CN114238330A - Data processing method and device, electronic equipment and storage medium - Google Patents

Data processing method and device, electronic equipment and storage medium Download PDF

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CN114238330A
CN114238330A CN202111545569.2A CN202111545569A CN114238330A CN 114238330 A CN114238330 A CN 114238330A CN 202111545569 A CN202111545569 A CN 202111545569A CN 114238330 A CN114238330 A CN 114238330A
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徐超
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Beijing Softong Intelligent Technology Co ltd
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    • G06F16/245—Query processing
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    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
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    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16Y—INFORMATION AND COMMUNICATION TECHNOLOGY SPECIALLY ADAPTED FOR THE INTERNET OF THINGS [IoT]
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Abstract

The embodiment of the invention discloses a data processing method, a data processing device, electronic equipment and a storage medium, wherein the data processing method comprises the following steps: acquiring target data obtained by monitoring a target application scene by monitoring equipment of the target application scene; acquiring a candidate identification rule configured for a target application scene; identifying the target data according to the candidate identification rule to obtain a target alarm event corresponding to the target data; and generating an alarm report according to the target alarm event. The embodiment of the invention can analyze the professional data monitored by the monitoring equipment by using the configured identification rule, and convert the complex and unintelligible monitoring data information into the simple and easily understood event information, so that non-professional personnel can understand the real condition reflected by the target data, the user experience is improved, and the pressure of a management department is reduced.

Description

Data processing method and device, electronic equipment and storage medium
Technical Field
The embodiment of the invention relates to the technology of internet of things, in particular to a data processing method and device, electronic equipment and a storage medium.
Background
With the increasing development of the internet of things technology, the workload of internet of things data analysis and management application carried by related management departments is increasing, and the application scenarios of the internet of things technology are increasing, such as application scenarios of smart communities, smart parks, smart traffic, smart fire fighting, smart coal mines and the like.
The existing sensing data of the Internet of things are all composed of data collected by monitoring equipment in real time, and the data collected by the monitoring equipment has higher data concurrency and data specialty. Therefore, in practical application, non-professionals cannot know the real situation of the application scene of the internet of things. For example, in an application scene of an intelligent community, data monitored by monitoring equipment is usually a large amount of monitoring data information such as a certain residual current high peak value and a certain harmonic fundamental wave level, and non-professionals cannot understand the monitoring data information. Therefore, management personnel in the management department can not clearly reflect the sensing data of the internet of things, and pressure is brought to the management department.
Disclosure of Invention
Embodiments of the present invention provide a data processing method and apparatus, an electronic device, and a storage medium, so that a non-professional can understand a real situation reflected by internet of things perception data, and pressure of a management department is reduced.
In a first aspect, an embodiment of the present invention provides a data processing method, where the method includes:
acquiring target data obtained by monitoring a target application scene by monitoring equipment of the target application scene;
acquiring a candidate identification rule configured for the target application scene;
identifying the target data according to the candidate identification rule to obtain a target alarm event corresponding to the target data;
and generating an alarm report according to the target alarm event.
In a second aspect, an embodiment of the present invention further provides a data processing apparatus, where the apparatus includes:
a target data acquisition module: acquiring target data obtained by monitoring a target application scene by monitoring equipment of the target application scene;
a rule acquisition module: acquiring a candidate identification rule configured for the target application scene;
an alarm event acquisition module: identifying the target data according to the candidate identification rule to obtain a target alarm event corresponding to the target data;
an alarm report generation module: and generating an alarm report according to the target alarm event.
In a third aspect, an embodiment of the present invention further provides an electronic device, which includes a memory, a processor, and a computer program stored in the memory and executable on the processor, where the processor implements the data processing method according to any one of the embodiments of the present invention when executing the computer program.
In a fourth aspect, the embodiment of the present invention further provides a computer-readable storage medium, on which a computer program is stored, where the computer program, when executed by a processor, implements the data processing method according to any one of the embodiments of the present invention.
In the embodiment of the invention, target data obtained by monitoring a target application scene by monitoring equipment of the target application scene is obtained; acquiring a candidate identification rule configured for a target application scene; identifying the target data according to the candidate identification rule to obtain a target alarm event corresponding to the target data; and generating an alarm report according to the target alarm event. In other words, in the embodiment of the present invention, the target data monitored by the monitoring device is analyzed by using the recognition rule configured for the application scenario, so that the actual situation reflected by the target data can be obtained, and the alarm event possibly existing in the application scenario can be inferred. And an alarm report is further generated according to the alarm event, so that complex and difficult monitoring data information can be converted into simple and easily understood event information. Therefore, non-professionals can quickly know the real situation reflected by the target data, the user experience is improved, and meanwhile, the pressure of management departments is relieved.
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Fig. 1 is a schematic flow chart of a data processing method according to an embodiment of the present invention;
FIG. 2 is another flow chart of a data processing method according to an embodiment of the present invention;
FIG. 3 is a flow chart of generating an alert report provided by an embodiment of the present invention;
FIG. 4 is a schematic structural diagram of a data processing apparatus according to an embodiment of the present invention;
FIG. 5 is a block diagram of a data processing system according to an embodiment of the present invention;
fig. 6 is a schematic structural diagram of an electronic device according to an embodiment of the present invention.
Detailed Description
The present invention will be described in further detail with reference to the accompanying drawings and examples. It is to be understood that the specific embodiments described herein are merely illustrative of the invention and are not limiting of the invention. It should be further noted that, for the convenience of description, only some of the structures related to the present invention are shown in the drawings, not all of the structures.
Fig. 1 is a schematic flow chart of a data processing method according to an embodiment of the present invention, which is applicable to a case where sensing data information of an internet of things with a strong specialty is converted into simple and easily understood event information. In a specific embodiment, the apparatus may be integrated in an electronic device, which may be, for example, a computer, a server. The following embodiments will be described by taking as an example that the apparatus is integrated in an electronic device, and referring to fig. 1, the method may specifically include the following steps:
step 101: and acquiring target data obtained by monitoring the target application scene by the monitoring equipment of the target application scene.
Specifically, the target application scenario may be an application scenario of any internet of things monitoring technology, such as smart community and smart fire protection. The intelligent community can integrate the existing various service resources of the community through the Internet of things technology, and provide various convenient services for the community. Wisdom fire control can utilize internet of things, realizes the intellectuality of city fire control. The monitoring device can be determined according to the target application scene and the specific requirements. The target data is data measured by the monitoring device according to requirements in a target application scene. For example, the target application scenario is intelligent fire protection in a certain area, and the working condition of a circuit in the area needs to be known. In the target application scene, the sensor is used as monitoring equipment to monitor data such as residual current, residual voltage, circuit temperature, circuit harmonic waves and the like in the circuit. The specific data in the circuit measured by the sensor is the target data in the intelligent fire fighting scene.
Step 102: and acquiring a candidate identification rule configured for the target application scene.
After the target data is acquired, a candidate identification rule configured for the target application scene needs to be acquired. Wherein, different application scenes correspond to different identification rules. For example, in the application scenario of intelligent fire fighting, the configured identification rule may be that the residual current value in a certain area cannot exceed a certain value, and if the residual current value exceeds the certain value, a current leakage phenomenon may exist in the circuit in the area; the circuit temperature value cannot exceed a certain temperature value, and if the circuit temperature value is greater than the certain temperature value, the circuit in the area may be aged. Further, based on the target application scenario and the target data, the identification rule corresponding thereto is selected.
Step 103: and identifying the target data according to the candidate identification rule to obtain a target alarm event corresponding to the target data.
Specifically, after the candidate identification rule configured for the target application scene is acquired, the target data is identified based on the selected identification rule. In an example, in an application scenario of intelligent fire fighting, a sensor is a monitoring device, and target data is data such as residual current, residual voltage, circuit temperature, circuit harmonic waves and the like in a circuit in a certain area measured by the sensor. Assume that the measured target data is:
1. current value: i is1＝7.2A，I2＝7.2A，I3＝15.2A……IN＝7.2A。
2. Voltage value: u shape1＝220V，U2＝220V……UN＝220V。
3. Temperature: t is1Take 45 timesDegree of falling, T246 degrees celsius, T347 degrees celsius … … TN60 degrees celsius.
4. The harmonics fluctuate normally.
Assume that there are: (average Current value-1)<(I1To INValue of (1)>(average current + 1).
According to the identification rule and the target data, I3＝15.2A>(average current +1), no rule is met. It can be concluded from the recognition rules that I is generated3May have a leakage condition. Suppose that I is generated3Is XX way XXX family. Further, the possible occurrence of an electrical leakage condition for XX channel XXX users is defined as a target alarm event.
Step 104: and generating an alarm report according to the target alarm event.
Specifically, after the target alarm event is obtained, the target alarm event is analyzed and sorted to generate an alarm report. As illustrated in step 103, assume that the target alarm event is that there may be an electrical leakage situation for XX line XXX users. And further generating an alarm report by combining the target data as follows:
and the XX path XXX user current data is abnormal, and the current value exceeds the normal value. And deducing that the XX circuit XXX user circuit is likely to have electric leakage, and please check as soon as possible.
In this embodiment, optionally, after generating an alarm report according to the target alarm event, the alarm report is sent to the terminal. The terminal can be a mobile phone, a notebook computer, a tablet computer and other devices.
By sending the alarm report to the terminal device, the most likely business problems at present can be provided for residents, management departments and enterprises, so that business management personnel can quickly understand the business problems and further effectively solve the business problems.
According to the technical scheme of the embodiment, target data obtained by monitoring the target application scene through the monitoring equipment for obtaining the target application scene; acquiring a candidate identification rule configured for a target application scene; identifying the target data according to the candidate identification rule to obtain a target alarm event corresponding to the target data; and generating an alarm report according to the target alarm event. Complex and unintelligible monitoring data information can be converted into simple and easily understood event information. Therefore, non-professionals can quickly know the real situation reflected by the target data, the user experience is improved, and meanwhile, the pressure of management departments is relieved.
The data processing method provided by the embodiment of the present invention is further explained below, and fig. 2 is another schematic flow chart of the data processing method provided by the embodiment of the present invention. A specific method can be shown in fig. 2, and the method can include the following steps:
step 201, configuring candidate identification rules for corresponding application scenes according to the basic data and the prior data of each application scene, and configuring alarm events for each candidate identification rule.
The basic data of the application scene can be acquired according to specific requirements. In an example, the application scenario is to perform oil smoke detection for a certain community, and the basic data in the application scenario includes data such as the number of people in the community, the building area of the community, and the number of shops in the community. Among these, a priori data is a vast amount of common knowledge data in existence. For example, in an application scenario of oil smoke detection for a certain community, the prior data includes data such as a standard range of oil smoke values generated in a specified residential area, a standard range of particulate matter values existing in air, and a measurable standard range of non-methane total hydrocarbon values under normal conditions. And further, configuring candidate identification rules for each application scene according to the basic data and the prior data of the application scene. The identification rule may be used to identify whether the target data has abnormal data, and different identification rules need to be configured in different application scenarios. In an example, the application scene is to perform oil smoke detection on a certain community, and the basic data includes community people, community building area, community shop quantity and the like. The configured identification rule for the application scenario may be:
1. the oil smoke value can not exceed the specified standard range of the oil smoke value.
2. The particulate matter value may not exceed a specified standard range of particulate matter values.
3. The non-methane total hydrocarbon value may not exceed a specified standard range of non-methane total hydrocarbon values.
After the identification rule is configured for the application scene, further, an alarm event is configured for the identification rule. The alarm event can be flexibly configured according to specific requirements based on the identification rule. For example, when the measured soot, particulate, and non-methane total hydrocarbon values are as follows (subscript N indicates different regions):
oil smoke value: t is1＝50；T2＝50；T3＝180；……TN＝50。
The particulate matter value: k1＝30mg/m3；K2＝35mg/m3；K3＝55mg/m3……KN＝35mg/m3。
Non-methane total hydrocarbons: f1＝15mg/m3；F2＝15mg/m3；F3＝35mg/m3……FN＝15mg/m3。
Based on the target data, according to the identification rule, the oil smoke value, the particulate matter value and the non-methane total hydrocarbon value generated in the No. 3 area do not accord with the identification rule, and the alarm event configured for the identification rule is that the No. 3 area has a serious oil smoke exceeding event, and possibly a shop in the No. 3 area privately increases the operation of the cooking bench. Furthermore, corresponding alarm events are configured for each candidate identification rule.
Step 202, testing the identification accuracy of the candidate identification rules configured for each application scene based on the test data.
Where the test data may be a small amount, representative data. Specifically, under a target application scenario, representative test data can be selected to test the recognition accuracy of the candidate recognition rule. In an example, the oil smoke detection for a certain community is an application scene, the test data is oil smoke values generated in N areas in the community, and the test rule is that the oil smoke values cannot exceed standard oil smoke values. In the test data, the soot value in zone No. 3 exceeded the standard soot value. And testing the identification rule by using the test data, and observing whether the test result is accurate. Furthermore, multiple tests can be performed on the identification rule by using multiple different test data, and the results of the multiple tests are averaged to obtain the final accuracy of the identification rule.
And 203, adding the candidate identification rules with the identification accuracy rate exceeding the preset threshold value and the correspondingly configured alarm events to a specified database to obtain a target database.
Specifically, the accuracy of the identification rule is observed, and if the accuracy of the identification rule is low, the configuration of the identification rule may be incorrect. Such as an excessively large range of standard range settings, etc. Further, the content of the identification rule may be modified until the accuracy of the identification rule exceeds a preset threshold. The preset threshold value can be flexibly set according to specific requirements. Further, the identification rule with the accuracy rate exceeding the preset threshold value and the alarm event configured correspondingly are added to a designated database, and the database is used as a target database.
And step 204, acquiring candidate identification rules configured for the target application scene from the target database.
The target database stores candidate identification rules of each application scene and alarm events configured correspondingly. And acquiring a candidate identification rule corresponding to the target application scene based on the target application scene.
And step 205, acquiring target data obtained by monitoring the target application scene by the monitoring equipment of the target application scene.
And step 206, determining an event recognition rule matched with the target data from the candidate recognition rules to obtain a target recognition rule.
Wherein one application scenario corresponds to a plurality of candidate recognition rules. For example, in an application scenario of intelligent fire fighting, the candidate target rules include an identification rule for determining whether the circuit is aged or not, an identification rule for determining whether the circuit has an electric leakage phenomenon or not, and the like. And acquiring target data according to specific requirements, wherein the target data is a residual current value in a certain area if the requirements are that whether a circuit in the certain area is aged or not needs to be judged. Under the application scene, the event identification rule matched with the target data is an identification rule for judging whether the circuit is aged, namely, the residual current value in a certain area cannot exceed a certain value, and if the residual current value exceeds the certain value, the electric leakage phenomenon exists in the circuit in the area. Further, the identification rule is determined as a target identification rule.
And step 207, identifying the target data according to the target identification rule, and determining the alarm event configured for the target identification rule as a target alarm event.
And after the target identification rule is determined, acquiring an alarm event configured for the target identification rule in a target database, and determining the alarm event as a target alarm event.
And step 208, acquiring the identification accuracy of the target identification rule.
Specifically, the target identification rule is acquired from the target database, so that the identification accuracy of the target identification rule exceeds a preset threshold.
And step 209, setting confidence for the target alarm event in the alarm report according to the identification accuracy of the target identification rule.
The target alarm event is configured based on the target identification rule, so that the confidence of the target alarm event is greater than or equal to the accuracy of the target identification rule. The confidence level of the target alarm event may reflect the likelihood of the target alarm event occurring. Illustratively, there are rule a, rule B, rule C, and rule D in the target identification rule. When the target data satisfies any two of the rules A, B, C and D, it indicates that a target alarm event will occur, and the greater the number of rules satisfying the target rule, the greater the likelihood of indicating that a target alarm event will occur. Assuming that the accuracy of the obtained target identification rule is 80%, when the target data satisfies a and B, the confidence of the target alarm event is 85%. When the target data satisfies A, B and D, the confidence of the target alarm event is 95%. And further, setting confidence for the target alarm event according to the identification accuracy of the target identification rule.
And step 210, generating an alarm report according to the target alarm event.
And after the target alarm event is determined, generating an alarm report according to the target alarm event. Wherein, the alarm report includes the confidence of the target alarm event. For example, assume that the target alarm event is that XX XXX users may have a power leakage condition. Further generating an alarm report as:
and the XX path XXX user current data is abnormal, and the current value exceeds the normal value. The probability of leakage of XX XXX circuit is 90%, please check it as soon as possible.
Fig. 3 is a flowchart of generating an alarm report according to an embodiment of the present invention, as shown in fig. 3. The target database stores candidate identification rules and corresponding alarm events, selects the target identification rules and the target alarm events from the target database, generates an alarm report according to the target alarm events, and sends the alarm report to the terminal. By sending the alarm report to the terminal, the most probable business problems at present can be provided for residents, management departments and enterprises, so that business management personnel can quickly understand the business problems and further effectively solve the business problems.
According to the technical scheme of the embodiment, candidate identification rules are configured for corresponding application scenes according to the basic data and the prior data of each application scene, and alarm events are configured for each candidate identification rule; testing the recognition accuracy of the candidate recognition rules configured for each application scene based on the test data; adding candidate identification rules with identification accuracy rate exceeding a preset threshold value and correspondingly configured alarm events to a specified database to obtain a target database; acquiring a candidate identification rule configured for a target application scene from a target database; acquiring target data obtained by monitoring a target application scene by monitoring equipment of the target application scene; determining an event recognition rule matched with the target data from the candidate recognition rules to obtain a target recognition rule; identifying the target data according to the target identification rule, and determining the alarm event configured for the target identification rule as a target alarm event; acquiring the identification accuracy of a target identification rule; setting a confidence coefficient for the target alarm event in the alarm report according to the identification accuracy of the target identification rule; and generating an alarm report according to the target alarm event. Through the technical scheme of the embodiment, the target data can be identified, the real service problem reflected by the target data is known, and the accuracy of identifying and processing the target data is improved by setting the accuracy for the target rule. And the confidence coefficient is set for the alarm event, so that the management department and the enterprise can quickly understand the problem reflected by the target data, the problem is processed, the user experience is further improved, and the pressure of the management department is relieved.
Fig. 4 is a structural diagram of a data processing apparatus according to an embodiment of the present invention, which is suitable for executing a data processing method according to an embodiment of the present invention. As shown in fig. 4, the apparatus may specifically include:
the target data acquisition module 401: acquiring target data obtained by monitoring a target application scene by monitoring equipment of the target application scene;
the rule acquisition module 402: acquiring a candidate identification rule configured for the target application scene;
the alarm event acquisition module 403: identifying the target data according to the candidate identification rule to obtain a target alarm event corresponding to the target data;
the alert report generation module 404: and generating an alarm report according to the target alarm event.
Optionally, the alarm event obtaining module 403 specifically includes:
determining an event recognition rule matched with the target data from the candidate recognition rules to obtain a target recognition rule;
and determining the alarm event configured for the target identification rule as the target alarm event.
Optionally, the alarm event obtaining module 403 further includes:
and acquiring candidate identification rules configured for the target application scene from a target database, wherein the target database comprises the candidate identification rules configured for each application scene and alarm events configured for each candidate identification rule.
Optionally, in the alarm event obtaining module 403, the establishment manner of the target database specifically includes:
configuring candidate identification rules for corresponding application scenes according to the basic data and the prior data of each application scene, and configuring alarm events for each candidate identification rule;
and adding the candidate identification rules configured for the application scenes and the alarm events configured for the candidate identification rules to a specified database to obtain the target database.
Optionally, the alarm event obtaining module 403 further includes:
testing the recognition accuracy of the candidate recognition rules configured for the various application scenes based on test data before adding the candidate recognition rules configured for the various application scenes and the alarm events configured for the various candidate recognition rules to a specified database;
the alarm event obtaining module 403 is further configured to add the candidate identification rule with the identification accuracy rate exceeding the preset threshold and the alarm event configured correspondingly to the specified database.
Optionally, the alarm event obtaining module 403 further includes:
acquiring the identification accuracy of the target identification rule;
and setting confidence for the target alarm event in the alarm report according to the identification accuracy of the target identification rule.
Optionally, the apparatus further includes sending the alarm report to a terminal.
The data processing device provided by the embodiment of the invention can execute the data processing method provided by any embodiment of the invention, and has corresponding functional modules and beneficial effects of the execution method. Reference may be made to the description of any method embodiment of the invention not specifically described in this embodiment.
An embodiment of the present invention further provides a data processing system, and fig. 5 is a schematic structural diagram of the data processing system provided in the embodiment of the present invention. As shown in fig. 5, includes an application layer, a resolution layer, a storage layer, a service layer, and a data processing subsystem.
The application layer comprises application scenes such as intelligent fire fighting, public safety, intelligent environment protection, intelligent communities, intelligent parks, fire early warning, video analysis, energy consumption monitoring, flow analysis and pollution source monitoring. The application layer is used for determining a target application scene and sending target data, basic data of the target application scene and prior data to the analysis layer.
And the analysis layer receives the target data, the target application scene basic data and the prior data to obtain a target identification rule and a target alarm event, and generates an alarm report according to the target alarm event.
The storage layer is used for storing target data, target application scene basic data, prior data, target identification rules, target alarm events and alarm reports. Wherein, the MongoDB database can be used for reading and writing data at high speed; the MySQL database may be used to store data in a structured manner; the InfluxDB database may store data in a time sequence.
The service layer can provide various convenient services for the user, such as user unified authentication service, message/short message service, application support service, event subscription service, workflow service, operation and maintenance inspection service, data acquisition service, alarm monitoring service and the like.
The data processing subsystem comprises a basic assembly and a functional module. The basic component comprises a device management subsystem, a data analysis subsystem and a basic management. The functional modules corresponding to the basic components comprise functional modules such as equipment management, a rule engine, event simulation, menu management, product management, template setting, an event center, an application interface, data management, data statistics, report export, system monitoring and the like.
For details of other implementations of the specific data processing method, reference may be made to the description of the foregoing embodiments, which are not described herein again.
The above-described embodiments should not be construed as limiting the scope of the invention. Those skilled in the art will appreciate that various modifications, combinations, sub-combinations, and substitutions can occur, depending on design requirements and other factors. Any modification, equivalent replacement, and improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Fig. 6 is a schematic structural diagram of an electronic device according to an embodiment of the present invention, and referring to fig. 6, a schematic structural diagram of a computer system 12 suitable for implementing the electronic device according to an embodiment of the present invention is shown. The electronic device shown in fig. 6 is only an example, and should not bring any limitation to the functions and the scope of use of the embodiments of the present invention. The components of electronic device 12 may include, but are not limited to: one or more processors or processing units 16, a system memory 28, and a bus 18 that couples various system components including the system memory 28 and the processing unit 16.
Bus 18 represents one or more of any of several types of bus structures, including a memory bus or memory controller, a peripheral bus, an accelerated graphics port, and a processor or local bus using any of a variety of bus architectures. By way of example, such architectures include, but are not limited to, Industry Standard Architecture (ISA) bus, micro-channel architecture (MAC) bus, enhanced ISA bus, Video Electronics Standards Association (VESA) local bus, and Peripheral Component Interconnect (PCI) bus.
Electronic device 12 typically includes a variety of computer system readable media. Such media may be any available media that is accessible by electronic device 12 and includes both volatile and nonvolatile media, removable and non-removable media.
The system memory 28 may include computer system readable media in the form of volatile memory, such as Random Access Memory (RAM)30 and/or cache memory 32. The electronic device 12 may further include other removable/non-removable, volatile/nonvolatile computer system storage media. By way of example only, storage system 34 may be used to read from and write to non-removable, nonvolatile magnetic media (not shown in FIG. 6, and commonly referred to as a "hard drive"). Although not shown in FIG. 6, a magnetic disk drive for reading from and writing to a removable, nonvolatile magnetic disk (e.g., a "floppy disk") and an optical disk drive for reading from or writing to a removable, nonvolatile optical disk (e.g., a CD-ROM, DVD-ROM, or other optical media) may be provided. In these cases, each drive may be connected to bus 18 by one or more data media interfaces. Memory 28 may include at least one program product having a set (e.g., at least one) of program modules that are configured to carry out the functions of embodiments of the invention.
A program/utility 40 having a set (at least one) of program modules 42 may be stored, for example, in memory 28, such program modules 42 including, but not limited to, an operating system, one or more application programs, other program modules, and program data, each of which examples or some combination thereof may comprise an implementation of a network environment. Program modules 42 generally carry out the functions and/or methodologies of the described embodiments of the invention.
Electronic device 12 may also communicate with one or more external devices 14 (e.g., keyboard, pointing device, display 24, etc.), with one or more devices that enable a user to interact with electronic device 12, and/or with any devices (e.g., network card, modem, etc.) that enable electronic device 12 to communicate with one or more other computing devices. Such communication may be through an input/output (I/O) interface 22. In the electronic device 12 of the present embodiment, the display 24 is not provided as a separate body but is embedded in the mirror surface, and when the display surface of the display 24 is not displayed, the display surface of the display 24 and the mirror surface are visually integrated. Also, the electronic device 12 may communicate with one or more networks (e.g., a Local Area Network (LAN), a Wide Area Network (WAN), and/or a public network, such as the Internet) via the network adapter 20. As shown, the network adapter 20 communicates with other modules of the electronic device 12 via the bus 18. It should be understood that although not shown in the figures, other hardware and/or software modules may be used in conjunction with electronic device 12, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage systems, among others.
The processing unit 16 executes various functional applications and data processing by executing programs stored in the system memory 28, for example, to implement a data processing method provided by an embodiment of the present invention: acquiring target data obtained by monitoring a target application scene by monitoring equipment of the target application scene; acquiring a candidate identification rule configured for the target application scene; identifying the target data according to the candidate identification rule to obtain a target alarm event corresponding to the target data; and generating an alarm report according to the target alarm event.
Embodiments of the present invention provide a computer-readable storage medium, on which a computer program is stored, where the computer program, when executed by a processor, implements a data processing method as provided in all embodiments of the present invention: acquiring target data obtained by monitoring a target application scene by monitoring equipment of the target application scene; acquiring a candidate identification rule configured for the target application scene; identifying the target data according to the candidate identification rule to obtain a target alarm event corresponding to the target data; and generating an alarm report according to the target alarm event. Any combination of one or more computer-readable media may be employed. The computer readable medium may be a computer readable signal medium or a computer readable storage medium. A computer readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the foregoing. More specific examples (a non-exhaustive list) of the computer readable storage medium would include the following: an electrical connection having 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. In the context of this document, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device.
A computer readable signal medium may include a propagated data signal with computer readable program code embodied therein, for example, in baseband or as part of a carrier wave. Such a propagated data signal may take many forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. A computer readable signal medium may also be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device.
Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to wireless, wireline, optical fiber cable, RF, etc., or any suitable combination of the foregoing.
Computer program code for carrying out operations for aspects of the present invention may be written in any combination of one or more programming languages, including an object oriented programming language such as Java, Smalltalk, C + +, or the like, as well as conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the case of a remote computer, the remote computer may 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 the connection may be made to an external computer (for example, through the Internet using an Internet service provider).
It is to be noted that the foregoing is only illustrative of the preferred embodiments of the present invention and the technical principles employed. It will be understood by those skilled in the art that the present invention is not limited to the particular embodiments illustrated herein, but is capable of various obvious changes, rearrangements and substitutions as will now become apparent to those skilled in the art without departing from the scope of the invention. Therefore, although the present invention has been described in greater detail by the above embodiments, the present invention is not limited to the above embodiments, and may include other equivalent embodiments without departing from the spirit of the present invention, and the scope of the present invention is determined by the scope of the appended claims.

Claims (10)

1. A data processing method, comprising:
acquiring target data obtained by monitoring a target application scene by monitoring equipment of the target application scene;
acquiring a candidate identification rule configured for the target application scene;
identifying the target data according to the candidate identification rule to obtain a target alarm event corresponding to the target data;
and generating an alarm report according to the target alarm event.
2. The method according to claim 1, wherein the candidate recognition rules include a plurality of candidate recognition rules, and the recognizing the target data according to the candidate recognition rules to obtain the target alarm event corresponding to the target data includes:
determining an event recognition rule matched with the target data from the candidate recognition rules to obtain a target recognition rule;
and determining the alarm event configured for the target identification rule as the target alarm event.
3. The method of claim 2, wherein the obtaining the candidate recognition rule configured for the target application scenario comprises:
and acquiring candidate identification rules configured for the target application scene from a target database, wherein the target database comprises the candidate identification rules configured for each application scene and alarm events configured for each candidate identification rule.
4. The method of claim 3, wherein the target database is established by:
configuring candidate identification rules for corresponding application scenes according to the basic data and the prior data of each application scene, and configuring alarm events for each candidate identification rule;
and adding the candidate identification rules configured for the application scenes and the alarm events configured for the candidate identification rules to a specified database to obtain the target database.
5. The method of claim 4, further comprising, before adding the candidate recognition rules configured for the respective application scenarios and the alarm events configured for the respective candidate recognition rules to a specified database:
testing the identification accuracy of the candidate identification rules configured for each application scene based on the test data;
and adding the candidate identification rules with the identification accuracy rate exceeding a preset threshold value and the alarm events configured correspondingly to the specified database.
6. The method of claim 5, wherein generating an alert report based on the target event comprises:
acquiring the identification accuracy of the target identification rule;
and setting confidence for the target alarm event in the alarm report according to the identification accuracy of the target identification rule.
7. The method of any of claims 1 to 6, further comprising:
and sending the alarm report to a terminal.
8. A data processing apparatus, comprising:
a target data acquisition module: acquiring target data obtained by monitoring a target application scene by monitoring equipment of the target application scene;
a rule acquisition module: acquiring a candidate identification rule configured for the target application scene;
an alarm event acquisition module: identifying the target data according to the candidate identification rule to obtain a target alarm event corresponding to the target data;
an alarm report generation module: and generating an alarm report according to the target alarm event.
9. An electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, characterized in that the processor implements the data processing method according to any of claims 1 to 7 when executing the program.
10. A computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, carries out the data processing method of any one of claims 1 to 7.
CN202111545569.2A 2021-12-16 2021-12-16 Data processing method and device, electronic equipment and storage medium Pending CN114238330A (en)

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Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN115271424A (en) * 2022-05-20 2022-11-01 中国电子产品可靠性与环境试验研究所（（工业和信息化部电子第五研究所）（中国赛宝实验室）） Method and device for identifying product use danger based on scene and computer equipment
CN115883779A (en) * 2022-10-13 2023-03-31 湖北公众信息产业有限责任公司 Smart park information safety management system based on big data

Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN115271424A (en) * 2022-05-20 2022-11-01 中国电子产品可靠性与环境试验研究所（（工业和信息化部电子第五研究所）（中国赛宝实验室）） Method and device for identifying product use danger based on scene and computer equipment
CN115883779A (en) * 2022-10-13 2023-03-31 湖北公众信息产业有限责任公司 Smart park information safety management system based on big data

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