Data processing method and device, equipment and medium

Abstract

The embodiment of the invention discloses a data processing method and device, equipment and a medium. The method comprises the following steps: acquiring current power data of Internet of Things equipment; if an alarm event is triggered according to the current power data, an alarm threshold value and an alarm coefficient of the alarm threshold value, determining an alarm judgment result of the alarm event; and updating the alarm coefficient according to the alarm judgment result. According to the technical scheme provided by the invention, the alarm coefficient can be instantly updated, and the alarm accuracy is improved.

Description

Technical Field

[0001] The present invention relates to the field of the Internet of Things, and in particular, to a data processing method, apparatus, device, and medium. Background Art

[0002] The rapid development of Internet of Things technology has promoted the intelligent application of industrial monitoring systems. In the traditional Internet of Things alarm system, the static threshold mechanism is one of the core designs: the system uses a preset fixed value as the alarm trigger condition, and immediately triggers an alarm when the real-time monitoring data exceeds the threshold. However, with the increasing complexity of the industrial environment, the operating states of devices and data distributions show high dynamics, and the static threshold mechanism gradually exposes the problem of insufficient adaptability.

[0003] The static threshold mechanism has a fundamental defect: the threshold setting depends on manual experience, resulting in problems of inaccurate alarms and frequent false alarms. Summary of the Invention

[0004] The present invention provides a data processing method, apparatus, device, and medium. Through the technical solution of the present invention, the alarm coefficient can be accurately calibrated, and the accuracy rate of alarms can be improved.

[0005] In a first aspect, an embodiment of the present invention provides a data processing method, including:

[0006] Obtaining the current power data of an Internet of Things device;

[0007] If an alarm event is triggered according to the current power data, the alarm threshold, and the alarm coefficient of the alarm threshold, determining the alarm judgment result of the alarm event;

[0008] Updating the alarm coefficient according to the alarm judgment result.

[0009] In a second aspect, an embodiment of the present invention provides a data processing apparatus, including:

[0010] An obtaining module, configured to obtain the current power data of an Internet of Things device;

[0011] A judgment module, configured to determine the alarm judgment result of the alarm event if an alarm event is triggered according to the current power data, the alarm threshold, and the alarm coefficient of the alarm threshold;

[0012] An updating module, configured to update the alarm coefficient according to the alarm judgment result.

[0013] In a third aspect, an embodiment of the present invention provides an electronic device, where the electronic device includes:

[0014] At least one processor; and,

[0015] A memory communicatively connected to the at least one processor; wherein,

[0016] The memory stores a computer program executable by the at least one processor, and when the computer program is executed by the at least one processor, the at least one processor is enabled to execute the data processing method according to any one of the embodiments of the present invention.

[0017] In a fourth aspect, an embodiment of the present invention provides a computer-readable storage medium storing computer instructions for causing a processor to implement the data processing method according to any one of the embodiments of the present invention when executed.

[0018] The data processing method, apparatus, device and medium according to the embodiments of the present invention, the method includes: obtaining current power data of an Internet of Things device; if an alarm event is triggered according to the current power data, an alarm threshold and an alarm coefficient of the alarm threshold, determining an alarm judgment result of the alarm event; updating the alarm coefficient according to the alarm judgment result. Specifically, the alarm coefficient can be updated differently according to different alarm judgment results, thereby improving the accuracy of alarm coefficient update and the accuracy of abnormal power data alarm. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following will briefly introduce the drawings required for description in the embodiments. Obviously, the drawings in the following description are only some embodiments of the present invention, and those of ordinary skill in the art can also obtain other drawings without creative efforts based on these drawings.

[0020] Figure 1 It is a flowchart of a data processing method provided in Embodiment 1 of the present invention;

[0021] Figure 2 It is a flowchart of a data processing method provided in Embodiment 2 of the present invention;

[0022] Figure 3 It is a schematic structural diagram of a data processing apparatus provided in Embodiment 3 of the present invention;

[0023] Figure 4 It is a schematic structural diagram of an electronic device provided in Embodiment 4 of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0024] To enable those skilled in the art to better understand the solution of the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0025] 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 do not necessarily need to describe a specific order or sequence. It should be understood that such data can be interchanged under appropriate circumstances 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 "comprising" and "having" and any variations thereof are intended to cover non-exclusive inclusion. For example, a process, method, system, product or device comprising a series of steps or units does not necessarily have to be limited to those steps or units clearly listed, but may include other steps or units not clearly listed or inherent to these processes, methods, products or devices.

[0026] It should be noted that in the technical solution of the present disclosure, the processing of the user's personal information such as collection, storage, use, processing, transmission, provision, and disclosure complies with the provisions of relevant laws and regulations and does not violate public order and good customs.

[0027] Embodiment 1

[0028] Figure 1 FIG. 13 is a flowchart of a data processing method provided in Embodiment 1 of the present invention. This method can be specifically applicable to the situation of alarm monitoring. This method can be executed by a data processing device, which can be composed of software and / or hardware and is configured in a computer or a server.

[0029] As Figure 1 shown, it includes:

[0030] Step 110, obtaining the current power data of the Internet of Things device.

[0031] Among them, the Internet of Things device can be various power devices, and the current power data is the real-time power data of the Internet of Things device, such as voltage, current, and power.

[0032] Step 120, if an alarm event is triggered according to the current power data, the alarm threshold, and the alarm coefficient of the alarm threshold, then determining the alarm judgment result of the alarm event.

[0033] Among them, the alarm threshold can be a fixed threshold or a variable threshold, which is not limited here. The alarm coefficient of the alarm threshold is used to adjust the specific size of the alarm threshold. The alarm coefficient can be determined based on multiple parameters, such as the coefficient update range, the initial alarm coefficient, and the coefficient upper limit value, etc. By adjusting the alarm coefficient, the alarm threshold can be indirectly adjusted, thereby improving the accuracy of alarm prediction. Exemplarily, the trigger condition of the alarm event can be that the current power data is greater than the product of the alarm threshold and the alarm coefficient. Further, since the current power data is dynamic, the same power data may be a dangerous alarm event or a safe event at different times. At the same time, the alarm itself has a certain lag. Therefore, when the alarm event comes, it may be a true alarm or a false alarm. Therefore, further judgment is needed. If there is a false alarm, the alarm coefficient needs to be adjusted according to the method of the embodiment of the present invention, thereby reducing frequent false alarms and improving the accuracy of the alarm. The alarm judgment result is the true judgment result of the alarm event, such as the alarm event is a false alarm or a true alarm

[0034] Step 130, update the alarm coefficient according to the alarm judgment result.

[0035] Further, the alarm coefficient can be appropriately increased or decreased to calibrate the alarm coefficient and directly affect the alarm rule, thereby improving the accuracy of the alarm rule. Among them, the alarm rule can include the alarm threshold and the alarm coefficient.

[0036] Optionally, the method for updating the alarm coefficient further includes: if a preset reset time is reached, reset the alarm coefficient to the initial alarm coefficient.

[0037] Among them, since the power data itself has a certain periodicity, such as at the same time every day, the power data may be similar. Therefore, a preset reset time or reset period can be set, and when the preset reset time or reset period is reached, the alarm coefficient is reset to the initial alarm coefficient.

[0038] The data processing method of the embodiment of the present invention includes: obtaining the current power data of the Internet of Things device; if an alarm event is triggered according to the current power data, the alarm threshold, and the alarm coefficient of the alarm threshold, determining the alarm judgment result of the alarm event; updating the alarm coefficient according to the alarm judgment result. Specifically, the alarm coefficient can be updated differently according to different alarm judgment results. For example, for false alarms, the occurrence of false alarms can be reduced by updating the alarm coefficient. The solution of the embodiment of the present invention can improve the accuracy of the alarm coefficient and the accuracy of abnormal power data alarm.

[0039] Embodiment 2

[0040] Figure 2It is a flowchart of a data processing method provided in the second embodiment of the present invention. This method makes further limitations based on the steps of the above embodiments.

[0041] As Figure 2 shown, it includes:

[0042] Step 210: Obtain the current power data of the Internet of Things device.

[0043] Step 220: Determine whether the current power data triggers an alarm event.

[0044] Specifically, if so, execute Step 230; if not, execute Step 210 to continue to obtain the current power data in real time.

[0045] Step 230: If an alarm event is triggered according to the current power data, the alarm threshold, and the alarm coefficient of the alarm threshold, determine the historical power data of the Internet of Things device according to the device identifier of the Internet of Things device.

[0046] Among them, different devices correspond to different device identifiers, and the historical power data of the Internet of Things device can be queried from the database through the device identifier.

[0047] Step 240: Compare the historical power data and the current power data, and determine the historical power data with successful comparison as the target historical power data.

[0048] Among them, the comparison can be the comparison of the power, voltage, power value, and change trend of the historical power data and the current power data.

[0049] Furthermore, since the power data of the Internet of Things device itself has a certain periodicity, therefore, within a certain period, if the historical power data and the current power data have the same data of current, voltage, and power change trend, the historical power data can be determined as the target historical power data.

[0050] Step 250: If there is a real alarm event in the target historical power data, determine that the alarm judgment result of the current power data is a real alarm; if there is no real alarm event in the target power data, determine that the alarm judgment result of the current power data is a false alarm.

[0051] Among them, the real alarm event can be an event that is determined to be a real alarm by the staff or other means. If there is a real alarm event in the target historical power data, then the current power data with the same power, voltage, power, and change trend should also have an alarm, and the alarm judgment result is a real alarm. If there is no real alarm event in the target power data, and the current power data generates an alarm event, it means that this time may be a false alarm.

[0052] Step 260: If the alarm judgment result is a false alarm, increase the alarm coefficient.

[0053] Specifically, if the alarm judgment result is a false alarm, the upper limit of alarm triggering can be increased by increasing the alarm coefficient, so as to avoid frequent false alarms in the future, reduce the number of false alarms, and improve the accuracy of alarms.

[0054] Optionally, increasing the alarm coefficient includes:

[0055] Determine the coefficient update range, and increase the alarm coefficient according to the coefficient update range.

[0056] Among them, the coefficient update range is the adjustment range of the alarm coefficient after each false alarm, and the alarm coefficient can be adjusted stage by stage through the coefficient update range.

[0057] Optionally, determining the coefficient update range includes:

[0058] Obtain the historical power data of each of multiple preset time periods; determine the coefficient upper limit value of the alarm coefficient according to the maximum value of the power data of the historical power data; determine the initial alarm coefficient of the alarm coefficient according to the minimum value of the power data of the historical power data; determine the coefficient update range of the alarm coefficient according to the coefficient upper limit value, the initial alarm coefficient and the preset number of alarms.

[0059] Specifically, the coefficient upper limit value can be determined according to the average value of the maximum values of the power data of the historical power data, and the initial alarm coefficient of the alarm coefficient can be determined according to the average value of the minimum values of the power data of the historical power data.

[0060] Exemplarily, if the current power data > I0 * K, an alarm event is triggered, where I0 is a fixed static threshold and K is the alarm coefficient. Further, K = K0 + (K max - K0) / N, where K0 is the initial alarm coefficient, K max is the coefficient upper limit value of the alarm coefficient, and (K max - K0) / N is the adjustment range of the alarm coefficient.

[0061] Optionally, if the updated alarm coefficient is greater than the coefficient upper limit value, this update operation is abandoned.

[0062] Step 270: If the alarm judgment result is a real alarm, adjust the alarm coefficient to the initial alarm coefficient.

[0063] Specifically, if the alarm judgment result is a real alarm, the alarm coefficient can be adjusted to the initial alarm coefficient to achieve automatic reset of the alarm coefficient, saving manpower and material resources.

[0064] An embodiment of the present invention provides a data processing method. When an error alarm occurs, the alarm coefficient is automatically increased to avoid repeated false alarms, improve the accuracy of the alarm, and save manpower and material resources.

[0065] Embodiment III

[0066] Figure 3 It is a schematic structural diagram of a data processing device provided in Embodiment III of the present invention. As Figure 3 shown, the device includes:

[0067] An acquisition module 310, configured to acquire the current power data of the Internet of Things device;

[0068] A judgment module 320, configured to determine the alarm judgment result of the alarm event if an alarm event is triggered according to the current power data, the alarm threshold, and the alarm coefficient of the alarm threshold;

[0069] An update module 330, configured to update the alarm coefficient according to the alarm judgment result.

[0070] The data processing device according to the embodiment of the present invention acquires the current power data of the Internet of Things device; if an alarm event is triggered according to the current power data, the alarm threshold, and the alarm coefficient of the alarm threshold, it determines the alarm judgment result of the alarm event; and updates the alarm coefficient according to the alarm judgment result. Specifically, the alarm coefficient can be updated differently according to different alarm judgment results, thereby improving the accuracy of the alarm coefficient and the accuracy of the abnormal power data alarm.

[0071] Optionally, the current power data includes the device identifier of the Internet of Things device, and the judgment module 320:

[0072] A retrieval unit, configured to determine the historical power data of the Internet of Things device according to the device identifier of the Internet of Things device;

[0073] A comparison unit, configured to compare the historical power data and the current power data, and determine the successfully compared historical power data as the target historical power data;

[0074] A judgment unit, configured to determine that the alarm judgment result of the current power data is a real alarm if a real alarm event exists in the target historical power data; and determine that the alarm judgment result of the current power data is a false alarm if no real alarm event exists in the target power data.

[0075] Optionally, the update module 330 is specifically configured to increase the alarm coefficient if the alarm judgment result is a false alarm; and adjust the alarm coefficient to the initial alarm coefficient if the alarm judgment result is a real alarm.

[0076] Optionally, the update module 330 includes:

[0077] A coefficient update amplitude determination unit for increasing the alarm coefficient according to the coefficient update amplitude.

[0078] Optionally, the coefficient update amplitude determination unit includes:

[0079] An acquisition subunit for acquiring historical power data for each of a plurality of preset time periods;

[0080] A coefficient upper limit value determination subunit for determining a coefficient upper limit value of the alarm coefficient according to a maximum value of the power data of the historical power data;

[0081] An initial alarm coefficient determination subunit for determining an initial alarm coefficient of the alarm coefficient according to a minimum value of the power data of the historical power data;

[0082] Determine the coefficient update amplitude of the alarm coefficient according to the coefficient upper limit value, the initial alarm coefficient, and a preset number of alarm times.

[0083] Optionally, the update module 330 is further configured to: if the updated alarm coefficient is greater than the coefficient upper limit value, abandon the current update operation.

[0084] Optionally, the data processing device further includes a reset module for resetting the alarm coefficient to the initial alarm coefficient if a preset reset time is reached.

[0085] The data processing device provided in the embodiments of the present invention can execute the data processing method provided in any embodiment of the present invention, and has corresponding function modules and beneficial effects for executing the method.

[0086] Embodiment 4

[0087] Figure 4 FIG. shows a schematic structural diagram of an electronic device 10 that can be used to implement embodiments of the present invention. The electronic device is intended to represent various forms of digital computers, such as, for example, a laptop computer, a desktop computer, a workbench, a personal digital assistant, a server, a blade server, a mainframe computer, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as, for example, a personal digital processor, a cellular phone, a smart phone, a wearable device (such as a helmet, glasses, a watch, 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 present invention described herein and / or claimed.

[0088] As Figure 4As shown, the electronic device 10 includes at least one processor 11 and a memory communicatively connected to the at least one processor 11, such as read-only memory (ROM) 12, random access memory (RAM) 13, etc. Among them, the memory stores a computer program executable by the at least one processor. The processor 11 can execute various appropriate actions and processes according to the computer program stored in the read-only memory (ROM) 12 or the computer program loaded from the storage unit 18 into the random access memory (RAM) 13. In the RAM 13, various programs and data required for the operation of the electronic device 10 can also be stored. The processor 11, ROM 12, and RAM 13 are connected to each other through a bus 14. The input / output (I / O) interface 15 is also connected to the bus 14.

[0089] Multiple components in the electronic device 10 are connected to the I / O interface 15, including: an input unit 16, such as a keyboard, mouse, etc.; an output unit 17, such as various types of displays, speakers, etc.; a storage unit 18, such as a disk, optical disc, etc.; and a communication unit 19, such as a network card, modem, wireless communication transceiver, etc. The communication unit 19 allows the electronic device 10 to exchange information / data with other devices through a computer network such as the Internet and / or various telecommunication networks.

[0090] The processor 11 can be various general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of the processor 11 include but are not limited to a central processing unit (CPU), a graphics processing unit (GPU), various dedicated artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, a digital signal processor (DSP), and any appropriate processor, controller, microcontroller, etc. The processor 11 executes the various methods and processes described above, such as the data processing method.

[0091] In some embodiments, the data processing method can be implemented as a computer program, which is tangibly contained in a computer-readable storage medium, such as the storage unit 18. In some embodiments, part or all of the computer program can be loaded and / or installed onto the electronic device 10 via the ROM 12 and / or the communication unit 19. When the computer program is loaded into the RAM 13 and executed by the processor 11, one or more steps of the data processing method described above can be executed. Alternatively, in other embodiments, the processor 11 can be configured to execute the data processing method in any other appropriate way (e.g., by means of firmware).

[0092] The various embodiments of the systems and techniques described above in this specification can be implemented in digital electronic circuitry, integrated circuit systems, field programmable gate arrays (FPGA), application specific integrated circuits (ASIC), application specific standard products (ASSP), systems on chip (SOC), complex programmable logic devices (CPLD), computer hardware, firmware, software, and / or combinations thereof. These various embodiments can include: being implemented in one or more computer programs that are executable and / or interpretable on a programmable system including at least one programmable processor, which can be a special-purpose or general-purpose programmable processor that receives data and instructions from, and transmits data and instructions to, a storage system, at least one input device, and at least one output device.

[0093] The computer program for implementing the methods of the present invention can be written in any combination of one or more programming languages. These computer programs can be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus, such that the computer programs, when executed by the processor, cause the functions / operations specified in the flowchart and / or block diagram to be implemented. The computer program can be executed entirely on the machine, partly on the machine, as a stand-alone software package partly on the machine and partly on a remote machine or entirely on the remote machine or server.

[0094] In the context of the present invention, a computer-readable storage medium can be a tangible medium that can contain or store a computer program for use by or in connection with an instruction execution system, apparatus, or device. The computer-readable storage medium can include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination of the foregoing. Alternatively, the computer-readable storage medium can be a machine-readable signal medium. More specific examples of the machine-readable storage medium would include an electrical connection based on one or more wires, a portable computer diskette, 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 disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0095] To provide interaction with a user, 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 user; and a keyboard and a pointing device (e.g., a mouse or a trackball) by which the user can provide input to the electronic device. Other kinds of devices can also be used to provide interaction with the user; for example, the feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including acoustic input, speech input, or tactile input).

[0096] The systems and techniques described herein can be implemented in a computing system including backend components (e.g., as a data server), or a computing system including middleware components (e.g., an application server), or a computing system including frontend components (e.g., a user computer having a graphical user interface or a web browser through which the user can interact with an implementation of the systems and techniques described herein), or a computing system including any combination of such backend components, middleware components, or frontend components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: local area network (LAN), wide area network (WAN), blockchain network, and the Internet.

[0097] A computing system can include a client and a server. The client and the server are generally far from each other and typically interact through a communication network. 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 a cloud host, which is a host product in the cloud computing service system, and solves the defects of difficult management and weak business scalability existing in traditional physical hosts and VPS services.

[0098] It should be understood that various forms of the processes shown above can be used, with steps reordered, added, or deleted. For example, the steps recited in the present invention can be executed in parallel, sequentially, or in a different order, as long as the desired results of the technical solution of the present invention can be achieved, and this is not limited herein.

[0099] The above specific embodiments do not constitute a limitation on the protection scope of the present 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 principle of the present invention shall be included within the protection scope of the present invention.

Claims

1. A data processing method, characterized in that, including: Obtain the current power data of the Internet of Things device; If an alarm event is triggered according to the current power data, the alarm threshold, and the alarm coefficient of the alarm threshold, determine the alarm judgment result of the alarm event; Update the alarm coefficient according to the alarm judgment result.

2. The method according to claim 1, wherein The current power data includes the device identifier of the Internet of Things device; The determining the alarm judgment result of the alarm event includes: Determine the historical power data of the Internet of Things device according to the device identifier of the Internet of Things device; Compare the historical power data and the current power data, and determine the compared successful historical power data as the target historical power data; If there is a real alarm event in the target historical power data, determine that the alarm judgment result of the current power data is a real alarm; if there is no real alarm event in the target power data, determine that the alarm judgment result of the current power data is a false alarm.

3. The method according to claim 1, characterized in that The updating the alarm coefficient according to the alarm judgment result includes: If the alarm judgment result is a false alarm, increase the alarm coefficient; If the alarm judgment result is a real alarm, adjust the alarm coefficient to the initial alarm coefficient.

4. The method according to claim 3, wherein The increasing the alarm coefficient includes: Determine the coefficient update range, and increase the alarm coefficient according to the coefficient update range.

5. The method according to claim 4, characterized in that, The determining the coefficient update range includes: Obtain the historical power data of each of multiple preset time periods; Determine the upper limit value of the coefficient of the alarm coefficient according to the maximum value of the power data of the historical power data; Determine the initial alarm coefficient of the alarm coefficient according to the minimum value of the power data of the historical power data; Determine the coefficient update range of the alarm coefficient according to the upper limit value of the coefficient, the initial alarm coefficient, and the preset number of alarm times.

6. The method according to claim 3, characterized in that, It also includes: If the updated alarm coefficient is greater than the upper limit value of the coefficient, abandon the current update operation.

7. The method according to claim 1, wherein The method for updating the alarm coefficient further includes: If the preset reset time is reached, reset the alarm coefficient to the initial alarm coefficient.

8. A data processing device, characterized in that, including: An acquisition module for acquiring the current power data of the Internet of Things device; A judgment module for determining the alarm judgment result of the alarm event if an alarm event is triggered according to the current power data, the alarm threshold, and the alarm coefficient of the alarm threshold; An update module for updating the alarm coefficient according to the alarm judgment result.

9. An electronic device, characterized in that, The electronic device includes: 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 data processing method according to any one of claims 1-7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions for causing a processor to execute the data processing method according to any one of claims 1-7 when executed.

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