build a digital model corresponding to the physical entity in the virtual space, and achieve accurate simulation and prediction of the physical entity by synchronizing the state changes in the physical space in real time. However, the existing digital twin technology still faces challenges such as high model complexity, high real-time requirements, and low data processing efficiency when facing complex IoT scenarios.

[0005] Therefore, there is an urgent need for an efficient and flexible parameter modeling method for physical space, equipment and their related massive indicators in the field of IoT digital twins, so as to improve the modeling accuracy and analysis efficiency of the digital twin system and meet diverse application needs. Summary of the invention

[0006] The embodiments of the present invention provide an Internet of Things ontology management method, device, equipment, medium and product to provide an efficient and flexible parameter modeling method for the Internet of Things ontology and its related massive indicators, so as to improve the management efficiency of the Internet of Things ontology and meet diverse application needs.

[0007] According to one aspect of the present invention, there is provided an Internet of Things ontology management method, comprising:

[0008] Acquire indicator information, classify each of the indicator information based on preset tags to obtain at least one indicator set, and construct each Internet of Things ontology according to at least one of the indicator sets;

[0009] Obtain the ontology type corresponding to each IoT ontology, the spatial location information of each IoT ontology in the physical space, and the physical subordinate relationship between every two IoT ontology, and construct a spatial matrix;

[0010] Each of the Internet of Things entities is managed according to the space matrix and the early warning rules.

[0011] According to another aspect of the present invention, there is provided an Internet of Things ontology management device, the device comprising:

[0012] An acquisition and classification module, used to acquire indicator information, classify each of the indicator information based on a preset label, obtain at least one indicator set, and construct each Internet of Things ontology according to at least one of the indicator sets;

[0013] An acquisition and construction module, used to acquire the ontology type corresponding to each IoT ontology, the spatial location information of each IoT ontology in the physical space, and the physical subordinate relationship between every two IoT ontology, and to construct a space matrix;

[0014] A management module is used to manage each of the Internet of Things entities according to the space matrix and early warning rules.

[0015] According to another aspect of the present invention, an electronic device is provided, the electronic device comprising:

[0016] at least one processor; and

[0017] a memory communicatively connected to the at least one processor; wherein,

[0018] The memory stores a computer program that can be executed by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can execute the Internet of Things ontology management method described in any embodiment of the present invention.

[0019] According to another aspect of the present invention, a computer-readable storage medium is provided, wherein the computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a processor to implement the Internet of Things ontology management method described in any embodiment of the present invention when executed.

[0020] According to another aspect of the present invention, an embodiment of the present invention further provides a computer program product, the computer program product includes a computer program, and when the computer program is executed by a processor, the Internet of Things ontology management method described in any embodiment of the present invention is implemented.

[0021] The embodiment of the present invention obtains indicator information, classifies each indicator information based on preset tags, obtains at least one indicator set, and constructs each Internet of Things ontology according to at least one indicator set, obtains the ontology type corresponding to each Internet of Things ontology, the spatial location information of each Internet of Things ontology in the physical space, and the physical subordinate relationship between every two Internet of Things ontologies, and constructs a spatial matrix, and manages each Internet of Things ontology according to the spatial matrix and early warning rules. Through the technical solution of the present invention, it is possible to provide an efficient and flexible parameter modeling method for the Internet of Things ontology and its related massive indicators, so as to improve the management efficiency of the Internet of Things ontology and meet diverse application needs.

[0022] It should be understood that the contents described in this section are not intended to identify the key or important features of the embodiments of the present invention, nor are they intended to limit the scope of the present invention. Other features of the present invention will become easily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for use in the embodiments are briefly introduced below. It should be understood that the following drawings only show certain embodiments of the present invention and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without creative work.

[0024] Figure 1 is a flow chart of an Internet of Things ontology management method in an embodiment of the present invention;

[0025] Figure 2 It is a structural diagram of an Internet of Things ontology management device in an embodiment of the present invention;

[0026] Figure 3 It is a structural schematic diagram of an electronic device for implementing the Internet of Things ontology management method according to an embodiment of the present invention. DETAILED DESCRIPTION

[0027] In order to enable those skilled in the art to better understand the scheme of the present invention, the technical scheme in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present invention.

[0028] It should be noted that the terms "first", "second", etc. in the specification and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchanged where appropriate, so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and their meanings are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0029] It is understandable that before using the technical solutions disclosed in the embodiments of the present disclosure, the types, scope of use, usage scenarios, etc. of the personal information involved in the present disclosure should be informed to the user and the user's authorization should be obtained in an appropriate manner in accordance with relevant laws and regulations.

[0030] Embodiment 1

[0031] Figure 1 is a flow chart of an Internet of Things ontology management method in an embodiment of the present invention. This embodiment is applicable to the parameter modeling and management of the digital twin of the Internet of Things ontology. The method can be executed by the Internet of Things ontology management device in the embodiment of the present invention. The device can be implemented in software and / or hardware, such as Figure 1 As shown, the method specifically comprises the following steps:

[0032] S101, obtaining indicator information, classifying each indicator information based on a preset tag, obtaining at least one indicator set, and constructing each Internet of Things ontology according to the at least one indicator set.

[0033] In this embodiment, the Internet of Things entity can be, for example, a component or device that actually exists in the physical space, and the indicator information can be parameters of the Internet of Things entity, such as the model of the device, the temperature and current of the sensor-collected device, and other information.

[0034] The preset label may be a label pre-set according to actual needs for dynamically classifying the collected indicator information. The indicator set may be a set corresponding to each category indicator obtained by classifying all indicator information according to the preset label.

[0035] Specifically, various parameters of the Internet of Things ontology are collected, and the wide variety of parameters are abstracted into indicator information so that it can adapt to various types of parameter definitions of various types of Internet of Things ontologies. Then, a tree-structured label tree is established to dynamically classify the collected various types of indicator information to obtain several indicator sets. After that, each Internet of Things ontology is comprehensively described according to the indicator information in these indicator sets to form a complete Internet of Things ontology.

[0036] S102, obtaining the ontology type corresponding to each IoT ontology, the spatial location information of each IoT ontology in the physical space, and the physical subordinate relationship between every two IoT ontologies, and constructing a space matrix.

[0037] In this embodiment, the ontology type corresponding to the IoT ontology may be an entity type that actually exists in the physical space of the IoT ontology, such as a single device, component, factory, or subway station, etc. The physical subordinate relationship between each two IoT ontology may be a real subordinate relationship of the IoT ontology in the physical space, for example, device A contains component B, and device A exists in factory C.

[0038] It should be noted that the spatial matrix can be a matrix used to represent the hierarchical relationship between all IoT entities. For example, in a factory scenario, the device matrix can represent the positional relationship and collaborative relationship between different devices in the physical space, and the component matrix can represent the relative position and functional coupling between different components in the same device. The spatial matrix can clearly show the layout and arrangement of each IoT entity in the physical space, which helps to analyze the spatial dependency between each two IoT entities. For example, by analyzing the spatial matrix, it can be determined which devices need to work closely together in the factory, and the impact of the physical distance between devices on system performance.

[0039] Specifically, the ontology type corresponding to each IoT ontology, the spatial location information of each IoT ontology in the physical space, and the physical subordinate relationship between every two IoT ontologies are obtained, and the association between every two IoT ontologies is determined according to the physical subordinate relationship between every two IoT ontologies. For example, the IoT device ontology is composed of a set of IoT component ontologies, and the IoT factory ontology is composed of a set of IoT device ontologies. These relationships provide a basis for the subsequent construction of a spatial matrix. Then, the spatial matrix is ​​constructed according to the ontology type corresponding to each IoT ontology, the spatial location information of each IoT ontology in the physical space, and the physical subordinate relationship between every two IoT ontologies.

[0040] S103. Manage each IoT entity according to the space matrix and early warning rules.

[0041] In this embodiment, the warning rules can be set according to the indicator information to trigger an alarm or response under certain conditions. For example, if the equipment energy efficiency score (indicator information) is lower than a certain threshold, a maintenance alarm is triggered; if the temperature (indicator information) of a component exceeds a set value, a speed reduction operation is performed.

[0042] Specifically, the indicator information corresponding to each IoT entity can be queried according to the spatial matrix, and then the management of each IoT entity can be achieved according to the indicator value and early warning rules.

[0043] The embodiment of the present invention obtains indicator information, classifies each indicator information based on preset tags, obtains at least one indicator set, and constructs each Internet of Things ontology according to at least one indicator set, obtains the ontology type corresponding to each Internet of Things ontology, the spatial location information of each Internet of Things ontology in the physical space, and the physical subordinate relationship between every two Internet of Things ontologies, and constructs a spatial matrix, and manages each Internet of Things ontology according to the spatial matrix and early warning rules. Through the technical solution of the present invention, it is possible to provide an efficient and flexible parameter modeling method for the Internet of Things ontology and its related massive indicators, so as to improve the management efficiency of the Internet of Things ontology and meet diverse application needs.

[0044] Optionally, the at least one indicator set includes: at least one of a static attribute indicator set, a dynamic time series indicator set, and a formula indicator set.

[0045] In this embodiment, the static attribute index can be an inherent attribute of the IoT entity, which usually remains unchanged during the life cycle of the IoT entity and constitutes the basic information of the device, and the index information is obtained after abstraction. Exemplarily, the static attribute index can be parameter information such as the model, production date, manufacturer, installation location and capacity of the device.

[0046] In this embodiment, the dynamic timing indicators can be parameters that can reflect the real-time operating status and environmental conditions of the IoT entity, and the indicator information obtained after abstraction. Exemplarily, the dynamic timing indicators can be parameter information such as temperature, humidity, voltage, pressure, current, vibration, etc. of the IoT entity collected in real time by sensors. These dynamic timing indicators can reflect the real-time operating status and environmental conditions of the device and support subsequent dynamic analysis.

[0047] Among them, the formula-based indicators are determined based on preset formulas according to static attribute indicators and / or dynamic timing indicators.

[0048] In this embodiment, the formula-based indicators can be comprehensive indicators calculated based on static attribute indicators and / or dynamic time series indicators through a preset formula or algorithm. The formula is used to generate indicator values ​​through calculation, and is applicable to indicators that need to be dynamically calculated based on other indicators or parameters, such as ambient temperature change ratio, ambient temperature daily peak value, energy efficiency ratio, equipment health score, etc. These formula-based indicators can provide a deeper analysis of complex scenarios.

[0049] This embodiment does not specifically limit the preset formula. For example, some commonly used formulas can be predefined, and users can directly choose to use them. These formulas can cover common calculation requirements, such as sum, average, maximum, minimum, etc. In addition, users can also write custom formulas according to specific needs to flexibly handle specific calculation scenarios. For example, users can perform real-time calculations by referring to other indicators or parameters. For example, the ambient temperature change ratio can be calculated based on the current temperature and the temperature of the previous day, or the daily peak value can be calculated based on the temperature data at different times of the day to monitor the highest temperature of the day. The average temperature at three time points in a day can also be calculated to provide a general temperature indicator.

[0050] Specifically, multiple indicators are calculated through preset formulas to generate new formula indicators. The indicator formula is usually based on a combination of original indicators and can be used to evaluate the performance or health status of the device. Exemplarily, for example, the formula indicator device health score corresponding to the Internet of Things ontology is defined as: Health score = (normal operation time / total operation time) × 100% - (number of failures / total operation time) × 50%. This formula calculates the health score of the device based on the normal operation time and the number of failures of the device. The longer the normal operation time and the fewer the number of failures, the higher the score. For another example, the formula indicator energy efficiency ratio corresponding to the Internet of Things ontology is defined as: Energy efficiency ratio = (output power / input power) × 100%. This formula calculates the energy efficiency ratio of the device, which represents the ratio of output power to input power, expressed as a percentage.

[0051] In actual operation, in order to solve the problem of diversity of indicator business dimensions, the label tree form can be used to manage and organize indicators, which can provide a more flexible and efficient way to classify, query and analyze indicators. The label tree form is a way to organize indicators into a hierarchical structure according to labels, and each indicator can be assigned multiple labels. Labels can be arbitrarily specified according to business needs to form a tree structure for easy classification and management. Specifically, after obtaining the indicator information, a tree-structured label tree can be established to dynamically classify the various types of collected indicator information. Each label represents a specific classification dimension or attribute, such as "environmental indicators", "performance indicators", "health status", etc. Labels are organized in a tree structure to form a label tree. The root node of the label tree represents the highest level of classification, such as "physical attributes", "operating status", etc. Each child node represents a more specific classification level. For example, under the "physical attributes" node, there can be sub-labels such as "temperature", "humidity", and "voltage". Through this tree structure, indicators in complex scenarios can be classified and retrieved at multiple levels and dimensions, thereby meeting the classification requirements in different application scenarios.

[0052] The tag tree has the ability to dynamically expand and adjust. When new devices, sensors, or new application scenarios are introduced into the IoT system, the tag tree can be expanded according to demand, new tags can be added, or the hierarchy of tags can be adjusted. This flexible adjustment mechanism ensures that the system can adapt to the ever-changing IoT environment and maintain the effectiveness and accuracy of classification management.

[0053] In actual operation, the implementation steps of the tag tree form can be described as follows: Tag library establishment: Establish a tag library to define all possible tags and their hierarchical relationships, for example, classification by region, product line, time dimension, etc. Tag hierarchy: Design the hierarchical structure of tags, clarify the relationship between parent tags and child tags, and adopt a tree structure, with the top level being the major category tags and the child tags being subdivided level by level. Tag allocation: Assign appropriate tags to each indicator, and specify one or more tags. Tag maintenance: Regularly maintain and update the tag library, add new tags or modify existing tags to adapt to business changes. Tag tree display: The development interface displays the tag tree, supporting users to browse and manage tags. Tag operation: Provides an operation interface for adding, deleting, and modifying tags, supporting users to dynamically manage the tag tree. Tag query: Supports querying indicators by tag, and users can filter indicators by selecting one or more tags. Tag combination query: Supports tag combination query, and users can combine multiple tags through Boolean operations (such as AND, OR) to perform complex queries. Tag statistical analysis: Provides tag-based statistical analysis functions, and users can view the distribution and changes of indicators by tag dimension.

[0054] In this embodiment, the use of a tag tree to manage and organize indicators has the following advantages: Flexibility: Supports multi-dimensional tags and can adapt to complex and diverse business needs. Scalability: The tag tree structure can be dynamically expanded, and new tags and levels can be added at any time. Visualization: Through the tree structure, indicator classification can be intuitively viewed and managed. Efficient query: Supports fast query and filtering by tag to improve data analysis efficiency.

[0055] Each IoT ontology is constructed based on at least one set of indicators, including:

[0056] The Internet of Things ontology is composed of at least one indicator set. Each Internet of Things ontology is comprehensively described by static attribute indicators, dynamic timing indicators and formula indicators to form a complete Internet of Things ontology.

[0057] In this embodiment, static attribute indicators, dynamic timing indicators and formula indicators are defined for each IoT entity according to all indicator sets. Specifically, static attributes are defined for each IoT entity, such as device model, manufacturer, installation location, etc.; dynamic timing indicators related to each IoT entity are determined, such as temperature, humidity, power consumption, etc.; formula indicators can be calculated through static attribute indicators, dynamic timing indicators or other formula indicators.

[0058] In this embodiment, the unit and field type of the indicator can also be uniformly defined in the form of a label tree, which is helpful for standardized management and improving the efficiency of query and analysis. For example, the unit of the indicator can be defined as follows: top-level label: major units (such as length, mass, time, currency, temperature, etc.); length: meter (m), kilometer (km), centimeter (cm), etc.; mass: kilogram (kg), gram (g), ton (t); time: second (s), minute (min), hour (h); temperature: Celsius (℃), Fahrenheit (℉), etc. The field types that can be defined for indicators are: Top-level label: major type (such as numeric type, text type, date and time type, Boolean type, etc.) Numeric type: integer (Integer), floating point number (Float), double precision number (Double); Text type: string (String), character (Char); Date and time type: date (Date), time (Time), date and time (Date Time); Boolean type: Boolean (Boolean); Complex type: multiple choice (Array), single choice (Object), such as used to select label trees, form instances, etc.

[0059] In actual operation, in order to better manage and use indicator value ranges, different value range types of indicators can be configured, including range, enumeration, label, form selection and formula, etc. The value range is defined as follows:

[0060] Range: The range domain is used to specify the minimum and maximum values ​​of digital indicator values ​​to ensure that the indicator is within a certain numerical range. It is suitable for indicators that need to limit the numerical range, such as temperature, humidity, current, voltage, etc. By defining the minimum and maximum values, the system can verify whether the collected indicator value is within the allowed range. The range domain usually contains the following parameters: Minimum value: the lower limit of the value domain. Maximum value: the upper limit of the value domain. When an indicator value is collected, the system will check whether the value is within the predefined range. If not, it will prompt a data error for the business layer to handle.

[0061] Enumeration: The enumeration value domain is used to specify a fixed set of possible values, so that the indicator can only be selected from this set of values. It is suitable for indicators with fixed and limited options, such as switch status, indicator light status, etc. By defining a fixed set of enumeration values, the system determines whether the collected data meets the enumeration value definition. The enumeration value domain usually contains the following parameters: Enumeration value list: a set of all possible values. When an indicator value is collected, the system verifies whether the value is in the enumeration value list. If not, it indicates a data error for the business layer to handle.

[0062] Tags: Tag ranges are used to specify a set of tags, so that an indicator can determine its range by selecting tags on the tag tree. Applicable to indicators that require flexible classification and multi-dimensional labeling, such as classification, tags, etc. By defining a tag tree, users can select a tag on the tree and specify the subordinate nodes of the tag as the range of the indicator. The tag range usually contains the following parameters: Tag tree: A hierarchical structure containing all possible tags. Tag selection: A tag node selected by the user. When a user selects a tag as the range of an indicator, the system uses all child nodes of the tag as possible values ​​of the indicator. Users can browse and select through the tag tree to ensure flexibility and diversity of indicator values.

[0063] Form selection: The form selection value domain is used to use the form data entered by the user as the source of the label value domain, so that the indicator can determine its value domain by selecting a specific form. It is suitable for indicators that need to use multiple forms entered by the user as input, such as brand selection, supplier selection, etc. By using the form data entered by the user as the label value domain, the system allows the user to select these form data as its value domain in the indicator. The form selection value domain usually contains the following parameters: Form collection: Contains all forms entered by the user. Form selection: The user selects a specific form as the value domain of the indicator. When the user selects a specific form as the value domain of the indicator, the system uses the data in the form as the possible value of the indicator. This method is particularly suitable for scenarios where user input data needs to be dynamically obtained and used.

[0064] Optionally, the ontology type corresponding to each IoT ontology, the spatial location information of each IoT ontology in the physical space, and the physical subordinate relationship between every two IoT ontologies are obtained, and a spatial matrix is ​​constructed, including:

[0065] Each parent-child ontology pair is determined according to the physical subordinate relationship between every two IoT ontologies.

[0066] It should be explained that the parent-child ontology pair can be two IoT ontology pairs with a subordinate relationship. For example, if device A contains component B, then in this parent-child ontology pair, device A is the parent ontology and component B is the child ontology.

[0067] Specifically, the association between each two IoT ontologies is determined based on the physical subordinate relationship between them. For example, the device ontology is composed of a set of component ontologies, while the factory ontology is composed of a set of multiple device ontologies. These relationships provide the basis for the subsequent construction of the spatial matrix.

[0068] The spatial position information of the sub-ontology in each parent-child ontology pair in the parent ontology corresponding to the sub-ontology is determined according to the spatial position information of each Internet of Things ontology in the physical space.

[0069] In this embodiment, the specific position of each IoT entity in the physical space can be represented by the eight vertex coordinates of a cuboid, for example, from (x1, y1, z1) to (x8, y8, z8). After knowing the spatial position information of each IoT entity in the physical space, the spatial position information of the sub-entity in each parent-child entity pair in the parent entity corresponding to the sub-entity can be calculated.

[0070] The spatial position information of each sub-body in the parent body corresponding to the sub-body is determined as an element in the spatial matrix corresponding to the parent body to obtain the spatial matrix.

[0071] Specifically, the spatial position information of each sub-ontology in the parent ontology corresponding to the sub-ontology is determined as an element in the spatial matrix corresponding to the parent ontology. The hierarchical structure between every two IoT ontologies can be represented by a nested matrix, and finally a spatial matrix is ​​obtained.

[0072] Optionally, the ontology types corresponding to the Internet of Things ontology include: Internet of Things component ontology, Internet of Things device ontology and Internet of Things factory ontology.

[0073] The physical subordinate relationship between every two IoT ontologies includes: the IoT device ontologies are composed of a set of IoT component ontologies, and the IoT factory ontologies are composed of a set of IoT device ontologies.

[0074] The spatial matrix describes the hierarchical relationship between the IoT ontologies and the three-dimensional spatial position of the child ontologies in the parent ontologies. The specific position of each IoT ontology in space can be represented by the eight vertex coordinates of a cuboid.

[0075] For example, let M be a spatial matrix, and each element M in the matrix i,j Represents the Internet of Things ontology O i In its parent body O j Factories, workshops, equipment and other entities can be represented by the nested structure of matrices. For example, the spatial matrix of a factory can be represented as: 工厂 =(M 车间1 , M 车间2 , …, M 车间n ), the equipment inside each workshop can also be expressed in a similar way: M 车间1 =(O 设备1 , O 设备2 , …, O 设备m ).

[0076] For each entity O i , whose position in space is represented by the coordinates of the eight vertices of the cuboid (x1, y1, z1) to (x8, y8, z8), for example: M 工厂 (O 车间1 ) = [(x1, y1, z1), (x2, y2, z2), …, (x8, y8, z8)]. This representation not only clarifies the position of each IoT entity in the three-dimensional space, but also can calculate the spatial relationship between every two IoT entities through these coordinates, such as overlap, adjacency, inclusion, etc.

[0077] As the system expands, the spatial matrix of the IoT ontology can be dynamically expanded. For example, when a new device is added, the matrix of the workshop where the device is located can be updated: M' 车间 =(O 设备1 , O 设备2 , …, O 设备(m+1) ), this expansion capability ensures the flexibility and adaptability of the system.

[0078] Optionally, manage each IoT entity according to the spatial matrix and warning rules, including:

[0079] By indexing the spatial matrix, the sub-ontology corresponding to each IoT ontology is obtained.

[0080] Get the indicator set of the sub-ontology corresponding to each IoT ontology.

[0081] Specifically, the management of each IoT ontology requires the corresponding indicator information of each IoT ontology. The sub-ontologies included in each IoT ontology can be indexed through the spatial matrix, and then the indicator information corresponding to these sub-ontologies can be obtained. For example, the energy efficiency score of a device may require the calculation of the energy efficiency indicators of its subordinate components, and the energy efficiency indicators of these components can be indexed through the spatial matrix.

[0082] If there are indicator values ​​in the indicator set that meet the warning conditions, a warning message is generated.

[0083] In this embodiment, the warning condition can be a warning condition of abnormal indicators pre-configured by the user according to actual needs, and a warning message is issued in a timely manner when the warning condition is met. Exemplarily, the warning condition can be, for example, a certain threshold, or a certain value range, or a certain state, a certain level, etc., which is not limited in this embodiment.

[0084] Specifically, by defining warning rules, the system can monitor the indicator value. When the indicator value exceeds the preset range or meets specific conditions, the system will trigger the corresponding warning. Users can set different warning levels according to business needs, and the system will take different response measures according to different warning levels.

[0085] For example, users can configure range-based warning rules: Upper and lower limit settings: Users can set upper and lower limits for indicator values. When the indicator value exceeds the range, the system will trigger an alert. Multi-interval configuration: Users can define multiple intervals, each of which corresponds to a different warning level. For example, the indicator value is a low-level warning in a certain interval, a medium-level warning in another interval, and a high-level warning in a higher interval. In addition, users can also customize the warning level: Warning level definition: Users can customize the warning level and its corresponding name, color, and processing method. For example, the warning level can be defined as "low-level warning", "medium-level warning" and "high-level warning", corresponding to different colors and notification methods. Warning response settings: Users can set different response measures for each warning level, such as sending emails, SMS notifications, triggering automatic processing programs, etc. In addition, users can also manage warning rules: Rule creation and editing: Users can easily create and edit warning rules, define trigger conditions and corresponding warning levels. Rule enablement and disablement: Users can enable or disable specific warning rules and flexibly control the effectiveness of warning rules.

[0086] Exemplarily, if there is an indicator value in the indicator set that meets the warning condition, the generated warning information can be, for example: Temperature monitoring warning: Range warning rule: Set the normal range of the temperature indicator to 0°C to 30°C. When the temperature is lower than 0°C or higher than 30°C, a low-level warning is triggered; when the temperature is lower than -10°C or higher than 40°C, a high-level warning is triggered. Customized warning level: low-level warning (yellow), send email notification; high-level warning (red), send email and SMS notification, and trigger the emergency handling procedure at the same time.

[0087] Exemplarily, if there is an indicator value in the indicator set that meets the warning condition, the generated warning information can be, for example: Equipment operation status warning: Range warning rule: Set the normal range of equipment operation time to 8 to 12 hours per day. When it is less than 8 hours or more than 12 hours, trigger the warning. Customized warning level: Low-level warning (blue), record logs; medium-level warning (orange), send email notifications; high-level warning (red), send email and SMS notifications, and start the backup device at the same time.

[0088] The technical solution of the embodiment of the present invention can be applied to scenarios such as environmental monitoring, equipment maintenance, and business operations. For example, environmental monitoring: monitor indicators such as ambient temperature and humidity, and issue warnings in a timely manner when the indicators exceed the preset range to prevent harm caused by environmental anomalies. Equipment maintenance: monitor the operating status of the equipment, and issue warnings of equipment failures or anomalies in a timely manner to ensure the normal operation of the equipment and production safety. Business operations: monitor key business indicators such as sales, customer satisfaction, etc., promptly discover and handle abnormal situations, optimize business processes and improve operational efficiency. According to the technical solution of the embodiment of the present invention, users can customize warning rules and warning levels according to actual needs to meet various complex business scenarios; after configuring the warning rules, the system can monitor the indicator values ​​in real time, and when the indicators are abnormal, trigger warnings in a timely manner to ensure rapid response, providing powerful monitoring and warning capabilities, helping users to promptly discover and handle abnormal situations, and improving system reliability and business continuity.

[0089] Optionally, after obtaining the indicator set of the sub-ontology corresponding to each IoT ontology, it also includes:

[0090] Based on the preset Internet of Things ontology evaluation formula, the indicator values ​​in the indicator set are calculated to obtain the Internet of Things ontology evaluation indicators.

[0091] In this embodiment, the behavior formula is used to describe the dynamic state or performance of the ontology, and the formula may involve the indicators of the subordinate ontology and be indexed by the spatial matrix. For example, the energy efficiency score of a device may require calculating the energy efficiency of its subordinate components and indexing the energy efficiency indicators of these components by the spatial matrix.

[0092] Exemplarily, the IoT ontology evaluation formula can be expressed as:

[0093]

[0094] Among them, α k is the weight of each component, M 设备 (O 部件k ) represents the energy efficiency index obtained by the component index in the device space matrix. The IoT ontology evaluation index here is the energy efficiency score. In other embodiments, different IoT ontology evaluation formulas can also be set according to actual needs to obtain different IoT ontology evaluation indicators.

[0095] Each IoT ontology is evaluated based on the IoT ontology evaluation indicators.

[0096] Specifically, the status of each IoT ontology during operation is evaluated through the calculated IoT ontology evaluation indicators.

[0097] Through the above steps, the spatial matrix not only clarifies the spatial position and hierarchical relationship of each IoT entity, but also provides a basis for complex behavioral calculations and interactions. Through the dynamic matrix structure and flexible behavioral formulas, complex scenarios and changing requirements in the IoT system can be accurately expressed and responded to.

[0098] The technical solution of the embodiment of the present invention proposes a parameter modeling method for the field of digital twins of the Internet of Things, which is particularly suitable for digital modeling and analysis of physical space, equipment and massive indicators. The method accurately models the complex and changeable Internet of Things ontology and the relationship between them based on indicators, Internet of Things ontology and spatial matrix. First, various indicators of the Internet of Things ontology are defined and configured, including static attribute indicators, dynamic time series indicators and formula indicators calculated by one or more indicators. Then, a tree-structured label system is used to dynamically classify and manage indicators to ensure flexibility in complex scenarios. Subsequently, multiple indicator sets are constructed as Internet of Things ontology, such as equipment, components or factories, and the hierarchical relationship between ontologies, such as the subordinate relationship between equipment and components, and between factories and equipment, is represented by a spatial matrix. By constructing and optimizing parameter models, accurate description and dynamic update of various entities and their relationships in the Internet of Things system are achieved. The technical solution of the embodiment of the present invention accurately describes the equipment and its operating status in the physical space by constructing a digital model corresponding to the physical entity, and establishes a mathematical model related to the physical parameters through the collected massive index data. The model can be customized and optimized according to the needs of specific application scenarios to achieve real-time monitoring, prediction and analysis of the physical space and equipment status. The technical solution of the embodiment of the present invention can effectively cope with the diversity of equipment and environments in IoT applications. By providing an abstract and unified parameter model, it can achieve consistent modeling, analysis and optimization in different devices and environments, ensuring that when facing complex and changeable IoT applications, parameters can be quickly adjusted and operations can be optimized to ensure the flexibility and adaptability of the system.

[0099] Embodiment 2

[0100] Figure 2 This is a schematic diagram of the structure of an IoT ontology management device in an embodiment of the present invention. This embodiment is applicable to the parameter modeling and management of the IoT ontology digital twin. The device can be implemented in software and / or hardware. The device can be integrated in any device that provides the function of IoT ontology management, such as Figure 2 As shown, the Internet of Things ontology management device specifically includes: an acquisition and classification module 201, an acquisition and construction module 202 and a management module 203.

[0101] The acquisition and classification module 201 is used to acquire indicator information, classify each indicator information based on a preset tag, obtain at least one indicator set, and construct each Internet of Things ontology according to at least one indicator set;

[0102] The acquisition and construction module 202 is used to acquire the ontology type corresponding to each IoT ontology, the spatial location information of each IoT ontology in the physical space, and the physical subordinate relationship between every two IoT ontology, and construct a space matrix;

[0103] The management module 203 is used to manage each of the Internet of Things entities according to the space matrix and the warning rules.

[0104] Optionally, at least one of the indicator sets includes: at least one of a static attribute indicator set, a dynamic time series indicator set, and a formula indicator set, wherein the formula indicator is determined according to the static attribute indicator and / or the dynamic time series indicator based on a preset formula;

[0105] The acquisition and classification module 201 is specifically used for:

[0106] The Internet of Things ontology is composed of at least one of the indicator sets, and each of the Internet of Things ontology is comprehensively described by the static attribute indicators, the dynamic timing indicators and the formula indicators to form a complete Internet of Things ontology.

[0107] Optionally, the acquisition and construction module 202 is specifically used for:

[0108] Determine each parent-child ontology pair according to the physical subordinate relationship between each two of the Internet of Things ontologies;

[0109] Determine the spatial position information of the sub-ontology in each of the parent-child ontology pairs in the parent ontology corresponding to the sub-ontology according to the spatial position information of each of the Internet of Things ontology in the physical space;

[0110] The spatial position information of each sub-body in the parent body corresponding to the sub-body is determined as an element in the spatial matrix corresponding to the parent body to obtain the spatial matrix.

[0111] Optionally, the ontology types corresponding to the Internet of Things ontology include: Internet of Things component ontology, Internet of Things device ontology and Internet of Things factory ontology; the physical subordinate relationship between every two of the Internet of Things ontologies includes: the Internet of Things device ontology is composed of a set of Internet of Things component ontologies, and the Internet of Things factory ontology is composed of a set of Internet of Things device ontologies.

[0112] Optionally, the management module 203 includes:

[0113] An indexing unit, used to obtain a sub-ontology corresponding to each of the Internet of Things ontology by indexing the space matrix;

[0114] An acquisition unit, used to acquire an indicator set of a sub-ontology corresponding to each of the Internet of Things ontology;

[0115] A generating unit is used to generate warning information if there is an indicator value in the indicator set that meets the warning condition.

[0116] Optionally, the device further comprises:

[0117] A calculation unit, used to calculate the indicator values ​​in the indicator set based on a preset Internet of Things ontology evaluation formula to obtain an Internet of Things ontology evaluation indicator;

[0118] An evaluation unit is used to evaluate each of the Internet of Things ontology based on the Internet of Things ontology evaluation index.

[0119] The above-mentioned product can execute the Internet of Things ontology management method provided by any embodiment of the present invention, and has the corresponding functional modules and beneficial effects of the execution method.

[0120] Embodiment 3

[0121] Figure 3 A schematic diagram of the structure of an electronic device 30 that can be used to implement an embodiment of the present invention is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices (such as helmets, glasses, watches, etc.) and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present invention described and / or required herein.

[0122] like Figure 3 As shown, the electronic device 30 includes at least one processor 31, and a memory connected to the at least one processor 31, such as a read-only memory (ROM) 32, a random access memory (RAM) 33, etc., wherein the memory stores a computer program that can be executed by at least one processor, and the processor 31 can perform various appropriate actions and processes according to the computer program stored in the read-only memory (ROM) 32 or the computer program loaded from the storage unit 38 to the random access memory (RAM) 33. In the RAM 33, various programs and data required for the operation of the electronic device 30 can also be stored. The processor 31, the ROM 32, and the RAM 33 are connected to each other through a bus 34. An input / output (I / O) interface 35 is also connected to the bus 34.

[0123] A number of components in the electronic device 30 are connected to the I / O interface 35, including: an input unit 36, such as a keyboard, a mouse, etc.; an output unit 37, such as various types of displays, speakers, etc.; a storage unit 38, such as a disk, an optical disk, etc.; and a communication unit 39, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 39 allows the electronic device 30 to exchange information / data with other devices through a computer network such as the Internet and / or various telecommunication networks.

[0124] The processor 31 may be a variety of general and / or special processing components with processing and computing capabilities. Some examples of the processor 31 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, digital signal processors (DSPs), and any appropriate processors, controllers, microcontrollers, etc. The processor 31 executes the various methods and processes described above, such as the IoT ontology management method:

[0125] Acquire indicator information, classify each of the indicator information based on preset tags to obtain at least one indicator set, and construct each Internet of Things ontology according to at least one of the indicator sets;

[0126] Obtain the ontology type corresponding to each IoT ontology, the spatial location information of each IoT ontology in the physical space, and the physical subordinate relationship between every two IoT ontology, and construct a spatial matrix;

[0127] Each of the Internet of Things entities is managed according to the space matrix and the early warning rules.

[0128] In some embodiments, the Internet of Things ontology management method may be implemented as a computer program, which is tangibly contained in a computer-readable storage medium, such as a storage unit 38. In some embodiments, part or all of the computer program may be loaded and / or installed on the electronic device 30 via the ROM 32 and / or the communication unit 39. When the computer program is loaded into the RAM 33 and executed by the processor 31, one or more steps of the Internet of Things ontology management method described above may be performed. Alternatively, in other embodiments, the processor 31 may be configured to execute the Internet of Things ontology management method in any other appropriate manner (e.g., by means of firmware).

[0129] Various implementations of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on chips (SOCs), load programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various implementations can include: being implemented in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which can be a special purpose or general purpose programmable processor that can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.

[0130] Computer programs for implementing the methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, so that when the computer program is executed by the processor, the functions / operations specified in the flow chart and / or block diagram are implemented. The computer program may be executed entirely on the machine, partially on the machine, partially on the machine and partially on a remote machine as a stand-alone software package, or entirely on a remote machine or server.

[0131] In the context of the present invention, a computer-readable storage medium may be a tangible medium that may contain or store a computer program for use by or in combination with an instruction execution system, device or equipment. A computer-readable storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices or equipment, or any suitable combination of the foregoing. Alternatively, a computer-readable storage medium may be a machine-readable signal medium. A more specific example of a machine-readable storage medium may include an electrical connection based on one or more lines, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0132] To provide interaction with a user, the systems and techniques described herein may be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and a pointing device (e.g., a mouse or trackball) through which the user can provide input to the electronic device. Other types of devices may also be used to provide interaction with the user; for example, the feedback provided to the user may be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form (including acoustic input, voice input, or tactile input).

[0133] The systems and techniques described herein may be implemented in a computing system that includes backend components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes frontend components (e.g., a user computer with a graphical user interface or a web browser through which a user can interact with implementations of the systems and techniques described herein), or a computing system that includes any combination of such backend components, middleware components, or frontend components. The components of the system may be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: a local area network (LAN), a wide area network (WAN), a blockchain network, and the Internet.

[0134] A computing system may include a client and a server. The client and the server are generally remote from each other and usually interact through a communication network. The client and server relationship is generated by computer programs running on the corresponding computers and having a client-server relationship with each other. The server may be a cloud server, also known as a cloud computing server or cloud host, which is a host product in the cloud computing service system to solve the defects of difficult management and weak business scalability in traditional physical hosts and VPS services.

[0135] In one embodiment, the embodiment of the present invention further includes a computer program product, the computer program product includes a computer program, and the computer program implements the Internet of Things ontology management method of any embodiment of the present invention when executed by a processor.

[0136] In the process of implementation, the computer program product can be written in one or more programming languages ​​or a combination thereof to perform the computer program code of the present invention, including object-oriented programming languages, such as Java, Smalltalk, C++, and conventional procedural programming languages, such as "C" language or similar programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a separate software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In the case of a remote computer, the remote computer can be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or can be connected to an external computer (for example, using an Internet service provider to connect through the Internet).

[0137] It should be understood that the various forms of processes shown above can be used to reorder, add or delete steps. For example, the steps described in the present invention can be executed in parallel, sequentially or in different orders, as long as the desired results of the technical solution of the present invention can be achieved, and this document does not limit this.

[0138] The above specific implementations do not constitute a limitation on the protection scope of the present invention. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions can be made according to design requirements and other factors. Any modification, equivalent substitution and improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A method for managing an Internet of Things ontology, characterized in that: include: Acquire indicator information, classify each of the indicator information based on preset tags to obtain at least one indicator set, and construct each Internet of Things ontology according to at least one of the indicator sets; Obtain the ontology type corresponding to each IoT ontology, the spatial location information of each IoT ontology in the physical space, and the physical subordinate relationship between every two IoT ontology, and construct a spatial matrix; Managing each of the Internet of Things entities according to the space matrix and the early warning rules; Among them, obtaining the ontology type corresponding to each Internet of Things ontology, the spatial location information of each Internet of Things ontology in the physical space, and the physical subordinate relationship between every two Internet of Things ontology, and constructing a space matrix, includes: Determine each parent-child ontology pair according to the physical subordinate relationship between each two of the Internet of Things ontologies; Determine the spatial position information of the sub-ontology in each of the parent-child ontology pairs in the parent ontology corresponding to the sub-ontology according to the spatial position information of each of the Internet of Things ontology in the physical space; The spatial position information of each sub-body in the parent body corresponding to the sub-body is determined as an element in the spatial matrix corresponding to the parent body to obtain the spatial matrix.

2. The method according to claim 1, characterized in that At least one of the indicator sets includes: at least one of a static attribute indicator set, a dynamic time series indicator set, and a formula indicator set, wherein the formula indicator is determined according to the static attribute indicator and / or the dynamic time series indicator based on a preset formula; Constructing each IoT ontology according to at least one of the indicator sets includes: The Internet of Things ontology is composed of at least one of the indicator sets, and each of the Internet of Things ontology is comprehensively described by the static attribute indicators, the dynamic timing indicators and the formula indicators to form a complete Internet of Things ontology.

3. The method according to claim 1, characterized in that The ontology types corresponding to the Internet of Things ontology include: Internet of Things component ontology, Internet of Things device ontology and Internet of Things factory ontology; the physical subordinate relationship between every two of the Internet of Things ontologies includes: the Internet of Things device ontology is composed of a set of Internet of Things component ontologies, and the Internet of Things factory ontology is composed of a set of Internet of Things device ontologies.

4. The method according to claim 1, characterized in that: Managing each of the Internet of Things entities according to the space matrix and the early warning rules includes: By indexing the space matrix, a sub-ontology corresponding to each of the Internet of Things ontology is obtained; Obtaining a set of indicators of the sub-ontology corresponding to each of the Internet of Things ontology; If there is an indicator value in the indicator set that meets the warning condition, a warning message is generated.

5. The method according to claim 4, characterized in that After obtaining the indicator set of each sub-ontology corresponding to the Internet of Things ontology, the method further includes: Calculate the indicator values ​​in the indicator set based on a preset Internet of Things ontology evaluation formula to obtain the Internet of Things ontology evaluation indicator; Each of the Internet of Things ontology is evaluated based on the Internet of Things ontology evaluation index.

6. An Internet of Things ontology management device, characterized in that: include: An acquisition and classification module, used to acquire indicator information, classify each of the indicator information based on a preset label, obtain at least one indicator set, and construct each Internet of Things ontology according to at least one of the indicator sets; An acquisition and construction module, used to acquire the ontology type corresponding to each IoT ontology, the spatial location information of each IoT ontology in the physical space, and the physical subordinate relationship between every two IoT ontology, and to construct a space matrix; A management module, used to manage each of the Internet of Things entities according to the space matrix and the warning rules; The acquisition and construction module is specifically used for: Determine each parent-child ontology pair according to the physical subordinate relationship between each two of the Internet of Things ontologies; Determine the spatial position information of the sub-ontology in each of the parent-child ontology pairs in the parent ontology corresponding to the sub-ontology according to the spatial position information of each of the Internet of Things ontology in the physical space; The spatial position information of each sub-body in the parent body corresponding to the sub-body is determined as an element in the spatial matrix corresponding to the parent body to obtain the spatial matrix.

7. An electronic device, characterized in that: The electronic device comprises: at least one processor; and a memory communicatively connected to the at least one processor; wherein, The memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can execute the Internet of Things ontology management method according to any one of claims 1 to 5.

8. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a processor to implement the Internet of Things ontology management method according to any one of claims 1 to 5 when executed.

9. A computer program product, comprising a computer program, wherein when the computer program is executed by a processor, the computer program implements the Internet of Things ontology management method according to any one of claims 1 to 5.

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