A method, apparatus, device and medium for Internet of Things (IoT) data management

Abstract

This invention discloses an IoT data management method, apparatus, device, and medium. The method includes: acquiring IoT data in real time; if the difference between the generation time of a parameter value and the current time is less than a first preset time threshold, processing the IoT data to obtain first processed data; determining second processed data based on the IoT data and sending the second processed data to a second database; if the difference between the generation time of a parameter value and the current time is greater than a second preset time threshold, processing the IoT data to obtain third processed data and storing the third processed data in the second database; and responding to a data acquisition request from a target application, determining target IoT data matching the data acquisition request. By implementing this solution, applications with real-time requirements for IoT data and applications with big data analytics requirements for IoT data can be simultaneously addressed.

Description

Technical Field

[0001] This invention relates to the technical field of digital building, and in particular to an Internet of Things (IoT) data management method, apparatus, equipment, and medium. Background Technology

[0002] Current building intelligence applications heavily rely on IoT data. A common problem across various applications is that some applications require rapid processing of IoT data, pursuing high real-time performance, stability, and security; while others prioritize big data processing. Although these two application directions share the same data source, their data usage needs differ significantly, making it easy to overlook one aspect when providing data services.

[0003] The reason is that the underlying data transmission uses local communication, such as RS485, Wi-Fi, and LoRa, which are commonly used in construction sites. These types of communication are characterized by high speed but small capacity. On the other hand, after a large amount of data is collected, it is often uploaded to the cloud. Cloud communication uses the TCP / IP protocol and is stored in a database. In this case, data retrieval is characterized by low speed but large capacity. This also reflects the contradiction between these two application directions. Summary of the Invention

[0004] This invention provides an IoT data management method, apparatus, device, and medium that can simultaneously meet the needs of applications requiring real-time IoT data as well as applications requiring big data analysis of IoT data.

[0005] According to one aspect of the present invention, an Internet of Things (IoT) data management method is provided, the method comprising:

[0006] Real-time acquisition of IoT data; the IoT data includes object parameter name, object parameter value, and parameter value generation time;

[0007] If the difference between the parameter value generation time and the current time is determined to be less than a first preset time threshold, then the IoT data is processed to obtain first processed data, and the first processed data is stored in a first database. Additionally, second processed data is determined based on the IoT data, and the second processed data is sent to a second database. The second processed data includes non-object parameter names.

[0008] If the difference between the parameter value generation time and the current time is determined to be greater than a second preset time threshold, the IoT data is processed to obtain third processed data, and the third processed data is stored in the second database; the data reading speed of the second database is lower than the data reading speed of the first database; the data storage capacity of the second database is greater than the data storage capacity of the first database; the second preset time threshold is greater than the first preset time threshold.

[0009] In response to a data acquisition request from a target application, target IoT data matching the data acquisition request is determined from the first database or the second database.

[0010] According to another aspect of the present invention, an Internet of Things (IoT) data management device is provided, the device comprising:

[0011] The IoT data determination module is used to acquire IoT data in real time; the IoT data includes object parameter name, object parameter value, and parameter value generation time.

[0012] The first processing data and second processing data determination module is used to process the IoT data to obtain first processing data if the difference between the parameter value generation time and the current time is less than a first preset time threshold, and to store the first processing data in a first database; and to determine second processing data based on the IoT data and send the second processing data to a second database; the second processing data includes a non-object parameter name;

[0013] The third processing data determination module is used to process the IoT data to obtain third processing data if the difference between the parameter value generation time and the current time is greater than a second preset time threshold, and store the third processing data in the second database; the data reading speed of the second database is lower than the data reading speed of the first database; the data storage capacity of the second database is greater than the data storage capacity of the first database; the second preset time threshold is greater than the first preset time threshold.

[0014] The target data determination module is used to determine target IoT data that matches the data acquisition request from the first database or the second database in response to a data acquisition request from a target application.

[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, which enables the at least one processor to perform the Internet of Things data management method according to any embodiment of the present invention.

[0019] According to another aspect of the present invention, a computer-readable storage medium is provided, the computer-readable storage medium storing computer instructions for causing a processor to execute and implement the Internet of Things data management method according to any embodiment of the present invention.

[0020] The technical solution of this invention provides real-time acquisition of IoT data. The IoT data includes object parameter names, object parameter values, and parameter value generation times. If the difference between the parameter value generation time and the current time is less than a first preset time threshold, the IoT data is processed to obtain first processed data, which is stored in a first database. Additionally, second processed data is determined based on the IoT data and sent to a second database. The second processed data includes non-object parameter names. If the difference between the parameter value generation time and the current time is greater than a second preset time threshold, the IoT data is processed to obtain third processed data, which is stored in the second database. The data reading speed of the second database is lower than that of the first database. The data storage capacity of the second database is greater than that of the first database. The second preset time threshold is greater than the first preset time threshold. In response to a data acquisition request from a target application, target IoT data matching the data acquisition request is determined from either the first or second database. By implementing the technical solution provided by this invention, applications requiring real-time IoT data and applications requiring big data analytics on IoT data can be simultaneously implemented.

[0021] It should be understood that the description in this section is not intended to identify key or essential features of the embodiments of the present invention, nor is it intended to limit the scope of the invention. Other features of the invention will become readily apparent from the following description. Attached Figure Description

[0022] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0023] Figure 1 This is a flowchart of an IoT data management method provided in an embodiment of the present invention;

[0024] Figure 2 This is a flowchart of another IoT data management method provided in an embodiment of the present invention;

[0025] Figure 3 This is a schematic diagram of the structure of an Internet of Things (IoT) data management device provided in an embodiment of the present invention;

[0026] Figure 4 This is a schematic diagram of the structure of an electronic device that implements the Internet of Things data management method of this invention. Detailed Implementation

[0027] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort 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, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of the invention described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover a non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0029] It is understood that before using the technical solutions disclosed in the various embodiments of the present invention, users should be informed of the types, scope of application, and usage scenarios of the personal information involved in the present invention and their authorization should be obtained in accordance with relevant laws and regulations through appropriate means.

[0030] For example, upon receiving a user's active request, a prompt message is sent to the user to explicitly inform them that the requested operation will require the acquisition and use of the user's personal information. This allows the user to independently choose whether to provide personal information to the software or hardware, such as the electronic device, application program, server, or storage medium executing the operation of this invention, based on the prompt message.

[0031] As an optional but non-limiting implementation, in response to a user's active request, sending a prompt message to the user can be done via a pop-up window, where the prompt message can be presented in text format. Furthermore, the pop-up window can also include a selection control allowing the user to choose "agree" or "disagree" to provide personal information to the electronic device.

[0032] It is understood that the above notification and user authorization process is merely illustrative and does not constitute a limitation on the implementation of the present invention. Other methods that comply with relevant laws and regulations may also be applied to the implementation of the present invention.

[0033] It is understood that the data involved in this technical solution (including but not limited to the data itself, the acquisition or use of the data) shall comply with the requirements of relevant laws, regulations and related provisions.

[0034] Figure 1 This is a flowchart of an IoT data management method provided in an embodiment of the present invention. This embodiment is applicable to the management of IoT data in the building sector. The method can be executed by an IoT data management device, which can be implemented in hardware and / or software and can be configured in an electronic device for IoT data management. Figure 1 As shown, the method includes:

[0035] S110: Acquire IoT data in real time.

[0036] The IoT data includes the object parameter name, the object parameter value, and the parameter value generation time.

[0037] For example, IoT data can be configured according to actual needs. For instance, IoT data can be data from underlying devices in the building sector, such as voltage and current data from transformers. IoT data can also be non-underlying device data, which includes non-object parameter names.

[0038] Assuming the IoT data is voltage data, the object parameter name is transformer voltage, the object parameter value is the voltage value, and the parameter value generation time is the time when the voltage value is read.

[0039] S120: If it is determined that the difference between the parameter value generation time and the current time is less than a first preset time threshold, then the IoT data is processed to obtain first processed data, and the first processed data is stored in a first database. Additionally, second processed data is determined based on the IoT data, and the second processed data is sent to a second database.

[0040] The second processed data includes non-object parameter names.

[0041] The first preset time threshold can be set according to actual needs; it can be 5 seconds or 10 seconds. The first database can also be set according to actual needs; it can be an online database, such as Redis. If the difference between the parameter value generation time and the current time is less than the first preset time threshold, the IoT data is considered current data. This IoT data can be processed to obtain first processed data, such as performing reliability or consistency verification on object parameter values ​​to obtain new object parameter values, which are then used as the first processed data and stored in the first database. Furthermore, when the difference between the parameter value generation time and the current time is less than the first preset time threshold, this solution can also determine other derived data based on the IoT data, use this derived data as second processed data, and store it in a second database. The second processed data can be non-underlying device data. The second database can be an offline database, such as a hard disk database. The data read speed of the second database is much lower than that of the first database, and the data storage capacity of the second database is much larger than that of the first database.

[0042] S130: If it is determined that the difference between the parameter value generation time and the current time is greater than the second preset time threshold, the IoT data is processed to obtain third processed data, and the third processed data is stored in the second database.

[0043] Among these conditions, the data reading speed of the second database is lower than that of the first database; the data storage capacity of the second database is greater than that of the first database; and the second preset time threshold is greater than the first preset time threshold.

[0044] The second preset time threshold can be set according to actual needs; it can be 30 seconds or 1 minute. The second preset time threshold should be greater than the first preset time threshold. If the difference between the parameter value generation time and the current time is greater than the second preset time threshold, it indicates that the IoT data is historical data. This data can then be processed to obtain third-processed data. For example, by analyzing multiple object parameter values ​​based on the object parameter name, missing historical data can be obtained. This missing historical data, along with the original object parameter values, can be used as the third-processed data, which is then stored in the second database.

[0045] S140: In response to a data acquisition request from a target application, determine target IoT data that matches the data acquisition request from the first database or the second database.

[0046] This solution can respond to a data acquisition request from a target application and determine the data that matches the data acquisition request from a first database or a second database, i.e., the target IoT data, based on the data type required in the data acquisition request.

[0047] The technical solution of this invention provides real-time acquisition of IoT data. The IoT data includes object parameter names, object parameter values, and parameter value generation times. If the difference between the parameter value generation time and the current time is less than a first preset time threshold, the IoT data is processed to obtain first processed data, which is stored in a first database. Additionally, second processed data is determined based on the IoT data and sent to a second database. The second processed data includes non-object parameter names. If the difference between the parameter value generation time and the current time is greater than a second preset time threshold, the IoT data is processed to obtain third processed data, which is stored in the second database. The data reading speed of the second database is lower than that of the first database. The data storage capacity of the second database is greater than that of the first database. The second preset time threshold is greater than the first preset time threshold. In response to a data acquisition request from a target application, target IoT data matching the data acquisition request is determined from either the first or second database. By implementing the technical solution provided by this invention, applications requiring real-time IoT data and applications requiring big data analytics on IoT data can be simultaneously implemented.

[0048] Figure 2 This is a flowchart of an IoT data management method provided in an embodiment of the present invention. This embodiment is an optimization based on the above embodiments. Figure 2 As shown, the IoT data management method in this embodiment of the invention may include:

[0049] S210: Real-time acquisition of IoT data.

[0050] In this embodiment, optionally, after acquiring IoT data in real time, the method further includes: if it is determined that the IoT data is abnormal, then filtering the IoT data.

[0051] This solution categorizes IoT data into six types based on business scenarios and data physical characteristics: transient quantities, transient setpoints, switching quantities, switching setpoints, enumerated quantities, and enumerated setpoints. These data types may have a collection cycle on the order of seconds. However, in reality, these data values ​​typically remain unchanged or change slowly most of the time, only exhibiting rapid changes during specific periods, such as power-on / off or operational anomalies. This solution needs to accurately capture the timing and process of these changes while avoiding the storage and computational pressure caused by large amounts of unchanging data during stable operation. Therefore, after acquiring IoT data in real time, this solution filters out abnormal IoT data, discarding the anomalous data.

[0052] Therefore, by filtering IoT data if anomalies are identified, invalid data can be removed, improving data management efficiency.

[0053] In this embodiment, optionally, determining that the IoT data is abnormal includes: if it is determined that the parameter value was generated earlier than the current time, or if it is determined that the object parameter value is not within a preset range, then the IoT data is determined to be abnormal.

[0054] For example, this solution considers IoT data of switch / enumerated value type as abnormal if the feedback data is not 0 or 1, or is not an enumerated integer such as 1, 2, 3, etc., and does not meet the structural requirements of the data dictionary for this type of value. For IoT data of transient value type, it considers IoT data as abnormal if the feedback data exceeds the preset maximum and minimum value range for that data point. For example, the reasonable range for temperature values ​​is set to -50 to 150℃; if it is outside this range, the data is considered abnormal. This allows for the removal of invalid data, improving data management efficiency.

[0055] In addition, if this scheme determines that the generation time of the parameter value of the IoT data exceeds the current time by a time constant, then the IoT data is considered abnormal.

[0056] Furthermore, for a certain variable X, the time series corresponding to the acquisition points is t1, t2, ..., tn, tn+1, and the numerical sequence of X is X(1), X(2), ..., X(n), X(n+1). Tc is the acquisition period, and T0 is the time constant. If X is a transient quantity or a transient setpoint, its acquisition accuracy is ux.

[0057] The filtering logic for IoT data of the switch quantity, switch set value, enumeration quantity, and enumeration set value types in this solution can be as follows: Let the downsampling period be T1 = w × T0, where w is the downsampling coefficient with a value range of 0 to 1, and w is preferably 0.1. If Tc is less than T1, the current collected value is X(n), and the collection time point is tn. The next (latest) collected value is X(n + 1), and the collection time point is tn + 1. If X(n + 1) is not equal to X(n), then output X(n + 1). Otherwise, if X(n + 1) is equal to X(n) and tn + 1 - tn is greater than or equal to T1, then output X(n + 1). If tn + 1 - tn is less than T1, do not output X(n + 1), that is, downsample and filter this data. Otherwise, if Tc is greater than or equal to T1, no downsampling calculation is performed.

[0058] The filtering logic for IoT data of the transient quantity / transient set value type in this solution can be as follows: Let the downsampling period be T1 = w × T0, where w is the downsampling coefficient with a value range of 0 to 1, and w is preferably 0.1. If Tc is less than T1, the current collected value is X(n), and the collection time point is tn. The next (latest) collected value is X(n + 1), and the collection time point is tn + 1. If |X(n + 1) - X(n)| ≥ ux, then output X(n + 1). Otherwise, if |X(n + 1) - X(n)| < ux and tn + 1 - tn is greater than or equal to T1, then output X(n + 1). Otherwise, if tn + 1 - tn is less than T1, do not output X(n + 1), that is, downsample and filter this data. Otherwise, if Tc is greater than or equal to T1, no downsampling calculation is performed.

[0059] S220: If it is determined that the difference between the parameter value generation time and the current time is less than the first preset time threshold, then determine at least one source data associated with the object parameter name.

[0060] Exemplarily, assume that the object parameter value of a certain object parameter name is jointly determined by the numerical representations of multiple parameters, following the principle of "the minority obeys the majority". In this solution, taking the switch state of the cold machine as an example of the object parameter name, the switch state of the cold machine can be determined by three source data: the cold machine switch state, the running current, and the cold machine load rate.

[0061] S230: Determine the candidate parameter value of the object parameter name according to the source data.

[0062] First, this solution can determine the candidate parameter value of the object parameter name according to the source data. For example, this solution can calculate the rated current Ir based on the rated power. If the running current I is greater than 0.1 × Ir, then it is determined that the device is turned on, otherwise the device is turned off. Then, judge according to the cold machine load rate: If the load rate PLR is greater than 0.1, then it is determined that the device is turned on, otherwise the device is turned off.

[0063] S240: Determine the proportion of each candidate parameter value in the total amount of source data.

[0064] S250: Update the object parameter value of the object parameter name according to the candidate parameter value with the largest proportion to obtain the first processing data, and store the first processing data in the first database; and determine the second processing data according to the IoT data, and send the second processing data to the second database.

[0065] In this scheme, if the chiller on / off states indicated by signal sources 1, 2, and 3 are consistent, the chiller on / off state is output. If the chiller on / off states indicated by the three signal sources are inconsistent, the chiller on / off state, i.e., the first processed data, is output according to the candidate parameter value with the largest proportion. The original chiller on / off state is then updated based on the first processed data, and the first processed data is stored in the first database.

[0066] In addition, this solution can also refer to the values ​​of multiple parameters of the object parameter name. Different reference sources have their own confidence levels, and the numerical results of the data source with higher confidence levels are given priority as the object parameter values ​​of the object parameter name.

[0067] If the object parameter name is "single-source data," this solution can update the object parameter value based on the real-time acquisition results. Alternatively, this solution can use a moving average algorithm, updating the moving average as data becomes available, and using the moving average to represent the object parameter value. Examples include the on / off status and fault status of access control systems; and the pressure value of air conditioning terminal pipes.

[0068] In another feasible implementation, optionally, determining the second processing data based on the IoT data includes: determining a non-object parameter name associated with the object parameter name; determining a target parameter value for the non-object parameter name based on the association between the object parameter name and the non-object parameter name and the object parameter value; and using the non-object parameter name, the parameter value generation time, and the target parameter value as the second processing data.

[0069] For example, this solution can determine the non-object parameter name associated with the object parameter name of IoT data, and determine the target parameter value of the non-object parameter name based on the association between the object parameter name and the non-object parameter name and the object parameter value; the non-object parameter name, the parameter value generation time, and the target parameter value are used as second processing data, and the second processing data is sent to a second database. The IoT data associated with the object parameter name can be directly collected data, and the IoT data associated with the non-object parameter name can be derived data, i.e., the second processing data.

[0070] Data that can be directly correlated with a single data collection point in the physical world, without requiring data from other collection points for calculation, is called directly collected data. For example, if a temperature and humidity sensor m is installed in property zone a, connected to the system, and uploads its temperature measurement value, then the temperature measurement value—the operating parameter of object m in the physical world—is directly collected data. This solution can update the directly collected data using nearest neighbor interpolation or linear interpolation.

[0071] Data that cannot be directly correlated with a single data collection point in the physical world, and requires the inclusion of other collected data from the object itself or other objects in the calculation, is called derived data. For example, the operating efficiency of a host can be determined using data such as host power, host traffic, and host stress. Host power, host traffic, and host stress are directly collected data, while operating efficiency is derived data. Assuming the object parameters are host voltage, host power, and host traffic, this solution can determine the relationship between host voltage, host power, and host traffic and host operating efficiency. Then, the values ​​of each object parameter can be substituted into this relationship to determine the host operating efficiency, which is the second processed data and a non-object parameter. This solution can determine the second processed data based on the object parameter values ​​and send the second processed data to a second database. This allows for the storage of real-time data in an offline database.

[0072] S260: If it is determined that the difference between the parameter value generation time and the current time is greater than the second preset time threshold, the IoT data is processed to obtain third processed data, and the third processed data is stored in the second database.

[0073] In one feasible implementation, optionally, the IoT data is processed to obtain third processed data, including: determining the object parameter name's object parameter value sequence within a preset historical time period; determining the data interruption time point of the object parameter name within the preset historical time period based on the time series associated with the object parameter value sequence; processing the object parameter value sequence based on a target algorithm to obtain missing data corresponding to the data interruption time point; the target algorithm includes linear interpolation or clustering algorithm; and using the missing data and the object parameter value sequence as the third processed data.

[0074] For example, the preset historical time period can be set according to actual needs, such as 1 hour or 2 hours. This solution can statistically analyze the sequence of object parameter values ​​for a certain object parameter name within the preset historical time period every preset time (e.g., 6 hours) to determine the time of data interruption. Then, a target algorithm is used to process the object parameter value sequence within the preset historical time period through simulation calculation to complete automatic data completion, and the data supplemented through simulation calculation is labeled accordingly. The object parameter name, missing data, object parameter value sequence, and parameter value generation time are used as third-party processed data. The target algorithm can be set according to actual needs; for example, the target algorithm can be a linear interpolation method. The target algorithm can also be a clustering algorithm, forming a feature curve based on the trend of historical data clustering, and determining the complement based on the feature curve. This can achieve the supplementation of missing data and ensure data integrity.

[0075] In addition, this solution can also realize the function of resuming interrupted transmission, thus avoiding data loss caused by transmission interruption.

[0076] S270: In response to a data acquisition request from a target application, determine target IoT data that matches the data acquisition request from the first database or the second database.

[0077] The technical solution of this invention involves real-time acquisition of IoT data; if the difference between the parameter value generation time and the current time is less than a first preset time threshold, at least one source data associated with the object parameter name is determined; candidate parameter values ​​for the object parameter name are determined based on the source data; the proportion of each candidate parameter value in the total source data is determined; the object parameter value of the object parameter name is updated based on the candidate parameter value with the largest proportion to obtain first processed data, and the first processed data is stored in a first database; and, second processed data is determined based on the IoT data and sent to a second database; if the difference between the parameter value generation time and the current time is greater than a second preset time threshold, the IoT data is processed to obtain third processed data, and the third processed data is stored in a second database. In response to a data acquisition request from a target application, target IoT data matching the data acquisition request is determined from the first database or the second database. By implementing the technical solution provided by this invention, applications with real-time requirements for IoT data and applications with big data analysis requirements for IoT data can be simultaneously realized.

[0078] Figure 3 This is a schematic diagram of the structure of the Internet of Things (IoT) data management device provided in an embodiment of the present invention. Figure 3 As shown, the device includes:

[0079] The IoT data determination module 310 is used to acquire IoT data in real time; the IoT data includes object parameter name, object parameter value, and parameter value generation time.

[0080] The first processing data and second processing data determination module 320 is configured to process the IoT data to obtain first processing data if the difference between the parameter value generation time and the current time is less than a first preset time threshold, and store the first processing data in a first database; and to determine second processing data based on the IoT data and send the second processing data to a second database; the second processing data includes a non-object parameter name;

[0081] The third processing data determination module 330 is used to process the IoT data to obtain third processing data if the difference between the parameter value generation time and the current time is greater than a second preset time threshold, and store the third processing data in the second database; the data reading speed of the second database is lower than the data reading speed of the first database; the data storage capacity of the second database is greater than the data storage capacity of the first database; the second preset time threshold is greater than the first preset time threshold.

[0082] The target data determination module 340 is used to determine target IoT data that matches the data acquisition request from the first database or the second database in response to a data acquisition request from a target application.

[0083] Optionally, the device further includes a filtering module, which filters the IoT data if it is determined that the IoT data is abnormal after real-time acquisition of IoT data.

[0084] Optionally, the filtering module is specifically used to determine that the IoT data is abnormal if it is determined that the parameter value was generated earlier than the current time, or if it is determined that the object parameter value is not within a preset range.

[0085] Optionally, the first processing data determination module 320 includes a source data determination unit for determining at least one source data associated with the object parameter name; a candidate parameter value determination unit for determining candidate parameter values ​​for the object parameter name based on the source data; a proportion determination unit for determining the proportion of each candidate parameter value in the total amount of source data; and a first processing data determination unit for updating the object parameter value of the object parameter name based on the candidate parameter value with the largest proportion to obtain first processing data.

[0086] Optionally, the first processing data and second processing data determination module 320 is specifically used to determine the non-object parameter name associated with the object parameter name; determine the target parameter value of the non-object parameter name based on the association between the object parameter name and the non-object parameter name and the object parameter value; and use the non-object parameter name, the parameter value generation time, and the target parameter value as the second processing data.

[0087] Optionally, the third processing data determination module 330 includes a parameter value sequence determination unit, used to determine the object parameter name's object parameter value sequence within a preset historical time period; a data interruption time point determination unit, used to determine the data interruption time point of the object parameter name within the preset historical time period based on the time series associated with the object parameter value sequence; a missing data determination unit, used to process the object parameter value sequence based on a target algorithm to obtain the missing data corresponding to the data interruption time point; the target algorithm includes linear interpolation or clustering algorithm; and a third processing data determination unit, used to use the missing data and the object parameter value sequence as third processing data.

[0088] The IoT data management device provided in this embodiment of the invention can execute the IoT data management method provided in any embodiment of the invention, and has the corresponding functional modules and beneficial effects of the method execution.

[0089] Figure 4 A schematic diagram of an electronic device 40 that can be used to implement embodiments 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 processors, cellular phones, smartphones, wearable devices (e.g., helmets, glasses, watches, etc.), and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely illustrative and are not intended to limit the implementation of the invention described and / or claimed herein.

[0090] like Figure 4As shown, the electronic device 40 includes at least one processor 41 and a memory, such as a read-only memory (ROM) 42 or a random access memory (RAM) 43, communicatively connected to the at least one processor 41. The memory stores computer programs executable by the at least one processor. The processor 41 can perform various appropriate actions and processes based on the computer program stored in the ROM 42 or loaded into the RAM 43 from storage unit 48. The RAM 43 may also store various programs and data required for the operation of the electronic device 40. The processor 41, ROM 42, and RAM 43 are interconnected via a bus 44. An input / output (I / O) interface 45 is also connected to the bus 44.

[0091] Multiple components in electronic device 40 are connected to I / O interface 45, including: input unit 46, such as keyboard, mouse, etc.; output unit 47, such as various types of monitors, speakers, etc.; storage unit 48, such as disk, optical disk, etc.; and communication unit 49, such as network card, modem, wireless transceiver, etc. Communication unit 49 allows electronic device 40 to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks.

[0092] Processor 41 can be a variety of general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of processor 41 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special-purpose artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, a digital signal processor (DSP), and any suitable processor, controller, microcontroller, etc. Processor 41 performs the various methods and processes described above, such as Internet of Things (IoT) data management methods.

[0093] In some embodiments, the IoT data management method may be implemented as a computer program tangibly contained in a computer-readable storage medium, such as storage unit 48. In some embodiments, part or all of the computer program may be loaded into and / or installed on electronic device 40 via ROM 42 and / or communication unit 49. When the computer program is loaded into RAM 43 and executed by processor 41, one or more steps of the IoT data management method described above may be performed. Alternatively, in other embodiments, processor 41 may be configured to perform the IoT data management method by any other suitable means (e.g., by means of firmware).

[0094] Various embodiments 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-a-chip (SoCs), payload-programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments may include implementations in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which may be a dedicated or general-purpose programmable processor, capable of receiving data and instructions from a storage system, at least one input device, and at least one output device, and transmitting data and instructions to the storage system, the at least one input device, and the at least one output device.

[0095] Computer programs used to implement 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, such that when executed by the processor, the computer programs cause the functions / operations specified in the flowcharts and / or block diagrams to be performed. The computer programs may be executed entirely on a machine, partially on a machine, or as a standalone software package, partially on a machine and partially on a remote machine, or entirely on a remote machine or server.

[0096] In the context of this invention, a computer-readable storage medium can be a tangible medium that may contain or store a computer program for use by or in conjunction with an instruction execution system, apparatus, or device. A computer-readable storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination thereof. Alternatively, a computer-readable storage medium may be a machine-readable signal medium. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof.

[0097] To provide interaction with an object, the systems and techniques described herein can 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 object; and a keyboard and pointing device (e.g., a mouse or trackball) through which the object provides input to the electronic device. Other types of devices can also be used to provide interaction with the object; for example, feedback provided to the object can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the object can be received in any form (including sound input, voice input, or tactile input).

[0098] The systems and technologies described herein can be implemented in computing systems that include backend components (e.g., as data servers), or computing systems that include middleware components (e.g., application servers), or computing systems that include frontend components (e.g., object computers with a graphical object interface or web browser through which objects can interact with implementations of the systems and technologies described herein), or any combination of such backend, middleware, or frontend components. The components of the system can be interconnected via digital data communication of any form or medium (e.g., communication networks). Examples of communication networks include local area networks (LANs), wide area networks (WANs), blockchain networks, and the Internet.

[0099] A computing system can include clients and servers. Clients and servers are generally located far apart and typically interact through communication networks. The client-server relationship is created by computer programs running on the respective computers and having a client-server relationship with each other. The server can be a cloud server, also known as a cloud computing server or cloud host, which is a hosting product within the cloud computing service system to address the shortcomings of traditional physical hosts and VPS services, such as high management difficulty and weak business scalability.

[0100] It should be understood that the various forms of processes shown above can be used, with steps reordered, added, or deleted. For example, the steps described in this invention can be executed in parallel, sequentially, or in different orders, as long as the desired result of the technical solution of this invention can be achieved, and this is not limited herein.

[0101] The specific embodiments described above do not constitute a limitation on the scope of protection of this invention. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this invention should be included within the scope of protection of this invention.

Claims

1. An Internet of Things (IoT) data management method, characterized in that, include: Real-time acquisition of IoT data; the IoT data includes object parameter name, object parameter value, and parameter value generation time; If the difference between the parameter value generation time and the current time is determined to be less than a first preset time threshold, then the IoT data is processed to obtain first processed data, and the first processed data is stored in a first database. Additionally, second processed data is determined based on the IoT data, and the second processed data is sent to a second database. The second processed data includes non-object parameter names. If the difference between the parameter value generation time and the current time is determined to be greater than a second preset time threshold, the IoT data is processed to obtain third processed data, and the third processed data is stored in the second database; the data reading speed of the second database is lower than the data reading speed of the first database; The data storage capacity of the second database is greater than that of the first database; The second preset time threshold is greater than the first preset time threshold; In response to a data acquisition request from a target application, target IoT data matching the data acquisition request is determined from the first database or the second database; The first processed data is obtained by processing the IoT data, including: Identify at least one source data associated with the object parameter name; Based on the source data, determine the candidate parameter values ​​for the object parameter name; Determine the proportion of each candidate parameter value in the total amount of source data; The object parameter value of the object parameter name is updated based on the candidate parameter value with the largest proportion to obtain the first processed data.

2. The method according to claim 1, characterized in that, After acquiring IoT data in real time, the method further includes: If the IoT data is determined to be abnormal, then the IoT data is filtered.

3. The method according to claim 2, characterized in that, Determining the anomaly in the IoT data includes: If it is determined that the parameter value was generated earlier than the current time, or if it is determined that the object parameter value is not within a preset range, then the IoT data is determined to be abnormal.

4. The method according to claim 1, characterized in that, The second processing data is determined based on the IoT data, including: Determine the non-object parameter name associated with the object parameter name; The target parameter value of the non-object parameter name is determined based on the association between the object parameter name and the non-object parameter name and the object parameter value. The non-object parameter name, the parameter value generation time, and the target parameter value are used as the second processing data.

5. The method according to claim 1, characterized in that, The IoT data is processed to obtain third processed data, including: Determine the sequence of object parameter values ​​for the object parameter name within a preset historical time period; The data interruption time point of the object parameter name within the preset historical time period is determined based on the time series associated with the object parameter value sequence. The object parameter value sequence is processed based on the target algorithm to obtain the missing data corresponding to the data interruption time point; the target algorithm includes linear interpolation or clustering algorithm; The missing data and the sequence of object parameter values ​​are used as the third processing data.

6. An Internet of Things (IoT) data management device, characterized in that, include: The IoT data determination module is used to acquire IoT data in real time; the IoT data includes object parameter name, object parameter value, and parameter value generation time. The first processing data and second processing data determination module is used to process the IoT data to obtain first processing data if the difference between the parameter value generation time and the current time is less than a first preset time threshold, and to store the first processing data in a first database; and to determine second processing data based on the IoT data and send the second processing data to a second database; the second processing data includes a non-object parameter name; The third processing data determination module is used to process the IoT data to obtain third processing data if the difference between the parameter value generation time and the current time is greater than a second preset time threshold, and store the third processing data in the second database; the data reading speed of the second database is lower than the data reading speed of the first database; The data storage capacity of the second database is greater than that of the first database; The second preset time threshold is greater than the first preset time threshold; The target data determination module is used to determine target IoT data that matches the data acquisition request from the first database or the second database in response to a data acquisition request from a target application. The first data processing and determination module includes: A source data determination unit is used to determine at least one source data associated with the object parameter name; A candidate parameter value determination unit is used to determine candidate parameter values ​​for the object parameter name based on the information source data; The proportion determination unit is used to determine the proportion of each candidate parameter value in the total amount of source data; The first processing data determination unit is used to update the object parameter value of the object parameter name according to the candidate parameter value with the largest proportion to obtain the first processing data.

7. An electronic object, characterized in that, The electronic object includes: At least one processor; and A memory communicatively connected to the at least one processor; wherein, The memory stores a computer program that can be executed by the at least one processor, the computer program being executed by the at least one processor to enable the at least one processor to perform the Internet of Things data management method according to any one of claims 1-5.

8. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions that cause a processor to execute the Internet of Things data management method according to any one of claims 1-5.

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