Early warning processing method, device and equipment, storage medium and computer program product

Abstract

The application relates to a pre-warning processing method, device, equipment, storage medium and computer program product. The method comprises the following steps: acquiring current inspection data corresponding to a target inspection point on an inspection route; in the case that it is determined according to the current inspection data that an inspection report needs to be generated, generating the inspection report according to the current inspection data and historical inspection data corresponding to the target inspection point; and sending the inspection report to a server, so that the server performs pre-warning processing based on the inspection report. By adopting the method, the inspection data collected can be analyzed in time while saving the time and bandwidth of data transmission, and the inspection point with a safety hidden danger can be pre-warned.

Description

Technical Field

[0001] This application relates to the field of power grid inspection technology, and in particular to early warning processing methods, devices, equipment, storage media, and computer program products. Background Technology

[0002] With continuous technological advancements, intelligent inspection drones, intelligent inspection monitoring systems, intelligent inspection robots, high-precision infrared thermal imagers, and 3D lidar scanning equipment have emerged, enriching the methods for inspecting power transmission lines. In recent years, in particular, drone technology has advanced rapidly, and the use of drones equipped with dedicated sensor payloads for power transmission line inspection has seen explosive growth, promoting the shift from traditional manual inspection to drone-based inspection. Compared to traditional manual inspection, drone inspection offers advantages such as high efficiency, high quality, and immunity to terrain conditions, making it a crucial means for power transmission line management to move towards greater safety, efficiency, precision, and economy.

[0003] Existing technologies involve using intelligent inspection equipment to inspect the power grid, storing the inspection data collected over a period of time, and then transmitting and analyzing all the stored inspection data in a unified manner. However, this method requires a significant amount of data transmission time and consumes a large amount of data transmission bandwidth, making it impossible to process the collected data in a timely manner. Summary of the Invention

[0004] Therefore, it is necessary to provide an early warning processing method, device, equipment, storage medium, and computer program product to address the above-mentioned technical problems. This method saves data transmission time and bandwidth, enables timely analysis of collected inspection data, and provides early warning processing for inspection points with potential safety hazards.

[0005] Firstly, this application provides an early warning processing method. The method includes:

[0006] Obtain the current inspection data corresponding to the target inspection point on the inspection route;

[0007] If it is determined that an inspection report needs to be generated based on the current inspection data, an inspection report is generated based on the current inspection data and the historical inspection data corresponding to the target inspection point.

[0008] Send inspection reports to the server so that the server can perform early warning processing based on the inspection reports.

[0009] In one embodiment, based on the current inspection data, it is determined that an inspection report needs to be generated, including:

[0010] Determine the early warning coefficient based on the current inspection data;

[0011] If the warning coefficient is greater than the set threshold, then it is determined that an inspection report needs to be generated.

[0012] In one embodiment, an inspection report is generated based on the current inspection data and the historical inspection data corresponding to the target inspection point, including:

[0013] Compare historical abnormal data in historical inspection data with current abnormal data in current inspection data to identify duplicate abnormal data and / or newly added abnormal data.

[0014] Generate inspection reports based on duplicate and / or newly added abnormal data.

[0015] In one embodiment, an inspection report is generated based on duplicate and newly added abnormal data, including:

[0016] Generate the first inspection sub-report based on the newly added abnormal data;

[0017] Based on the duplicate anomaly data, a second inspection sub-report is generated;

[0018] An inspection report is generated based on the first and second inspection sub-reports.

[0019] In one embodiment, a second inspection sub-report is generated based on the recurring anomaly data, including:

[0020] Determine the frequency of the first occurrence of duplicate abnormal data at the target inspection point;

[0021] Based on other inspection data from other inspection points along the inspection route, determine the second frequency of occurrence of duplicate abnormal data at other inspection points.

[0022] Obtain the handling schemes for duplicate and abnormal data at the target inspection point and other inspection points respectively;

[0023] Based on the frequency of the first occurrence, the frequency of the second occurrence, and the handling plan, a second inspection sub-report is generated.

[0024] In one embodiment, obtaining the current inspection data corresponding to the target inspection point on the inspection route includes:

[0025] The system acquires the current inspection data sent by the data acquisition device corresponding to the target inspection point on the inspection route through a local area network.

[0026] Secondly, this application also provides an early warning processing device. The device includes:

[0027] The data acquisition module is used to acquire the current inspection data corresponding to the target inspection point on the inspection route;

[0028] The report generation module is used to generate an inspection report based on the current inspection data and the historical inspection data corresponding to the target inspection point, when it is determined that an inspection report needs to be generated based on the current inspection data.

[0029] The early warning processing module is used to send inspection reports to the server so that the server can perform early warning processing based on the inspection reports.

[0030] Thirdly, this application also provides a computer device. The computer device includes a memory and a processor, the memory storing a computer program, and the processor executing the computer program to perform the following steps:

[0031] Obtain the current inspection data corresponding to the target inspection point on the inspection route;

[0032] If it is determined that an inspection report needs to be generated based on the current inspection data, an inspection report is generated based on the current inspection data and the historical inspection data corresponding to the target inspection point.

[0033] Send inspection reports to the server so that the server can perform early warning processing based on the inspection reports.

[0034] Fourthly, this application also provides a computer-readable storage medium. The computer-readable storage medium stores a computer program thereon, which, when executed by a processor, performs the following steps:

[0035] Obtain the current inspection data corresponding to the target inspection point on the inspection route;

[0036] If it is determined that an inspection report needs to be generated based on the current inspection data, an inspection report is generated based on the current inspection data and the historical inspection data corresponding to the target inspection point.

[0037] Send inspection reports to the server so that the server can perform early warning processing based on the inspection reports.

[0038] Fifthly, this application also provides a computer program product. The computer program product includes a computer program that, when executed by a processor, performs the following steps:

[0039] Obtain the current inspection data corresponding to the target inspection point on the inspection route;

[0040] If it is determined that an inspection report needs to be generated based on the current inspection data, an inspection report is generated based on the current inspection data and the historical inspection data corresponding to the target inspection point.

[0041] Send inspection reports to the server so that the server can perform early warning processing based on the inspection reports.

[0042] The aforementioned early warning processing method, device, computer equipment, storage medium, and computer program product acquire the current inspection data corresponding to the target inspection point on the inspection route, and determine whether an inspection report needs to be generated for the target inspection point based on the current inspection data. Furthermore, for inspection points that require the generation of inspection reports, an inspection report is generated based on the current inspection data and historical inspection data, and transmitted to the server so that the server can perform early warning processing on the target inspection point. This saves data transmission time and bandwidth, while enabling timely analysis of the collected inspection data, and thus providing early warning processing for inspection points with potential safety hazards. Attached Figure Description

[0043] Figure 1 This is an application environment diagram of the early warning processing method in one embodiment;

[0044] Figure 2 This is a flowchart illustrating the early warning processing method in one embodiment;

[0045] Figure 3 This is a flowchart illustrating the process of generating an inspection report in one embodiment;

[0046] Figure 4 This is a flowchart illustrating the process of generating an inspection report in another embodiment;

[0047] Figure 5 This is a flowchart illustrating the early warning processing method in another embodiment;

[0048] Figure 6 This is a structural block diagram of the early warning processing device in one embodiment;

[0049] Figure 7 This is a structural block diagram of the early warning processing device in another embodiment;

[0050] Figure 8 This is an internal structural diagram of a computer device in one embodiment. Detailed Implementation

[0051] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0052] The early warning processing method provided in this application embodiment can be applied to, for example, Figure 1In the application environment shown, the data acquisition device 102 communicates with the smart terminal 104 via a network; furthermore, the smart terminal 104 communicates with the server 106 via a network. Specifically, the data acquisition device 102 is installed at inspection points along the inspection route to collect inspection data from these points and establishes a local area network with the smart terminal 104; the smart terminal 104 is installed on a smart inspection device that inspects the inspection route and acquires the inspection data collected by the data acquisition device 102 through the local area network; furthermore, after acquiring the inspection data, the smart terminal 104 analyzes the inspection data, generates an inspection report, and transmits the inspection report to the server 106; the server 106, based on the acquired inspection report, performs early warning processing on the inspection points corresponding to the inspection report. The smart terminal 104 can be, but is not limited to, various personal computers, laptops, smartphones, tablets, and portable wearable devices, such as smartwatches, smart bracelets, and head-mounted devices. Intelligent inspection equipment can be mobile intelligent inspection equipment or fixed intelligent data acquisition equipment at inspection points; specifically, mobile intelligent inspection equipment can be, but is not limited to, intelligent aerial survey drones, intelligent inspection robots, intelligent inspection monitoring equipment, etc. Server 106 can be implemented using a standalone server or a server cluster composed of multiple servers.

[0053] In one embodiment, such as Figure 2 As shown, an early warning processing method is provided, which can be applied to... Figure 1 Taking the smart terminal 104 as an example, the explanation includes the following steps:

[0054] S201, Obtain the current inspection data corresponding to the target inspection point on the inspection route.

[0055] In this embodiment, the intelligent inspection equipment needs to perform inspections according to a pre-set inspection route. The intelligent terminal on the intelligent inspection equipment can acquire data from each inspection point along the inspection route. Optionally, there can be one or more pre-set inspection routes in an area to be inspected. The target inspection point is the inspection point on the inspection route where 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.

[0056] Specifically, after the data acquisition device collects data from the target inspection point at the current moment, it sends the collected current inspection data to the smart terminal through a pre-built local area network. Furthermore, the smart terminal obtains the current inspection data sent by the data acquisition device corresponding to the target inspection point on the inspection route through the local area network.

[0057] S202, if it is determined that an inspection report needs to be generated based on the current inspection data, an inspection report is generated based on the current inspection data and the historical inspection data corresponding to the target inspection point.

[0058] In this embodiment, the inspection report is a report generated after analyzing the current inspection data; it may include abnormal situations that occur at the target inspection point, as well as handling solutions for abnormal situations that have occurred in the past.

[0059] First, based on the current inspection data, determine whether an inspection report needs to be generated for the target inspection point.

[0060] Optionally, an early warning coefficient can be determined based on the current inspection data; if the early warning coefficient is greater than the set threshold, then it is determined that an inspection report needs to be generated.

[0061] In this embodiment, the warning coefficient is the degree to which the target inspection point needs to be warned; for example, in the case of a line break at the inspection point, the severity of the line break is determined; optionally, if it is determined that the degree of the line break is not serious and will not lead to an accident, the warning coefficient is low; if it is determined that the line break is very serious and will have a huge impact on the power grid, the warning coefficient is high.

[0062] Specifically, after obtaining the current inspection data, the current inspection data is analyzed to obtain the warning coefficient of the target inspection point; further, the warning coefficient is compared with the set warning threshold; if the warning coefficient is less than or equal to the set warning threshold, there is no need to generate a warning report for the target inspection point; if the warning coefficient is greater than the set warning threshold, it is determined that an inspection report needs to be generated for the target inspection point.

[0063] Furthermore, if it is determined that an inspection report needs to be generated for the target inspection point, the historical inspection data corresponding to the target inspection point is obtained; based on the current inspection data and the obtained historical inspection data, an inspection report is generated for the target inspection point.

[0064] Optionally, there can be one or more target inspection points on the inspection route. When multiple target inspection points exist, the current inspection data corresponding to multiple target inspection points can be acquired simultaneously. Further, the acquired current inspection data corresponding to multiple target inspection points is input into a pre-defined classification model. The classification model compares the information included in the acquired current inspection data, such as the collection time and collection location, with the unique identifier of each target inspection point, classifying the current inspection data according to each target inspection point, and obtaining the current inspection data corresponding to each target point. This enables the analysis of the current inspection data corresponding to each target inspection point at the same time, generating inspection reports for each target inspection point.

[0065] S203 sends an inspection report to the server so that the server can perform early warning processing based on the inspection report.

[0066] In this embodiment, the early warning processing may include, but is not limited to, the server issuing an early warning signal for the target inspection point. For example, it may control the early warning signal light corresponding to the target inspection point to flash, reminding relevant personnel to carry out maintenance or other processing at the target inspection point.

[0067] Specifically, the smart terminal generates an inspection report for the target inspection point based on the current inspection data and historical inspection data, and then transmits the inspection report to the server. The server obtains the inspection report transmitted by the smart terminal and performs early warning processing on the target inspection point based on the inspection report.

[0068] The aforementioned early warning processing method obtains the current inspection data corresponding to the target inspection point on the inspection route, and determines whether an inspection report needs to be generated for the target inspection point based on the current inspection data. Furthermore, for inspection points that require the generation of inspection reports, an inspection report is generated based on the current inspection data and historical inspection data, and transmitted to the server so that the server can perform early warning processing for the target inspection point. This saves data transmission time and bandwidth, while enabling timely analysis of the collected inspection data, and thus providing early warning processing for inspection points with potential safety hazards.

[0069] Based on the above embodiments, in one embodiment, such as Figure 3 As shown, the above S202 can be further refined and may include the following steps:

[0070] S301, compare the historical abnormal data in the historical inspection data with the current abnormal data in the current inspection data to identify duplicate abnormal data and / or newly added abnormal data.

[0071] In this embodiment, abnormal data refers to data indicating anomalies at the target inspection point. For example, if anomalies such as vibration damper failure, insulator spontaneous explosion, external debris, or line breakage are detected at the target inspection point, the anomalies are encoded to obtain corresponding abnormal data. Duplicate abnormal data refers to abnormal data that has occurred in historical periods or has been stored in the system; newly added abnormal data refers to abnormal data that has not occurred in historical periods or has not been stored in the system.

[0072] Specifically, the acquired current inspection data is analyzed to identify current abnormal data. Further, each current abnormal data point is compared with each historical abnormal data point in the historical inspection data to a pre-defined model, and the comparison results are compared with preset comparison thresholds. Optionally, if the comparison result of a current abnormal data point with a historical abnormal data point is higher than the preset comparison threshold, the current abnormal data point is identified as duplicate abnormal data; if the comparison results of a current abnormal data point with each historical abnormal data point are not higher than the preset comparison threshold, the current abnormal data point is identified as newly added abnormal data.

[0073] S302, Generate an inspection report based on duplicate and / or newly added abnormal data.

[0074] Optionally, if the current inspection data contains only duplicate abnormal data or only newly added abnormal data, an inspection report for the target inspection point will be generated for the duplicate or newly added abnormal data in the current inspection data; if the current inspection data contains both duplicate and newly added abnormal data, an inspection report for the target inspection point will be generated for both duplicate and newly added abnormal data in the current inspection data.

[0075] Understandably, by combining historical abnormal data from historical inspection data with current abnormal data from current inspection data, duplicate and / or newly added abnormal data in the current inspection data are identified. Furthermore, based on the duplicate and / or newly added abnormal data, an inspection report for the target inspection point is generated, making the generated inspection report more comprehensive and accurate.

[0076] Based on the above embodiments, in one embodiment, such as Figure 4 As shown, the above S302 can be further refined and may include the following steps:

[0077] S401, Generate the first inspection sub-report based on the newly added abnormal data.

[0078] In this embodiment, the first inspection sub-report may include the type of newly added abnormal data, the time when the newly added abnormal data appeared, and the location where the newly added abnormal data appeared.

[0079] Specifically, after determining that the current abnormal data includes newly added abnormal data, the newly added abnormal data is analyzed to obtain information such as the type, time of occurrence, and location of occurrence of the newly added abnormal data. Based on the information obtained from the analysis, the first inspection sub-report for the newly added abnormal data is generated.

[0080] S402, Generate a second inspection sub-report based on the repeated abnormal data.

[0081] In this embodiment, the second inspection sub-report may include information such as the frequency of occurrence of duplicate abnormal data on the current inspection route, the frequency of occurrence on other inspection routes, and the handling plan for duplicate abnormal data occurring in historical periods.

[0082] For example, one possible approach is to determine that the current abnormal data includes duplicate abnormal data, input the duplicate abnormal data into a pre-defined model, analyze and process the duplicate abnormal data, and then generate a second inspection sub-report.

[0083] Optionally, another possible approach is to determine the first frequency of occurrence of duplicate anomaly data at the target inspection point; based on other inspection data from other inspection points along the inspection route, determine the second frequency of occurrence of duplicate anomaly data at other inspection points; obtain the processing plans for duplicate anomaly data at the target inspection point and other inspection points respectively; and generate a second inspection sub-report based on the first frequency of occurrence, the second frequency of occurrence, and the processing plan. Here, the other inspection data refers to the inspection data corresponding to other inspection points along the inspection route within the historical time period.

[0084] Specifically, by combining historical inspection data of the target inspection point, duplicate anomaly data is analyzed to obtain the frequency of occurrence of duplicate anomaly data at the target inspection point, and this frequency is used as the first occurrence frequency. Other inspection data from other inspection points along the inspection route are acquired, and by combining this acquired data with the duplicate anomaly data, duplicate anomaly data is analyzed to obtain the frequency of occurrence of duplicate anomaly data at other inspection points along the inspection route, and this frequency is used as the second occurrence frequency. Further, historical inspection reports from the target inspection point and other inspection points along the inspection route are acquired, and data extraction is performed on each historical inspection report to extract the processing solutions for duplicate anomaly data. Combining the first occurrence frequency, the second occurrence frequency, and the processing solutions for duplicate anomaly data in historical time periods, a second inspection sub-report for duplicate anomaly data is generated.

[0085] S403, Generate an inspection report based on the first and second inspection sub-reports.

[0086] In this embodiment, the inspection report may include the type, time and location of newly added abnormal data, the type, time, location and frequency of repeated abnormal data, and the processing scheme for repeated abnormal data in historical time periods.

[0087] Specifically, after analyzing newly added abnormal data, a first inspection sub-report is obtained; after analyzing duplicate abnormal data, a second inspection sub-report is obtained; further, the first and second inspection sub-reports are combined to generate an inspection report.

[0088] Understandably, by combining historical inspection data, the current abnormal data in the current inspection data is analyzed to identify newly added and duplicate abnormal data. For newly added and duplicate abnormal data, a first inspection sub-report and a second inspection sub-report are generated respectively, thus obtaining the most complete inspection report, making the generated inspection report more comprehensive and accurate.

[0089] In one embodiment, such as Figure 5 As shown, an optional example of an early warning processing method is provided. The specific process is as follows:

[0090] S501, obtain the current inspection data corresponding to the target inspection point on the inspection route.

[0091] S502, determine the early warning coefficient based on the current inspection data.

[0092] S503, determine whether the warning coefficient is greater than the set threshold; if yes, execute S504; if no, execute S512.

[0093] S504 compares historical abnormal data in historical inspection data with current abnormal data in current inspection data to identify duplicate abnormal data and newly added abnormal data.

[0094] S505, based on the newly added abnormal data, generate the first inspection sub-report.

[0095] S506, determine the frequency of the first occurrence of duplicate abnormal data at the target inspection point.

[0096] S507, based on other inspection data from other inspection points along the inspection route, determine the second frequency of occurrence of duplicate abnormal data at other inspection points.

[0097] S508, obtain the processing plan for duplicate abnormal data of the target inspection point and other inspection points respectively.

[0098] S509, based on the first occurrence frequency, the second occurrence frequency, and the handling plan, generate the second inspection sub-report.

[0099] S510 generates an inspection report based on the first and second inspection sub-reports.

[0100] S511 sends an inspection report to the server so that the server can perform early warning processing based on the inspection report.

[0101] S512 eliminates the need to generate inspection reports.

[0102] The specific processes of S501-S512 described above can be found in the description of the above method embodiments. Their implementation principles and technical effects are similar, and will not be repeated here.

[0103] It should be understood that although the steps in the flowcharts of the embodiments described above are shown sequentially according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the flowcharts of the embodiments described above may include multiple steps or multiple stages. These steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these steps or stages is not necessarily sequential, but can be performed alternately or in turn with other steps or at least some of the steps or stages of other steps.

[0104] Based on the same inventive concept, this application also provides an early warning processing device for implementing the aforementioned early warning processing method. The solution provided by this device is similar to the implementation described in the above method; therefore, the specific limitations in one or more early warning processing device embodiments provided below can be found in the limitations of the early warning processing method described above, and will not be repeated here.

[0105] In one embodiment, such as Figure 6 As shown, an early warning processing device 1 is provided, comprising: a data acquisition module 10, a report generation module 20, and an early warning processing module 30, wherein:

[0106] Data acquisition module 10 is used to acquire the current inspection data corresponding to the target inspection point on the inspection route;

[0107] The report generation module 20 is used to generate an inspection report based on the current inspection data and the historical inspection data corresponding to the target inspection point, when it is determined that an inspection report needs to be generated based on the current inspection data.

[0108] The early warning processing module 30 is used to send an inspection report to the server so that the server can perform early warning processing based on the inspection report.

[0109] In one embodiment, the above Figure 5 The report generation module 20 can be specifically used for:

[0110] Based on the current inspection data, determine the early warning coefficient; if the early warning coefficient is greater than the set threshold, then it is determined that an inspection report needs to be generated.

[0111] In one embodiment, such as Figure 7 As shown above, Figure 5 The report generation module 20 may include:

[0112] The data determination unit 21 is used to compare the historical abnormal data in the historical inspection data with the current abnormal data in the current inspection data to determine the duplicate abnormal data and / or the newly added abnormal data.

[0113] The report generation unit 22 is used to generate inspection reports based on duplicate abnormal data and / or newly added abnormal data.

[0114] In one embodiment, the above Figure 7 The report generation unit 22 may include:

[0115] The first sub-unit is used to generate the first inspection sub-report based on the newly added abnormal data;

[0116] The second sub-unit is used to generate a second inspection sub-report based on the duplicate anomaly data;

[0117] The third sub-unit is used to generate an inspection report based on the first and second inspection sub-reports.

[0118] In one embodiment, the second subunit described above can be specifically used for:

[0119] Determine the first occurrence frequency of duplicate abnormal data at the target inspection point; based on other inspection data from other inspection points along the inspection route, determine the second occurrence frequency of duplicate abnormal data at other inspection points; obtain the handling plans for duplicate abnormal data at the target inspection point and other inspection points respectively; generate a second inspection sub-report based on the first occurrence frequency, the second occurrence frequency, and the handling plan.

[0120] In one embodiment, the above Figure 6 The data acquisition module 10 in the middle can be specifically used for:

[0121] The system acquires the current inspection data sent by the data acquisition device corresponding to the target inspection point on the inspection route through a local area network.

[0122] Each module in the aforementioned early warning processing device can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in or independent of the processor in a computer device, or stored in the memory of a computer device as software, so that the processor can call and execute the corresponding operations of each module.

[0123] In one embodiment, a computer device is provided, which may be a terminal, and its internal structure diagram may be as follows: Figure 8As shown, the computer device includes a processor, memory, and a communication interface connected via a system bus. The processor provides computing and control capabilities. The memory includes a non-volatile storage medium and internal memory. The non-volatile storage medium stores the operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs stored in the non-volatile storage medium. The communication interface is used for wired or wireless communication with external terminals; wireless communication can be achieved through local area networking, Wi-Fi, mobile cellular networks, NFC (Near Field Communication), or other technologies. When the computer program is executed by the processor, it implements an early warning processing method.

[0124] Those skilled in the art will understand that Figure 8 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0125] In one embodiment, a computer device is provided, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to perform the following steps:

[0126] Obtain the current inspection data corresponding to the target inspection point on the inspection route;

[0127] If it is determined that an inspection report needs to be generated based on the current inspection data, an inspection report is generated based on the current inspection data and the historical inspection data corresponding to the target inspection point.

[0128] Send inspection reports to the server so that the server can perform early warning processing based on the inspection reports.

[0129] In one embodiment, when the processor executes the logic of the computer program to determine whether an inspection report needs to be generated based on the current inspection data, it also implements the following steps:

[0130] Based on the current inspection data, determine the early warning coefficient; if the early warning coefficient is greater than the set threshold, then it is determined that an inspection report needs to be generated.

[0131] In one embodiment, when the processor executes the logic of a computer program to generate an inspection report based on the current inspection data and the historical inspection data corresponding to the target inspection point, it also implements the following steps:

[0132] Compare historical abnormal data in historical inspection data with current abnormal data in current inspection data to identify duplicate abnormal data and / or newly added abnormal data; generate an inspection report based on duplicate abnormal data and / or newly added abnormal data.

[0133] In one embodiment, when the processor executes the logic of the computer program to generate an inspection report based on duplicate and new abnormal data, it also implements the following steps:

[0134] Based on newly added abnormal data, generate the first inspection sub-report; based on duplicate abnormal data, generate the second inspection sub-report; based on the first and second inspection sub-reports, generate the inspection report.

[0135] In one embodiment, when the processor executes the logic of the computer program to generate a second inspection sub-report based on recurring anomaly data, it also implements the following steps:

[0136] Determine the first occurrence frequency of duplicate abnormal data at the target inspection point; based on other inspection data from other inspection points along the inspection route, determine the second occurrence frequency of duplicate abnormal data at other inspection points; obtain the handling plans for duplicate abnormal data at the target inspection point and other inspection points respectively; generate a second inspection sub-report based on the first occurrence frequency, the second occurrence frequency, and the handling plan.

[0137] In one embodiment, when the processor executes the logic of the computer program to obtain the current inspection data corresponding to the target inspection point on the inspection route, it also implements the following steps:

[0138] The system acquires the current inspection data sent by the data acquisition device corresponding to the target inspection point on the inspection route through a local area network.

[0139] In one embodiment, a computer-readable storage medium is provided having a computer program stored thereon, the computer program performing the following steps when executed by a processor:

[0140] Obtain the current inspection data corresponding to the target inspection point on the inspection route;

[0141] If it is determined that an inspection report needs to be generated based on the current inspection data, an inspection report is generated based on the current inspection data and the historical inspection data corresponding to the target inspection point.

[0142] Send inspection reports to the server so that the server can perform early warning processing based on the inspection reports.

[0143] In one embodiment, when the computer program determines that an inspection report needs to be generated based on the current inspection data and is executed by the processor, it also performs the following steps:

[0144] Based on the current inspection data, determine the early warning coefficient; if the early warning coefficient is greater than the set threshold, then it is determined that an inspection report needs to be generated.

[0145] In one embodiment, when the computer program generates an inspection report based on the current inspection data and the historical inspection data corresponding to the target inspection point, the process also performs the following steps:

[0146] Compare historical abnormal data in historical inspection data with current abnormal data in current inspection data to identify duplicate abnormal data and / or newly added abnormal data; generate an inspection report based on duplicate abnormal data and / or newly added abnormal data.

[0147] In one embodiment, when the computer program generates an inspection report based on duplicate and new abnormal data, and the report is executed by the processor, it also performs the following steps:

[0148] Based on newly added abnormal data, generate the first inspection sub-report; based on duplicate abnormal data, generate the second inspection sub-report; based on the first and second inspection sub-reports, generate the inspection report.

[0149] In one embodiment, when the computer program generates a second inspection sub-report based on recurring anomaly data and is executed by the processor, it also performs the following steps:

[0150] Determine the first occurrence frequency of duplicate abnormal data at the target inspection point; based on other inspection data from other inspection points along the inspection route, determine the second occurrence frequency of duplicate abnormal data at other inspection points; obtain the handling plans for duplicate abnormal data at the target inspection point and other inspection points respectively; generate a second inspection sub-report based on the first occurrence frequency, the second occurrence frequency, and the handling plan.

[0151] In one embodiment, when the computer program obtains the current inspection data corresponding to the target inspection point on the inspection route and is executed by the processor, it also performs the following steps:

[0152] The system acquires the current inspection data sent by the data acquisition device corresponding to the target inspection point on the inspection route through a local area network.

[0153] In one embodiment, a computer program product is provided, including a computer program that, when executed by a processor, performs the following steps:

[0154] Obtain the current inspection data corresponding to the target inspection point on the inspection route;

[0155] If it is determined that an inspection report needs to be generated based on the current inspection data, an inspection report is generated based on the current inspection data and the historical inspection data corresponding to the target inspection point.

[0156] Send inspection reports to the server so that the server can perform early warning processing based on the inspection reports.

[0157] In one embodiment, when the computer program determines that an inspection report needs to be generated based on the current inspection data and is executed by the processor, it also performs the following steps:

[0158] Based on the current inspection data, determine the early warning coefficient; if the early warning coefficient is greater than the set threshold, then it is determined that an inspection report needs to be generated.

[0159] In one embodiment, when the computer program generates an inspection report based on the current inspection data and the historical inspection data corresponding to the target inspection point, the process also performs the following steps:

[0160] Compare historical abnormal data in historical inspection data with current abnormal data in current inspection data to identify duplicate abnormal data and / or newly added abnormal data; generate an inspection report based on duplicate abnormal data and / or newly added abnormal data.

[0161] In one embodiment, when the computer program generates an inspection report based on duplicate and new abnormal data, and the report is executed by the processor, it also performs the following steps:

[0162] Based on newly added abnormal data, generate the first inspection sub-report; based on duplicate abnormal data, generate the second inspection sub-report; based on the first and second inspection sub-reports, generate the inspection report.

[0163] In one embodiment, when the computer program generates a second inspection sub-report based on recurring anomaly data and is executed by the processor, it also performs the following steps:

[0164] Determine the first occurrence frequency of duplicate abnormal data at the target inspection point; based on other inspection data from other inspection points along the inspection route, determine the second occurrence frequency of duplicate abnormal data at other inspection points; obtain the handling plans for duplicate abnormal data at the target inspection point and other inspection points respectively; generate a second inspection sub-report based on the first occurrence frequency, the second occurrence frequency, and the handling plan.

[0165] In one embodiment, when the computer program obtains the current inspection data corresponding to the target inspection point on the inspection route and is executed by the processor, it also performs the following steps:

[0166] The system acquires the current inspection data sent by the data acquisition device corresponding to the target inspection point on the inspection route through a local area network.

[0167] It should be noted that the data involved in this application (including but not limited to current inspection data used for analysis, stored historical inspection data, etc.) are all authorized or fully authorized by all parties.

[0168] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium, and when executed, it can include the processes of the embodiments of the above methods. Any references to memory, databases, or other media used in the embodiments provided in this application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM). The databases involved in the embodiments provided in this application may include at least one type of relational database and non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the embodiments provided in this application may be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, quantum computing-based data processing logic devices, etc., and are not limited to these.

[0169] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0170] The embodiments described above are merely illustrative of several implementation methods of this application, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of this patent application. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this application should be determined by the appended claims.

Claims

1. A method for early warning processing, characterized in that, The method includes: Obtain the current inspection data corresponding to the target inspection point on the inspection route; If it is determined that an inspection report needs to be generated based on the current inspection data, each historical abnormal data in the historical inspection data is compared with each current abnormal data in the current inspection data to obtain the comparison results between each current abnormal data and each historical abnormal data. For any current abnormal data, if the comparison result between the current abnormal data and historical abnormal data is greater than the comparison threshold, the current abnormal data is considered as duplicate abnormal data; if the comparison result between the current abnormal data and historical abnormal data is not greater than the comparison threshold, the current abnormal data is considered as newly added abnormal data. Based on the newly added abnormal data, a first inspection sub-report is generated; wherein, the first inspection sub-report includes the type, time of occurrence, and location of the newly added abnormal data; Based on the recurring abnormal data, a second inspection sub-report is generated; The inspection report is generated based on the first inspection sub-report and the second inspection sub-report; The inspection report is sent to the server so that the server can perform early warning processing based on the inspection report; The second inspection sub-report is generated based on the recurring abnormal data, including: Determine the first occurrence frequency of the recurring abnormal data at the target inspection point; Based on other inspection data from other inspection points along the inspection route, determine the second frequency of occurrence of the repeated abnormal data at those other inspection points; Obtain the processing schemes for the duplicate abnormal data at the target inspection point and the other inspection points respectively; The second inspection sub-report is generated based on the first occurrence frequency, the second occurrence frequency, and the processing scheme.

2. The method according to claim 1, characterized in that, The step of determining whether an inspection report needs to be generated based on the current inspection data includes: Based on the current inspection data, determine the early warning coefficient; If the warning coefficient is greater than the set threshold, then it is determined that an inspection report needs to be generated.

3. The method according to claim 1, characterized in that, The acquisition of the current inspection data corresponding to the target inspection point on the inspection route includes: The system acquires the current inspection data sent by the data acquisition device corresponding to the target inspection point on the inspection route through a local area network.

4. An early warning processing device, characterized in that, The device includes: The data acquisition module is used to acquire the current inspection data corresponding to the target inspection point on the inspection route; The report generation module is used to compare each historical abnormal data in the historical inspection data with each current abnormal data in the current inspection data when it is determined that an inspection report needs to be generated based on the current inspection data, and obtain the comparison results between each current abnormal data and each historical abnormal data. For any current abnormal data, if the comparison result between the current abnormal data and historical abnormal data is greater than the comparison threshold, then the current abnormal data is regarded as duplicate abnormal data. If there is no historical abnormal data and the comparison result of the current abnormal data is greater than the comparison threshold, then the current abnormal data is regarded as newly added abnormal data; Based on the newly added abnormal data, a first inspection sub-report is generated; wherein, the first inspection sub-report includes the type, time of occurrence, and location of the newly added abnormal data; A second inspection sub-report is generated based on the repeated abnormal data; the inspection report is generated based on the first inspection sub-report and the second inspection sub-report. The early warning processing module is used to send the inspection report to the server so that the server can perform early warning processing based on the inspection report. The report generation module is also used for: Determine the first occurrence frequency of the recurring abnormal data at the target inspection point; Based on other inspection data from other inspection points along the inspection route, determine the second frequency of occurrence of the repeated abnormal data at those other inspection points; Obtain the processing schemes for the duplicate abnormal data at the target inspection point and the other inspection points respectively; The second inspection sub-report is generated based on the first occurrence frequency, the second occurrence frequency, and the processing scheme.

5. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the method according to any one of claims 1 to 3.

6. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 3.

7. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 3.

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