CN116071864A - Early warning processing method, device, equipment, storage medium and computer program product - Google Patents
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Abstract
The application relates to an early warning processing method, an early warning processing device, a storage medium and a computer program product. The method comprises the following steps: acquiring current inspection data corresponding to a target inspection point on an inspection route; under the condition that the need of generating a patrol report is determined according to the current patrol data, generating the patrol report according to the current patrol data and the historical patrol data corresponding to the target patrol point; and sending the inspection report to the server so that the server performs early warning processing based on the inspection report. By adopting the method, the time and the bandwidth of data transmission are saved, and meanwhile, the collected inspection data can be analyzed in time, so that early warning treatment is carried out on inspection points with potential safety hazards.
Description
Technical Field
The application relates to the technical field of power grid inspection, in particular to an early warning processing method, an early warning processing device, early warning processing equipment, a storage medium and a computer program product.
Background
With the continuous development and progress of technology, detection equipment such as intelligent inspection unmanned aerial vehicle, intelligent inspection monitoring, intelligent inspection robot, high-precision thermal infrared imager, three-dimensional laser radar scanning equipment and the like appears, and the inspection means of the transmission line are enriched. In particular, in recent years, unmanned aerial vehicle technology is rapidly advanced, a load inspection transmission line of an unmanned aerial vehicle carrying a special sensor is rapidly developed, and the line is promoted to be converted from a traditional manual inspection mode to an inspection mode. Compared with the traditional manual inspection, the unmanned aerial vehicle inspection has the characteristics of high efficiency, high quality, no influence of terrain conditions and the like, and is an important means for the development of the management of the power transmission line in the directions of safer, efficient, fine and economic.
In the prior art, the intelligent inspection equipment is used for inspecting the power grid, storing inspection data acquired in a period of time, transmitting all the stored inspection data, and uniformly analyzing. However, this method requires a lot of data transmission time, occupies a lot of data transmission bandwidth, and cannot process the collected data in time.
Disclosure of Invention
Based on the above, it is necessary to provide an early warning processing method, device, equipment, storage medium and computer program product for the above technical problems, which save data transmission time and bandwidth, and can analyze collected inspection data in time, so as to perform early warning processing on inspection points with potential safety hazards.
In a first aspect, the present application provides a method for early warning processing. The method comprises the following steps:
acquiring current inspection data corresponding to a target inspection point on an inspection route;
under the condition that the need of generating a patrol report is determined according to the current patrol data, generating the patrol report according to the current patrol data and the historical patrol data corresponding to the target patrol point;
and sending the inspection report to the server so that the server performs early warning processing based on the inspection report.
In one embodiment, determining that a patrol report needs to be generated according to current patrol data includes:
determining an early warning coefficient according to the current inspection data;
if the early warning coefficient is larger than the set threshold, determining that a patrol report needs to be generated.
In one embodiment, generating a patrol report according to the current patrol data and the historical patrol data corresponding to the target patrol point includes:
comparing the historical abnormal data in the historical inspection data with the current abnormal data in the current inspection data to determine repeated abnormal data and/or newly added abnormal data;
and generating a patrol report according to the repeated abnormal data and/or the newly added abnormal data.
In one embodiment, generating a patrol report according to the repeated exception data and the newly added exception data includes:
generating a first patrol sub report according to the newly added abnormal data;
generating a second patrol sub-report according to the repeated abnormal data;
and generating a patrol report according to the first patrol sub-report and the second patrol sub-report.
In one embodiment, generating a second patrol sub-report according to the repeated anomaly data includes:
determining a first occurrence frequency of repeated abnormal data at a target inspection point;
determining second occurrence frequency of repeated abnormal data at other patrol points according to other patrol data of other patrol points on the patrol route;
acquiring a processing scheme of the target inspection point and other inspection points for repeated abnormal data respectively;
and generating a second patrol sub report according to the first occurrence frequency, the second occurrence frequency and the processing scheme.
In one embodiment, obtaining current inspection data corresponding to a target inspection point on an inspection route includes:
and acquiring current inspection data sent by a data acquisition device corresponding to the target inspection point on the inspection route through local area networking.
In a second aspect, the application further provides an early warning processing device. The device comprises:
the data acquisition module is used for acquiring current inspection data corresponding to a target inspection point on the inspection route;
the report generation module is used for generating a patrol report according to the current patrol data and the historical patrol data corresponding to the target patrol point under the condition that the patrol report is required to be generated according to the current patrol data;
and the early warning processing module is used for sending the inspection report to the server so that the server carries out early warning processing based on the inspection report.
In a third aspect, the present application also provides a computer device. The computer device comprises a memory storing a computer program and a processor which when executing the computer program performs the steps of:
acquiring current inspection data corresponding to a target inspection point on an inspection route;
under the condition that the need of generating a patrol report is determined according to the current patrol data, generating the patrol report according to the current patrol data and the historical patrol data corresponding to the target patrol point;
and sending the inspection report to the server so that the server performs early warning processing based on the inspection report.
In a fourth aspect, the present application also provides a computer-readable storage medium. The computer readable storage medium having stored thereon a computer program which when executed by a processor performs the steps of:
acquiring current inspection data corresponding to a target inspection point on an inspection route;
under the condition that the need of generating a patrol report is determined according to the current patrol data, generating the patrol report according to the current patrol data and the historical patrol data corresponding to the target patrol point;
and sending the inspection report to the server so that the server performs early warning processing based on the inspection report.
In a fifth aspect, the present application also provides a computer program product. The computer program product comprises a computer program which, when executed by a processor, implements the steps of:
acquiring current inspection data corresponding to a target inspection point on an inspection route;
under the condition that the need of generating a patrol report is determined according to the current patrol data, generating the patrol report according to the current patrol data and the historical patrol data corresponding to the target patrol point;
and sending the inspection report to the server so that the server performs early warning processing based on the inspection report.
According to the early warning processing method, the early warning processing device, the computer equipment, the storage medium and the computer program product, whether a patrol report needs to be generated for the target patrol point is judged according to the current patrol data by acquiring the current patrol data corresponding to the target patrol point on the patrol route; further, for the inspection point needing to generate the inspection report, the inspection report is generated according to the current inspection data and the historical inspection data and is transmitted to the server, so that the server performs early warning processing on the target inspection point, the acquired inspection data can be analyzed in time while the time and the bandwidth of data transmission are saved, and further the early warning processing is performed on the inspection point with potential safety hazard.
Drawings
FIG. 1 is an application environment diagram of an early warning processing method in one embodiment;
FIG. 2 is a flow chart of a method of early warning processing in one embodiment;
FIG. 3 is a flow diagram of generating a patrol report in one embodiment;
FIG. 4 is a flow diagram of generating a patrol report in another embodiment;
FIG. 5 is a flow chart of a pre-warning method according to another embodiment;
FIG. 6 is a block diagram of an early warning processing device in one embodiment;
FIG. 7 is a block diagram of an apparatus for pre-warning processing according to another embodiment;
fig. 8 is an internal structural diagram of a computer device in one embodiment.
Detailed Description
In order to make the objects, technical solutions and advantages of the present application more apparent, the present application will be further described in detail with reference to the accompanying drawings and examples. It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the present application.
The early warning processing method provided by the embodiment of the application can be applied to an application environment shown in fig. 1. The data acquisition device 102 communicates with the intelligent terminal 104 through a network; further, the intelligent terminal 104 communicates with the server 106 through a network. Specifically, the data acquisition device 102 is installed in a patrol point on a patrol route, acquires patrol data of the patrol point, and constructs a local area network with the intelligent terminal 104; the intelligent terminal 104 is installed on an intelligent inspection device for inspecting an inspection route, and acquires inspection data acquired by the data acquisition device 102 through local networking; further, after the inspection data is obtained, the intelligent terminal 104 analyzes the inspection data, generates an inspection report for the inspection data, and transmits the inspection report to the server 106; and the server 106 performs early warning processing on the inspection points corresponding to the inspection reports according to the acquired inspection reports. The smart terminal 104 may be, but not limited to, various personal computers, notebook computers, smart phones, tablet computers, and portable wearable devices, which may be smart watches, smart bracelets, headsets, etc. The intelligent inspection equipment can be mobile intelligent inspection equipment or intelligent acquisition equipment fixed on an inspection point; specifically, the mobile intelligent inspection device can be, but is not limited to, an intelligent aerial survey unmanned plane, an intelligent inspection robot, an intelligent inspection monitor and other monitoring devices. The server 106 may be implemented as a stand-alone server or as a cluster of servers.
In one embodiment, as shown in fig. 2, an early warning processing method is provided, and the method is applied to the intelligent terminal 104 in fig. 1 for illustration, and includes the following steps:
s201, current inspection data corresponding to a target inspection point on an inspection route is obtained.
In this embodiment, the intelligent inspection device needs to inspect according to a preset inspection route, and the intelligent terminal on the intelligent inspection device can acquire data of each inspection point on the inspection route. The target inspection point is the inspection point on the inspection route, and the intelligent terminal performs data analysis on the inspection data. The current inspection data is the inspection data acquired by the intelligent terminal from the target inspection point at the current moment.
Specifically, after the data acquisition device acquires data of a target inspection point at the current moment, the acquired current inspection data is sent to the intelligent terminal through a pre-constructed local area network; further, the intelligent terminal acquires current inspection data sent by the data acquisition device corresponding to the target inspection point on the inspection route through the local area network.
S202, under the condition that the need of generating a patrol report is determined according to the current patrol data, generating the patrol report according to the current patrol data and the historical patrol data corresponding to the target patrol point.
In this embodiment, the inspection report is a report generated after analyzing the current inspection data; the method can comprise abnormal conditions of the target inspection point, processing schemes aiming at the abnormal conditions which occur once, and the like.
First, according to the current inspection data, determining whether the target inspection point needs to generate an inspection report.
Optionally, determining an early warning coefficient according to the current inspection data; if the early warning coefficient is larger than the set threshold, determining that a patrol report needs to be generated.
In this embodiment, the early warning coefficient is the degree to which the target inspection point needs to perform early warning processing; determining the severity of the line break, for example in the event of a line break at the inspection point; optionally, if the line breakage degree is not serious, no accident occurs, and the early warning coefficient is low; if the line breakage is serious, the power grid is greatly influenced, and the early warning coefficient is high.
Specifically, after the current inspection data are obtained, the current inspection data are analyzed to obtain the early warning coefficient of the target inspection point; further, comparing the early warning coefficient with a set early warning threshold value; if the early warning coefficient is smaller than or equal to the set early warning threshold value, an early warning report is not required to be generated for the target inspection point; if the early warning coefficient is larger than the set early warning threshold value, determining that a patrol report needs to be generated for the target patrol point.
Further, under the condition that a patrol report needs to be generated for the target patrol point, acquiring historical patrol data corresponding to the target patrol point; and generating a patrol report aiming at the target patrol point according to the current patrol data and the acquired historical patrol data.
Alternatively, there may be one or more target inspection points on the inspection route. Under the condition that a plurality of target inspection points exist, current inspection data corresponding to the plurality of target inspection points can be acquired at the same time. Further, current inspection data corresponding to the plurality of acquired target inspection points are input into a preset classification model; the classification model is used for comparing the acquired information such as acquisition time, acquisition position and the like included in the current inspection data with the exclusive identification of each target inspection point, classifying the current inspection data according to each target inspection point and acquiring the current inspection data corresponding to each target inspection point. And further, the current inspection data corresponding to each target inspection point is respectively analyzed at the same time, and an inspection report aiming at each target inspection point is generated.
S203, sending the inspection report to the server so that the server performs early warning processing based on the inspection report.
In this embodiment, the early warning process may, but is not limited to, the server sends an early warning signal for the target inspection point, for example, controls the early warning signal lamp corresponding to the target inspection point to flash, and reminds relevant staff to repair the target inspection point.
Specifically, the intelligent terminal generates a patrol report of a target patrol point based on current patrol data and historical patrol data, and then transmits the patrol report to the server; the server acquires the inspection report transmitted by the intelligent terminal, and performs early warning processing on the target inspection point based on the inspection report.
According to the early warning processing method, whether a patrol report needs to be generated for the target patrol point is judged according to the current patrol data by acquiring the current patrol data corresponding to the target patrol point on the patrol route; further, for the inspection point needing to generate the inspection report, the inspection report is generated according to the current inspection data and the historical inspection data and is transmitted to the server, so that the server performs early warning processing on the target inspection point, the acquired inspection data can be analyzed in time while the time and the bandwidth of data transmission are saved, and further the early warning processing is performed on the inspection point with potential safety hazard.
Based on the foregoing embodiment, in one embodiment, as shown in fig. 3, the refining of S202 may specifically include the following steps:
s301, comparing the historical abnormal data in the historical inspection data with the current abnormal data in the current inspection data to determine repeated abnormal data and/or newly added abnormal data.
In this embodiment, the abnormal data is the data of the target inspection point with abnormality; for example, abnormal conditions such as damper faults, insulator self-explosion, drift, line breakage and the like of the target inspection point are detected, and the abnormal conditions are encoded to obtain corresponding abnormal data. The repeated abnormal data is the abnormal data which occurs in the historical period or is stored in the system; the newly added abnormal data is the abnormal data which does not occur in the history period or is not stored in the system.
Specifically, the obtained current inspection data is analyzed to obtain current abnormal data in the current inspection data; further, each current abnormal data and each historical abnormal data in the historical inspection data are input into a preset model for comparison, and each group of comparison results are compared with a preset comparison threshold value. Optionally, if the comparison result of a certain current abnormal data and a certain historical abnormal data is higher than a preset comparison threshold, determining the current abnormal data as repeated abnormal data; if the comparison result of the current abnormal data and each historical abnormal data is not higher than the preset comparison threshold value, determining the current abnormal data as newly added abnormal data.
S302, generating a patrol report according to the repeated abnormal data and/or the newly added abnormal data.
Optionally, if only repeated abnormal data exists in the current inspection data or only newly added abnormal data exists, generating an inspection report for the target inspection point according to the repeated abnormal data or the newly added abnormal data in the current inspection data; if the current inspection data has repeated abnormal data and newly-increased abnormal data, generating an inspection report for the target inspection point according to the repeated abnormal data and the newly-increased abnormal data in the current inspection data.
It can be understood that by combining each historical abnormal data in the historical inspection data, comparing the historical abnormal data with the current abnormal data in the current inspection data, and determining repeated abnormal data and/or newly added abnormal data in the current inspection data; furthermore, according to repeated abnormal data and/or newly added abnormal data, a patrol report aiming at the target patrol point is generated, so that the generated patrol report is more comprehensive and accurate.
Based on the foregoing embodiment, in one embodiment, as shown in fig. 4, the refining of S302 may specifically include the following steps:
s401, generating a first patrol sub report according to the newly added abnormal data.
In this embodiment, the first patrol sub-report may include a type of the newly added abnormal data, an occurrence time of the newly added abnormal data, an occurrence place of the newly added abnormal data, and the like.
Specifically, after determining that the current abnormal data includes newly-added abnormal data, analyzing the newly-added abnormal data to obtain information such as the type, appearance time, appearance place and the like of the newly-added abnormal data, and generating a first patrol sub-report aiming at the newly-added abnormal data according to the information obtained by analysis.
S402, generating a second patrol sub-report according to the repeated abnormal data.
In this embodiment, the second patrol sub report may include information such as occurrence frequency of repeated abnormal data on the current patrol route, occurrence frequency on other patrol routes, and processing scheme of repeated abnormal data occurring in a history period.
For example, in one implementation manner, after determining that the current abnormal data includes repeated abnormal data, the repeated abnormal data is input into a preset model, and analysis processing is performed on the repeated abnormal data, so as to generate a second patrol sub report.
Optionally, another implementation manner is to determine a first occurrence frequency of the repeated abnormal data at the target inspection point; determining second occurrence frequency of repeated abnormal data at other patrol points according to other patrol data of other patrol points on the patrol route; acquiring a processing scheme of the target inspection point and other inspection points for repeated abnormal data respectively; and generating a second patrol sub report according to the first occurrence frequency, the second occurrence frequency and the processing scheme. The other inspection data are inspection data corresponding to other inspection points on the inspection route in the history period.
Specifically, combining historical inspection data of the target inspection point, analyzing repeated abnormal data to obtain the occurrence frequency of the repeated abnormal data at the target inspection point, and taking the occurrence frequency as a first occurrence frequency; acquiring other inspection data of other inspection points on the inspection route, and analyzing repeated abnormal data by combining the acquired other inspection data of the other inspection points on the inspection route to obtain the occurrence frequency of the repeated abnormal data of the other inspection points on the inspection route, wherein the occurrence frequency is used as a second outgoing frequency; further, historical patrol reports of target patrol points and other patrol points on a patrol route are obtained, data extraction is carried out on each obtained historical patrol report, and a processing scheme of each historical patrol report on repeated abnormal data is extracted; and generating a second patrol sub-report aiming at the repeated abnormal data by combining the first occurrence frequency, the second occurrence frequency and the processing scheme of the repeated abnormal data in the historical period.
S403, generating a patrol report according to the first patrol sub-report and the second patrol sub-report.
In this embodiment, the patrol report may include the type, the appearance time, the appearance place of the newly added abnormal data, the type, the appearance time, the appearance place, the appearance frequency of the repeated abnormal data, the processing scheme for the repeated abnormal data in the history period, and the like.
Specifically, after the newly added abnormal data is analyzed, a first patrol sub report is obtained; analyzing the repeated abnormal data to obtain a second patrol sub report; further, the first patrol sub-report is combined with the second patrol sub-report to generate a patrol report.
It can be understood that by combining the historical inspection data, analyzing the current abnormal data in the current inspection data, and determining the newly added abnormal data and the repeated abnormal data; and respectively generating a first patrol sub-report and a second patrol sub-report aiming at the newly added abnormal data and the repeated abnormal data, so as to obtain the most perfect patrol report, and the generated patrol report is more comprehensive and accurate.
In one embodiment, as shown in FIG. 5, an alternative example of an early warning processing method is provided. The specific process is as follows:
s501, current inspection data corresponding to a target inspection point on an inspection route is obtained.
S502, determining an early warning coefficient according to the current inspection data.
S503, determining whether the early warning coefficient is larger than a set threshold value; if yes, executing S504; if not, S512 is performed.
S504, comparing the historical abnormal data in the historical inspection data with the current abnormal data in the current inspection data to determine repeated abnormal data and newly added abnormal data.
S505, generating a first patrol sub report according to the newly added abnormal data.
S506, determining the first occurrence frequency of the repeated abnormal data at the target inspection point.
S507, determining the second occurrence frequency of repeated abnormal data at other inspection points according to other inspection data of other inspection points on the inspection route.
S508, acquiring a processing scheme of the target inspection point and other inspection points for repeated abnormal data respectively.
S509, generating a second patrol sub-report according to the first occurrence frequency, the second occurrence frequency and the processing scheme.
S510, generating a patrol report according to the first patrol sub-report and the second patrol sub-report.
S511, sending the inspection report to the server so that the server can perform early warning processing based on the inspection report.
S512, the patrol report is not required to be generated.
The specific process of S501-S512 may refer to the description of the above method embodiment, and its implementation principle and technical effect are similar, and are not repeated here.
It should be understood that, although the steps in the flowcharts related to the embodiments described above are sequentially shown as indicated by arrows, these steps are not necessarily sequentially performed in the order indicated by the arrows. The steps are not strictly limited to the order of execution unless explicitly recited herein, and the steps may be executed in other orders. Moreover, at least some of the steps in the flowcharts described in the above embodiments may include a plurality of steps or a plurality of stages, which are not necessarily performed at the same time, but may be performed at different times, and the order of the steps or stages is not necessarily performed sequentially, but may be performed alternately or alternately with at least some of the other steps or stages.
Based on the same inventive concept, the embodiment of the application also provides an early warning processing device for realizing the early warning processing method. The implementation scheme of the solution to the problem provided by the device is similar to that described in the above method, so the specific limitation in the embodiments of one or more pre-warning processing devices provided below may refer to the limitation of the pre-warning processing method hereinabove, and will not be repeated here.
In one embodiment, as shown in fig. 6, there is provided an early warning processing apparatus 1 including: a data acquisition module 10, a report generation module 20 and an early warning processing module 30, wherein:
the data acquisition module 10 is used for acquiring current inspection data corresponding to a target inspection point on an inspection route;
the report generating module 20 is configured to generate a patrol report according to the current patrol data and the historical patrol data corresponding to the target patrol point when it is determined that the patrol report needs to be generated according to the current patrol data;
and the early warning processing module 30 is used for sending the inspection report to the server so that the server performs early warning processing based on the inspection report.
In one embodiment, the report generating module 20 in fig. 5 described above may be specifically configured to:
determining an early warning coefficient according to the current inspection data; if the early warning coefficient is larger than the set threshold, determining that a patrol report needs to be generated.
In one embodiment, as shown in fig. 7, the report generating module 20 in fig. 5 may include:
a data determining unit 21, configured to compare the historical abnormal data in the historical inspection data with the current abnormal data in the current inspection data, and determine repeated abnormal data and/or newly added abnormal data;
and a report generating unit 22, configured to generate a patrol report according to the repeated abnormal data and/or the newly added abnormal data.
In one embodiment, the report generating unit 22 in fig. 7 described above may include:
the first subunit is used for generating a first patrol sub-report according to the newly added abnormal data;
the second subunit is used for generating a second patrol sub-report according to the repeated abnormal data;
and the third subunit is used for generating a patrol report according to the first patrol sub-report and the second patrol sub-report.
In one embodiment, the second subunit may specifically be configured to:
determining a first occurrence frequency of repeated abnormal data at a target inspection point; determining second occurrence frequency of repeated abnormal data at other patrol points according to other patrol data of other patrol points on the patrol route; acquiring a processing scheme of the target inspection point and other inspection points for repeated abnormal data respectively; and generating a second patrol sub report according to the first occurrence frequency, the second occurrence frequency and the processing scheme.
In one embodiment, the data acquisition module 10 in fig. 6 described above may be specifically used to:
and acquiring current inspection data sent by a data acquisition device corresponding to the target inspection point on the inspection route through local area networking.
All or part of the modules in the early warning processing device can be realized by software, hardware and a combination thereof. The above modules may be embedded in hardware or may be independent of a processor in the computer device, or may be stored in software in a memory in the computer device, so that the processor may call and execute operations corresponding to the above modules.
In one embodiment, a computer device is provided, which may be a terminal, and the internal structure thereof may be as shown in fig. 8. The computer device includes a processor, a memory, and a communication interface connected by a system bus. Wherein the processor of the computer device is configured to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage media. The communication interface of the computer device is used for carrying out wired or wireless communication with an external terminal, and the wireless mode can be realized through local area networking, WIFI, a mobile cellular network, NFC (near field communication) or other technologies. The computer program is executed by a processor to implement a pre-warning processing method.
It will be appreciated by those skilled in the art that the structure shown in fig. 8 is merely a block diagram of some of the structures associated with the present application and is not limiting of the computer device to which the present application may be applied, and that a particular computer device may include more or fewer components than shown, or may combine certain components, or have a different arrangement of components.
In one embodiment, a computer device is provided comprising a memory and a processor, the memory having stored therein a computer program, the processor when executing the computer program performing the steps of:
acquiring current inspection data corresponding to a target inspection point on an inspection route;
under the condition that the need of generating a patrol report is determined according to the current patrol data, generating the patrol report according to the current patrol data and the historical patrol data corresponding to the target patrol point;
and sending the inspection report to the server so that the server performs early warning processing based on the inspection report.
In one embodiment, when the processor executes the computer program to determine logic needed to generate a patrol report according to the current patrol data, the following steps are further implemented:
determining an early warning coefficient according to the current inspection data; if the early warning coefficient is larger than the set threshold, determining that a patrol report needs to be generated.
In one embodiment, when the processor executes the logic for generating the inspection report according to the current inspection data and the historical inspection data corresponding to the target inspection point, the following steps are further implemented:
comparing the historical abnormal data in the historical inspection data with the current abnormal data in the current inspection data to determine repeated abnormal data and/or newly added abnormal data; and generating a patrol report according to the repeated abnormal data and/or the newly added abnormal data.
In one embodiment, when the processor executes logic for generating a patrol report from the repeated exception data and the newly added exception data, the processor further performs the steps of:
generating a first patrol sub report according to the newly added abnormal data; generating a second patrol sub-report according to the repeated abnormal data; and generating a patrol report according to the first patrol sub-report and the second patrol sub-report.
In one embodiment, when the processor executes the logic of the computer program to generate the second patrol sub-report according to the repeated anomaly data, the following steps are also implemented:
determining a first occurrence frequency of repeated abnormal data at a target inspection point; determining second occurrence frequency of repeated abnormal data at other patrol points according to other patrol data of other patrol points on the patrol route; acquiring a processing scheme of the target inspection point and other inspection points for repeated abnormal data respectively; and generating a second patrol sub report according to the first occurrence frequency, the second occurrence frequency and the processing scheme.
In one embodiment, when the processor executes logic for obtaining current inspection data corresponding to a target inspection point on an inspection route, the processor further performs the following steps:
and acquiring current inspection data sent by a data acquisition device corresponding to the target inspection point on the inspection route through local area networking.
In one embodiment, a computer readable storage medium is provided having a computer program stored thereon, which when executed by a processor, performs the steps of:
acquiring current inspection data corresponding to a target inspection point on an inspection route;
under the condition that the need of generating a patrol report is determined according to the current patrol data, generating the patrol report according to the current patrol data and the historical patrol data corresponding to the target patrol point;
and sending the inspection report to the server so that the server performs early warning processing based on the inspection report.
In one embodiment, the computer program further performs the following steps when determining that a patrol report needs to be generated based on the current patrol data, the steps being performed by the processor:
determining an early warning coefficient according to the current inspection data; if the early warning coefficient is larger than the set threshold, determining that a patrol report needs to be generated.
In one embodiment, the computer program further performs the following steps when executed by the processor, of generating a patrol report based on the current patrol data and the historical patrol data corresponding to the target patrol point:
comparing the historical abnormal data in the historical inspection data with the current abnormal data in the current inspection data to determine repeated abnormal data and/or newly added abnormal data; and generating a patrol report according to the repeated abnormal data and/or the newly added abnormal data.
In one embodiment, the computer program further performs the following steps when executed by the processor to generate a patrol report based on the repeated exception data and the newly added exception data:
generating a first patrol sub report according to the newly added abnormal data; generating a second patrol sub-report according to the repeated abnormal data; and generating a patrol report according to the first patrol sub-report and the second patrol sub-report.
In one embodiment, the computer program further performs the following steps when executed by the processor to generate a second patrol sub-report based on the repeated anomaly data:
determining a first occurrence frequency of repeated abnormal data at a target inspection point; determining second occurrence frequency of repeated abnormal data at other patrol points according to other patrol data of other patrol points on the patrol route; acquiring a processing scheme of the target inspection point and other inspection points for repeated abnormal data respectively; and generating a second patrol sub report according to the first occurrence frequency, the second occurrence frequency and the processing scheme.
In one embodiment, the computer program further performs the following steps when executed by the processor to obtain current inspection data corresponding to a target inspection point on the inspection route:
and acquiring current inspection data sent by a data acquisition device corresponding to the target inspection point on the inspection route through local area networking.
In one embodiment, a computer program product is provided comprising a computer program which, when executed by a processor, performs the steps of:
acquiring current inspection data corresponding to a target inspection point on an inspection route;
under the condition that the need of generating a patrol report is determined according to the current patrol data, generating the patrol report according to the current patrol data and the historical patrol data corresponding to the target patrol point;
and sending the inspection report to the server so that the server performs early warning processing based on the inspection report.
In one embodiment, the computer program further performs the following steps when determining that a patrol report needs to be generated based on the current patrol data, the steps being performed by the processor:
determining an early warning coefficient according to the current inspection data; if the early warning coefficient is larger than the set threshold, determining that a patrol report needs to be generated.
In one embodiment, the computer program further performs the following steps when executed by the processor, of generating a patrol report based on the current patrol data and the historical patrol data corresponding to the target patrol point:
comparing the historical abnormal data in the historical inspection data with the current abnormal data in the current inspection data to determine repeated abnormal data and/or newly added abnormal data; and generating a patrol report according to the repeated abnormal data and/or the newly added abnormal data.
In one embodiment, the computer program further performs the following steps when executed by the processor to generate a patrol report based on the repeated exception data and the newly added exception data:
generating a first patrol sub report according to the newly added abnormal data; generating a second patrol sub-report according to the repeated abnormal data; and generating a patrol report according to the first patrol sub-report and the second patrol sub-report.
In one embodiment, the computer program further performs the following steps when executed by the processor to generate a second patrol sub-report based on the repeated anomaly data:
determining a first occurrence frequency of repeated abnormal data at a target inspection point; determining second occurrence frequency of repeated abnormal data at other patrol points according to other patrol data of other patrol points on the patrol route; acquiring a processing scheme of the target inspection point and other inspection points for repeated abnormal data respectively; and generating a second patrol sub report according to the first occurrence frequency, the second occurrence frequency and the processing scheme.
In one embodiment, the computer program further performs the following steps when executed by the processor to obtain current inspection data corresponding to a target inspection point on the inspection route:
and acquiring current inspection data sent by a data acquisition device corresponding to the target inspection point on the inspection route through local area networking.
It should be noted that, the data (including, but not limited to, the current inspection data for analysis, the stored historical inspection data, etc.) referred to in the present application are all authorized or sufficiently authorized by each party.
Those skilled in the art will appreciate that implementing all or part of the above described methods may be accomplished by way of a computer program stored on a non-transitory computer readable storage medium, which when executed, may comprise the steps of the embodiments of the methods described above. Any reference to memory, database, or other medium used in the various embodiments provided herein may include at least one of non-volatile and volatile memory. The nonvolatile Memory may include Read-Only Memory (ROM), magnetic tape, floppy disk, flash Memory, optical Memory, high density embedded nonvolatile Memory, resistive random access Memory (ReRAM), magnetic random access Memory (Magnetoresistive Random Access Memory, MRAM), ferroelectric Memory (Ferroelectric Random Access Memory, FRAM), phase change Memory (Phase Change Memory, PCM), graphene Memory, and the like. Volatile memory can include random access memory (Random Access Memory, RAM) or external cache memory, and the like. By way of illustration, and not limitation, RAM can be in the form of a variety of forms, such as static random access memory (Static Random Access Memory, SRAM) or dynamic random access memory (Dynamic Random Access Memory, DRAM), and the like. The databases referred to in the various embodiments provided herein may include at least one of relational databases and non-relational databases. The non-relational database may include, but is not limited to, a blockchain-based distributed database, and the like. The processors referred to in the embodiments provided herein may be general purpose processors, central processing units, graphics processors, digital signal processors, programmable logic units, quantum computing-based data processing logic units, etc., without being limited thereto.
The technical features of the above embodiments may be arbitrarily combined, and all possible combinations of the technical features in the above embodiments are not described for brevity of description, however, as long as there is no contradiction between the combinations of the technical features, they should be considered as the scope of the description.
The above examples only represent a few embodiments of the present application, which are described in more detail and are not to be construed as limiting the scope of the present application. It should be noted that it would be apparent to those skilled in the art that various modifications and improvements could be made without departing from the spirit of the present application, which would be within the scope of the present application. Accordingly, the scope of protection of the present application shall be subject to the appended claims.
Claims (10)
1. An early warning processing method, which is characterized by comprising the following steps:
acquiring current inspection data corresponding to a target inspection point on an inspection route;
generating a patrol report according to the current patrol data and the historical patrol data corresponding to the target patrol point under the condition that the patrol report is required to be generated according to the current patrol data;
and sending the inspection report to a server so that the server performs early warning processing based on the inspection report.
2. The method of claim 1, wherein determining that a patrol report needs to be generated based on the current patrol data comprises:
determining an early warning coefficient according to the current inspection data;
and if the early warning coefficient is larger than a set threshold value, determining that a patrol report needs to be generated.
3. The method of claim 1, wherein generating a patrol report from the current patrol data and the historical patrol data corresponding to the target patrol point comprises:
comparing the historical abnormal data in the historical inspection data with the current abnormal data in the current inspection data to determine repeated abnormal data and/or newly added abnormal data;
and generating a patrol report according to the repeated abnormal data and/or the newly added abnormal data.
4. A method according to claim 3, wherein generating a patrol report from the repeated anomaly data and the newly added anomaly data comprises:
generating a first patrol sub report according to the newly added abnormal data;
generating a second patrol sub report according to the repeated abnormal data;
and generating the inspection report according to the first inspection sub report and the second inspection sub report.
5. The method of claim 4, wherein generating a second patrol report according to the repeated anomaly data comprises:
determining a first occurrence frequency of the repeated abnormal data at the target inspection point;
determining a second occurrence frequency of the repeated abnormal data at other patrol points according to other patrol data of other patrol points on the patrol route;
acquiring a processing scheme of the target inspection point and the other inspection points for the repeated abnormal data respectively;
and generating the second patrol sub report according to the first occurrence frequency, the second occurrence frequency and the processing scheme.
6. The method of claim 1, wherein the obtaining current inspection data corresponding to a target inspection point on the inspection route comprises:
and acquiring current inspection data sent by a data acquisition device corresponding to the target inspection point on the inspection route through local area networking.
7. An early warning processing device, the device comprising:
the data acquisition module is used for acquiring current inspection data corresponding to a target inspection point on the inspection route;
the report generation module is used for generating a patrol report according to the current patrol data and the historical patrol data corresponding to the target patrol point under the condition that the patrol report is required to be generated according to the current patrol data;
and the early warning processing module is used for sending the inspection report to a server so that the server carries out early warning processing based on the inspection report.
8. A computer device comprising a memory and a processor, the memory storing a computer program, characterized in that the processor implements the steps of the method of any of claims 1 to 6 when the computer program is executed.
9. A computer readable storage medium, on which a computer program is stored, characterized in that the computer program, when being executed by a processor, implements the steps of the method of any of claims 1 to 6.
10. A computer program product comprising a computer program, characterized in that the computer program, when being executed by a processor, implements the steps of the method of any of claims 1 to 6.
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| CN116756494A (en) * | 2023-08-22 | 2023-09-15 | 之江实验室 | Data outlier processing method, apparatus, computer device, and readable storage medium |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN116756494A (en) * | 2023-08-22 | 2023-09-15 | 之江实验室 | Data outlier processing method, apparatus, computer device, and readable storage medium |
| CN116756494B (en) * | 2023-08-22 | 2024-01-23 | 之江实验室 | Data outlier processing method, apparatus, computer device, and readable storage medium |
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