Risk monitoring method, system, and storage medium

Abstract

The application relates to a risk monitoring method, a system and a storage medium thereof. The risk monitoring method comprises the following steps: calculating monitoring information of a to-be-measured area by using a preset risk assessment model to obtain risk trend information; and obtaining risk level information of the to-be-measured area according to the risk trend information. The application has high risk monitoring efficiency for power equipment and is convenient for maintenance personnel to perform maintenance.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of device monitoring, in particular to a risk monitoring method and system and a storage medium. BACKGROUND

[0002] In daily life, people's demand for electricity is increasing. In the field of power transmission, the safety of power equipment operation directly affects people's normal use of electricity. With the increasing intensification of global climate change, the frequency and intensity of extreme weather such as typhoons and heavy rains are increasing. Under the action of extreme weather such as strong typhoons and heavy rains, power equipment is prone to cause equipment leakage accidents after being submerged, greatly increasing the risk of electric shock for people, and also causing huge economic losses. Therefore, the importance of power equipment risk monitoring for extreme weather is fully reflected.

[0003] At present, when maintenance personnel need to monitor the risk of multiple setting sites, the maintenance personnel arrive at each power equipment setting site one by one to realize the one-by-one investigation of the risk situation of the power equipment, that is, the maintenance personnel can only know the risk situation of one setting site at a time, which leads to low monitoring efficiency, and the maintenance personnel can only formulate a corresponding maintenance strategy after obtaining the risk situation of the power equipment on site, which leads to inconvenient maintenance. SUMMARY

[0004] Therefore, it is necessary to provide a risk monitoring method, system and storage medium to solve the technical problems of low risk monitoring efficiency of power equipment and inconvenience for maintenance personnel to maintain.

[0005] The present application provides a risk monitoring method, which comprises:

[0006] calculating the monitoring information of the to-be-tested region by using a preset risk assessment model to obtain risk trend information; wherein the to-be-tested region is a region range for setting power equipment, the monitoring information comprises at least one of waterlogging prevention early warning information and electric shock prevention early warning information, and the risk trend information is used to represent the trend of the probability of the to-be-tested region occurring a risk changing with time.

[0007] According to the risk trend information, the risk level information of the to-be-tested region is obtained; wherein the risk level information is used to represent the probability of the to-be-tested region occurring a risk.

[0008] In one embodiment, the step of calculating the monitoring information of the to-be-tested region by using a preset risk assessment model to obtain risk trend information comprises:

[0009] According to the monitoring information, a risk coefficient of the to-be-measured area is obtained by calculation, wherein the risk coefficient is a parameter associated with a probability of a risk occurring in the to-be-measured area.

[0010] According to the risk coefficient, the risk trend information is obtained.

[0011] In one of the embodiments, the step of obtaining the risk trend information by calculating the monitoring information of the to-be-measured area using a preset risk assessment model comprises:

[0012] According to at least two monitoring times and risk coefficients corresponding to the monitoring times respectively, the risk trend information is obtained, wherein each monitoring time is a time when a pre-warning event occurs in the to-be-measured area, or any time after the pre-warning event occurs in the to-be-measured area.

[0013] In one of the embodiments, the normal state of the to-be-measured area is an operating state of the to-be-measured area when the risk coefficient of the to-be-measured area is less than a preset first pre-warning threshold, and the pre-warning state of the to-be-measured area is an operating state of the to-be-measured area when the risk coefficient of the to-be-measured area is greater than or equal to the preset first pre-warning threshold.

[0014] The step of obtaining the risk trend information by calculating the monitoring information of the to-be-measured area using a preset risk assessment model comprises:

[0015] According to the monitoring information of the to-be-measured area at a monitoring starting point, a risk coefficient of the to-be-measured area at the monitoring starting point is obtained, wherein the monitoring starting point is a time node when the to-be-measured area changes from the normal state to the pre-warning state.

[0016] According to the monitoring information of the to-be-measured area at a monitoring ending point, a risk coefficient of the to-be-measured area at the monitoring ending point is obtained, wherein the monitoring ending point is a time node when the to-be-measured area changes from the pre-warning state to the normal state after the monitoring starting point.

[0017] In one of the embodiments, the risk trend information comprises a trend curve of the risk coefficient of the to-be-measured area changing with time and / or an association table of the risk coefficient of the to-be-measured area and time.

[0018] In one of the embodiments, the step of obtaining the risk coefficient of the to-be-measured area by calculating the monitoring information comprises:

[0019] According to the anti-flood pre-warning information, a first coefficient value is obtained by coefficient evaluation calculation; and / or,

[0020] According to the anti-electric shock pre-warning information, a second coefficient value is obtained by coefficient evaluation calculation.

[0021] The risk coefficient is obtained according to the first coefficient value and / or the second coefficient value.

[0022] According to the risk monitoring method, the method further comprises:

[0023] According to a comparison result of the risk coefficient and a preset second warning threshold, the risk level information is obtained.

[0024] A risk monitoring system, the system comprising an information acquisition module and a level evaluation module, wherein:

[0025] The information acquisition module is configured to calculate monitoring information of a to-be-tested region by using a preset risk evaluation model to obtain risk trend information, wherein the to-be-tested region is a region range in which power equipment is arranged, the monitoring information comprises at least one of waterlogging prevention warning information and electric shock prevention warning information, and the risk trend information is used to represent a trend of a probability of the to-be-tested region occurring a risk changing over time.

[0026] The level evaluation module is configured to obtain risk level information of the to-be-tested region according to the risk trend information, wherein the risk level information is used to represent a probability of the to-be-tested region occurring a risk.

[0027] A risk monitoring system comprising a memory and a processor, the memory storing a computer program, and the processor implementing steps of the above risk monitoring method when executing the computer program.

[0028] A computer readable storage medium, which stores a computer program, and the computer program implements steps of the above risk monitoring method when executed by a processor.

[0029] In the risk monitoring method, system and storage medium, the preset risk assessment model is used to calculate the monitoring information of the to-be-measured area to obtain risk trend information, and risk level information of the to-be-measured area is obtained according to the risk trend information. The above method provides conditions for simultaneously monitoring the risks of multiple to-be-measured areas, and the maintenance personnel do not need to go to the site of the to-be-measured area to monitor the risks, thereby effectively improving the efficiency of monitoring the risks of the power equipment. Moreover, the risk level information can represent the probability of the to-be-measured area having a risk, and the risk level information of the to-be-measured area can be used as a basis for the maintenance personnel to judge the operation of the power equipment in the to-be-measured area. The maintenance personnel can determine the probability of the to-be-measured area having a risk according to the risk level information, so as to quickly obtain whether the to-be-measured area has a risk and the severity of the risk. Therefore, the maintenance scheme of the to-be-measured area can be determined in advance before maintenance, specifically, the to-be-measured area with high risk severity is given priority to be maintained, and the to-be-measured area with low risk severity or no risk does not need to be maintained. The maintenance personnel can focus on repairing the to-be-measured area with high risk, which facilitates the maintenance personnel to formulate a maintenance strategy with strong pertinence, and is beneficial to improving the overall efficiency of maintaining multiple to-be-measured areas. BRIEF DESCRIPTION OF DRAWINGS

[0030] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the drawings needed to be used in the embodiments or the prior art description will be briefly introduced. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can be obtained by those skilled in the art without creative effort. Among them:

[0031] Figure 1 It is a flowchart of the risk monitoring method in one embodiment;

[0032] Figure 2 It is a flowchart of the risk trend information acquisition step in one embodiment;

[0033] Figure 3 It is a flowchart of the risk coefficient calculation step in one embodiment;

[0034] Figure 4 It is a flowchart of the start and end steps of the risk trend information acquisition in one embodiment;

[0035] Figure 5 It is a trend curve diagram of the risk coefficient in one embodiment;

[0036] Figure 6 It is a module structure diagram of the risk monitoring system in one embodiment. DETAILED EMBODIMENT

[0037] In order to make the purposes, technical solutions and advantages of the present application clearer, the present application will be further described in detail below with reference to the drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and not intended to limit the present application.

[0038] As shown in Figure 1 In one embodiment, the present application provides a risk monitoring method applied to a risk monitoring system 600 as shown in Figure 6 The risk monitoring method is used to monitor the risk change of a to-be-measured area provided with power equipment.

[0039] It is worth mentioning that the coverage of the to-be-measured area is not limited, and the number of power equipment provided in the same to-be-measured area is also not limited, for example, in some embodiments, the to-be-measured area is a circular monitoring area with a diameter of 20 meters, and the circular monitoring area is provided with at least one power equipment. The specific structure of the power equipment can be determined according to the actual monitored equipment, for example, in some embodiments, the power equipment includes but is not limited to at least one of a power transmission tower, a power distribution device, and a transformer.

[0040] It should be noted that with the continuous intensification of global climate change, the frequency and intensity of extreme weather such as typhoons and heavy rains are increasing. Under the action of strong typhoons, heavy rains and other severe weather, the power equipment is easily flooded, which can easily cause the power equipment to leak electricity, resulting in the to-be-measured area becoming a leakage area, and when people enter the to-be-measured area, electric shock accidents are likely to occur. Therefore, the risk of the to-be-measured area can be determined according to the waterlogging early warning information or the electric shock early warning information of the to-be-measured area.

[0041] The above method comprises the following steps:

[0042] Step 102, calculating the monitoring information of the to-be-measured area by using a preset risk assessment model to obtain risk trend information.

[0043] In step 102, the monitoring information includes at least one of the waterlogging early warning information and the electric shock early warning information of the to-be-measured area.

[0044] It is worth mentioning that the waterlogging early warning information includes but is not limited to waterlogging yellow early warning information, waterlogging orange early warning information and waterlogging red early warning information from low to high in danger level, and the danger level of the waterlogging early warning information is positively correlated with the probability of the risk occurring in the to-be-measured area. The specific acquisition steps of the waterlogging early warning information are as follows:

[0045] The water level information of the to-be-measured area is monitored in real time by the water level monitor arranged in the to-be-measured area, and then, according to a comparison result of the water level information of the to-be-measured area and a preset water level threshold, the waterlogging prevention early warning information is obtained; for example, in some embodiments, the preset water level threshold includes a first water level early warning value, a second water level early warning value and a third water level early warning value, the first water level early warning value < the second water level early warning value < the third water level early warning value, and the waterlogging prevention early warning information includes waterlogging prevention yellow early warning information, waterlogging prevention orange early warning information and waterlogging prevention red early warning information from low to high in danger level, wherein, when the first water level early warning value ≤ the water level information of the to-be-measured area < the second water level early warning value, the waterlogging prevention yellow early warning information is obtained; when the second water level early warning value ≤ the water level information of the to-be-measured area < the third water level early warning value, the waterlogging prevention orange early warning information is obtained; and when the third water level early warning value ≤ the water level information of the to-be-measured area, the waterlogging prevention red early warning information is obtained.

[0046] In addition, the electric shock prevention early warning information includes electric shock danger information and no electric shock information, when the electric shock prevention early warning information is the electric shock danger information, the to-be-measured area must have a risk, and when the electric shock prevention early warning information is the no electric shock danger information, the occurrence probability of the risk of the to-be-measured area can be judged according to other monitoring information (such as the waterlogging prevention early warning information) related to the risk of the to-be-measured area; the obtaining step of the electric shock prevention early warning information is as follows:

[0047] The electric leakage current of the to-be-measured area is monitored in real time by the electric leakage monitor arranged in the to-be-measured area, and then, according to a comparison result of the electric leakage current of the to-be-measured area and a preset safety current threshold, the electric shock prevention early warning information is obtained; for example, in some embodiments, the safety current threshold is set to 10 mA, and the electric shock prevention early warning information includes electric shock danger information and no electric shock danger information, when the electric leakage current of the to-be-measured area < 10 mA, the no electric shock danger information is obtained, and when the electric leakage current of the to-be-measured area ≥ 10 mA, the electric shock danger information is obtained.

[0048] The risk trend information is used to represent the trend of the probability of the risk of the to-be-measured area changing with time, and specifically, in some embodiments, the risk trend information can be expressed by drawing a trend curve graph of the probability of the risk of the to-be-measured area changing with time, or by drawing an association table of the probability of the risk of the to-be-measured area and time.

[0049] In step 104, the risk level information of the to-be-measured area is obtained according to the risk trend information.

[0050] In step 104, the risk level information is used to represent the probability of the risk of the to-be-measured area, and the risk level information can be used as a basis for the maintenance personnel to judge the operation of the power equipment in the to-be-measured area.

[0051] The aforementioned risk monitoring method provides the capability for simultaneous risk monitoring of multiple areas to be monitored, eliminating the need for maintenance personnel to visit the sites of the areas to be monitored and effectively improving the efficiency of risk monitoring of power equipment. Furthermore, maintenance personnel can determine the probability of risk occurrence in the areas to be monitored based on risk level information, quickly obtaining information on whether a risk has occurred and its severity. This allows for the pre-determining of maintenance plans for the areas to be monitored before maintenance begins. Specifically, areas with high risk severity are prioritized for maintenance, while areas with low risk severity or no risk do not require maintenance. This enables maintenance personnel to focus on emergency repairs of high-risk areas, facilitating the development of targeted maintenance strategies based on risk conditions and improving the overall efficiency of maintaining multiple areas to be monitored.

[0052] Furthermore, in some embodiments, in order to facilitate maintenance personnel in locating the area to be tested and the electrical equipment included in each area to be tested, the monitoring information may also include the location information of the area to be tested and the number information of the electrical equipment in the area to be tested. In the above method, in addition to outputting risk level information to maintenance personnel, the location information of the area to be tested and the number information of the electrical equipment in the area to be tested are also output.

[0053] like Figure 2 As shown, in some embodiments, the step of calculating risk trend information by using a preset risk assessment model to analyze the monitoring information of the area to be monitored includes:

[0054] Step 202: Calculate the risk coefficient of the area to be tested based on the monitoring information.

[0055] In step 202, the risk coefficient is a parameter associated with the probability of risk occurring in the area to be tested. For example, in some embodiments, the preset risk assessment model is a pre-set risk assessment score calculation rule, with monitoring information as the parameter basis for the risk assessment score calculation rule, and the calculated risk assessment score as the risk coefficient. The risk assessment score is positively correlated with the probability of risk occurring in the area to be tested. Of course, in other embodiments, the preset risk assessment model is a pre-set probability calculation rule, with monitoring information as the input parameter for the probability calculation rule, and the calculated probability value as the risk coefficient. That is, the risk coefficient is directly the probability value of risk occurring in the area to be tested.

[0056] Step 204: Obtain risk trend information based on the risk coefficient.

[0057] In step 204, the risk trend information is used to represent the numerical change trend of the risk coefficient. The high and low of the risk coefficient can directly express the high and low of the probability of risk occurrence in the to-be-tested region. In some embodiments, the obtained risk trend information can directly represent the change trend in any monitoring period, which can be specifically set according to actual use requirements.

[0058] Through the above steps 202-204, the risk trend information can be displayed to the maintenance personnel in a more intuitive manner, which facilitates the maintenance personnel to make targeted maintenance strategies according to the risk development trend of the to-be-tested region.

[0059] Further, in some embodiments, the step of obtaining the risk coefficient of the to-be-tested region according to the monitoring information includes:

[0060] In step 302, the first coefficient value is obtained by performing coefficient evaluation calculation according to the waterlogging early warning information.

[0061] The waterlogging early warning information includes a plurality of sub waterlogging early warning information. Before step 302, a plurality of preset coefficient values are preset, and the plurality of preset coefficient values are set in one-to-one correspondence with the plurality of sub waterlogging early warning information. In step 302, the preset coefficient value corresponding to the current sub waterlogging early warning information is obtained as the first coefficient value according to any sub waterlogging early warning information. For example, in some embodiments, the waterlogging early warning information includes three sub waterlogging early warning information (including waterlogging yellow early warning information, waterlogging orange early warning information, and waterlogging red early warning information). The preset coefficient values corresponding to the waterlogging yellow early warning information, the waterlogging orange early warning information, and the waterlogging red early warning information are set as 30, 60, and 90, respectively. In step 302, when the waterlogging yellow early warning information is obtained, the first coefficient value is 30. When the waterlogging orange early warning information is obtained, the first coefficient value is 60. When the waterlogging red early warning information is obtained, the first coefficient value is 90.

[0062] In step 304, the second coefficient value is obtained by performing coefficient evaluation calculation according to the electric shock early warning information.

[0063] The electric shock early warning information includes electric shock danger information and no electric shock danger information. Before step 304, two preset coefficient values are preset, one of which is set in correspondence with the electric shock danger information, and the other of which is set in correspondence with the no electric shock danger information. In step 304, the preset coefficient value corresponding to the electric shock danger information or the no electric shock danger information is obtained as the second coefficient value according to the electric shock danger information or the no electric shock danger information. For example, in some embodiments,

[0064] The preset coefficient values corresponding to the electric shock danger information and the non-electric shock danger information are respectively 100 and 0. In step 304, when the electric shock danger information is obtained, the second coefficient value is 100, and when the non-electric shock danger information is obtained, the second coefficient value is 0.

[0065] In step 306, the risk coefficient is calculated according to the first coefficient value and the second coefficient value.

[0066] It should be noted that in other embodiments, the step of obtaining the risk coefficient of the to-be-measured area according to the monitoring information can also be provided with only one of the above steps 302 and 304, and step 306 is adaptively adjusted according to the setting of steps 302 and 304. For example, in some embodiments, only step 302 or step 304 is provided, the first coefficient value is obtained through step 302, or the second coefficient value is obtained through step 304, and in step 306, the first coefficient value or the second coefficient value is directly taken as the risk coefficient.

[0067] Further, in some embodiments, the step of obtaining the risk trend information by using the preset risk assessment model to calculate the monitoring information of the to-be-measured area includes:

[0068] According to at least two monitoring times and the risk coefficients corresponding to the monitoring times respectively, the risk trend information is obtained.

[0069] In the above steps, each monitoring time is the time when the to-be-measured area has a warning event, or any time after the to-be-measured area has a warning event. When drawing the graph of the risk trend information, one monitoring time is one monitoring record node of the risk coefficient. For example, in some embodiments, n monitoring record nodes (i.e. n monitoring times, n≥1) are obtained in time sequence from the time when the to-be-measured area has a warning event, the difference between two adjacent monitoring record nodes is a preset time interval, the first monitoring record node (i.e. the first monitoring time) is the time when the to-be-measured area has a warning event, and the second to n monitoring record nodes are located after the first monitoring record node. The risk coefficients of the to-be-measured area corresponding to the n monitoring record nodes are obtained respectively, and the change trend curve graph of the risk coefficient on the n monitoring record nodes is drawn, or the risk trend information is drawn as the associated table of the risk coefficient and the n monitoring record nodes.

[0070] It should be noted that the time interval is not limited, for example, in some embodiments, the preset time interval is 1 hour.

[0071] The above-mentioned early warning event refers to triggering a waterlogging prevention early warning or an electric shock prevention early warning in the to-be-measured region, and the manner of judging whether the to-be-measured region has triggered an early warning event is not limited, for example, but not limited to the following three manners:

[0072] (1) When the water level information of the to-be-measured region exceeds the first water level early warning value, it is judged that the to-be-measured region triggers a waterlogging prevention early warning, and it is judged that the to-be-measured region has triggered an early warning event;

[0073] (2) When the leakage current of the to-be-measured region exceeds the safety current threshold value, it is judged that the to-be-measured region triggers an electric shock prevention early warning, and it is judged that the to-be-measured region has triggered an early warning event;

[0074] (3) When the risk coefficient of the to-be-measured region exceeds the first early warning threshold value, it is judged that the to-be-measured region has triggered an early warning event, for example, the first early warning threshold value is 30, and when the risk coefficient is greater than 30, it is judged that the to-be-measured region has triggered an early warning event.

[0075] As shown in Figure 4 some embodiments, the normal state of the to-be-measured region is the running state of the to-be-measured region when the risk coefficient thereof is less than the preset first early warning threshold value, and the early warning state of the to-be-measured region is the running state of the to-be-measured region when the risk coefficient thereof is greater than or equal to the preset first early warning threshold value, for example, when the first early warning threshold value is set to 30, the to-be-measured region is in a normal state when the risk coefficient thereof is less than 30, and the to-be-measured region is in an early warning state when the risk coefficient thereof is greater than or equal to 30.

[0076] In some embodiments, since the risk monitoring of the to-be-measured region is continuously performed, in the process of continuous monitoring, the risk trend information can be obtained in a single evaluation manner, for example, one risk trend information corresponding to one monitoring period is obtained in one monitoring period. For the convenience of understanding, the following will be described in detail.

[0077] The step of calculating the monitoring information of the to-be-measured region by using the preset risk evaluation model to obtain the risk trend information, comprising:

[0078] Step 402, obtaining the risk coefficient of the to-be-measured region at the monitoring starting point according to the monitoring information of the to-be-measured region at the monitoring starting point;

[0079] In step 402, the monitoring starting point is the time node when the to-be-measured region is transferred from the normal state to the early warning state, which is the first monitoring record node of the current monitoring period, for example, in some embodiments, when the risk coefficient of the to-be-measured region rises from less than 30 to greater than or equal to 30, the monitoring starting point is triggered, and step 402 is triggered to start obtaining the risk trend information of the current monitoring period.

[0080] At step 404, the risk coefficient of the to-be-tested region at the monitoring termination point is obtained according to the monitoring information of the to-be-tested region at the monitoring termination point.

[0081] The monitoring termination point is a time node at which the to-be-tested region is transferred from the early warning state to the normal state after the monitoring starting point, and is the last monitoring record node of the current monitoring period. For example, in some embodiments, when the risk coefficient of the to-be-tested region decreases from a level greater than or equal to 30 to a level less than 30, the time node is taken as the monitoring termination point, and step 404 is triggered to end the obtaining of the risk trend information of the current monitoring period.

[0082] In some embodiments, at least one intermediate point is further arranged between the monitoring starting point and the monitoring termination point in the same monitoring period. Each intermediate point is a monitoring record node located at a middle position of the monitoring period. The arrangement of the intermediate point can include the following two ways:

[0083] (1) n intermediate points (where n≥2) are obtained in time sequence from the monitoring starting point, where the time interval between two adjacent intermediate points is a preset time interval, and the time interval between the first intermediate point and the monitoring starting point is a preset time interval. The risk coefficient of the to-be-tested region at each intermediate point is obtained, and a risk coefficient change trend curve graph at the monitoring starting point, the intermediate points and the monitoring termination point is drawn, or a risk coefficient correlation table between the monitoring starting point, the intermediate points and the monitoring termination point is drawn as the risk trend information.

[0084] (2) After the monitoring starting point, when the risk coefficient of the to-be-tested region changes at any time node, the time node corresponding to each change of the risk coefficient is recorded as an intermediate point. The risk coefficient of the to-be-tested region at each intermediate point is obtained, and a risk coefficient change trend curve graph at the monitoring starting point, the intermediate points and the monitoring termination point is drawn, or a risk coefficient correlation table between the monitoring starting point, the intermediate points and the monitoring termination point is drawn as the risk trend information. Of course, it is also feasible to record the time node corresponding to each change of the first coefficient value or the second coefficient value as an intermediate point after the monitoring starting point, which is not described here.

[0085] In some embodiments, the risk trend information includes a risk coefficient change trend curve graph of the to-be-tested region over time and / or a risk coefficient correlation table of the to-be-tested region and time. For example, in some embodiments, as shown in FIG. 4, the risk trend information includes a risk coefficient change trend curve graph of the to-be-tested region over time and a risk coefficient correlation table of the to-be-tested region and time. Figure 5As shown, the risk trend information is a trend graph of the risk coefficient changing with time, in which the horizontal coordinate is time and the vertical coordinate is the risk coefficient. Of course, in some embodiments, the risk trend information is a correlation table of the risk coefficient of the to-be-tested region and time, as shown in Table 1 below.

[0086] Table 1

[0087] Time 13h 55min 13h 56min 13h 58min 14h 00min 14h 01min 14h 02min First coefficient value 0 30 60 30 0 0 Second coefficient value 100 100 100 100 100 0 Risk coefficient 100 130 160 130 0 0 Risk level information High risk High risk High risk High risk High risk No risk

[0088] In some embodiments, the above-mentioned risk monitoring method further comprises:

[0089] According to the comparison result of the risk coefficient and the preset second warning threshold, risk level information is obtained.

[0090] In the above steps, the risk level information can be divided into no risk level, low risk level, medium risk level and high risk level from low to high according to the probability of risk occurrence, and the second warning threshold includes a first risk threshold, a second risk threshold and a third risk threshold, wherein:

[0091] When the risk coefficient < the first risk threshold, the risk level information is the no risk level;

[0092] When the first risk threshold ≤ the risk coefficient < the second risk threshold, the risk level information is the low risk level;

[0093] When the second risk threshold ≤ the risk coefficient < the third risk threshold, the risk level information is the medium risk level;

[0094] When the third risk threshold ≤ the risk coefficient, the risk level information is the high risk level.

[0095] It is worth mentioning that the first risk threshold, the second risk threshold and the third risk threshold can be specifically set according to actual application conditions, for example, in some embodiments, the first risk threshold, the second risk threshold and the third risk threshold are 30, 60 and 90 respectively.

[0096] In some embodiments, if the risk level information at any time node changes from the risk level (including the low risk level, the medium risk level and the high risk level) to the no risk level, it proves that the to-be-tested region returns to the normal operation state, at this time, normal information representing that the to-be-tested region is in the normal state can be generated and sent to the terminal system or the background device.

[0097] In some embodiments, if the risk level information of the to-be-tested region is always a risk level (including a low risk level, a medium risk level and a high risk level) within a preset time interval, it is proved that the to-be-tested region is in an early warning state and its operation state is abnormal. At this time, abnormal information for representing that the to-be-tested region is in the early warning state can be generated and sent to a terminal system or a background device.

[0098] It should be understood that, although Figure 1-4 The steps in the flowcharts are shown in sequence according to the arrows, but these steps are not necessarily executed in the order indicated by the arrows. Unless otherwise specified herein, the execution of these steps is not strictly limited in sequence, and these steps can be executed in other orders. Moreover, Figure 1-4 At least part of the steps in the flowcharts can include multiple steps or multiple stages, which are not necessarily executed at the same time, but can be executed at different times, and the execution order of these steps or stages is not necessarily sequential, but can be alternately executed with at least part of other steps or steps or stages in other steps.

[0099] Referring to Figure 6 In one embodiment, the present application provides a risk monitoring system 600 to which the above risk monitoring method can be applied, the risk monitoring system 600 comprising an information acquisition module 610 and a level evaluation module 620 connected with the information acquisition module 610, the information acquisition module 610 being arranged on one side of a to-be-tested region 700. Wherein:

[0100] The information acquisition module 610 is configured to calculate the monitoring information of the to-be-tested region 700 by using a preset risk evaluation model to obtain risk trend information; wherein the to-be-tested region 700 is a region range for arranging power equipment, the monitoring information comprises at least one of flood control warning information and electric shock prevention warning information, and the risk trend information is used to represent the trend of the probability of the to-be-tested region 700 occurring a risk changing with time.

[0101] The level evaluation module 620 is configured to obtain risk level information of the to-be-tested region 700 according to the risk trend information; wherein the risk level information is used to represent the probability of the to-be-tested region 700 occurring a risk.

[0102] In one embodiment, the information acquisition module 610 is further configured to calculate the monitoring information to obtain a risk coefficient of the to-be-tested region 700, wherein the risk coefficient is a parameter associated with the probability of the to-be-tested region 700 occurring a risk; and obtain the risk trend information according to the risk coefficient.

[0103] In one of the embodiments, the information obtaining module 610 is further configured to obtain risk trend information according to at least two monitoring times and risk coefficients corresponding to the monitoring times respectively; each of the monitoring times is a time when the pre-warning event occurs in the to-be-measured region 700 or any time after the pre-warning event occurs in the to-be-measured region 700.

[0104] In one of the embodiments, the normal state of the to-be-measured region 700 is an operation state of the to-be-measured region 700 when the risk coefficient of the to-be-measured region 700 is less than a preset first pre-warning threshold, and the pre-warning state of the to-be-measured region 700 is an operation state of the to-be-measured region 700 when the risk coefficient of the to-be-measured region 700 is greater than or equal to the preset first pre-warning threshold.

[0105] The information obtaining module 610 is further configured to obtain the risk coefficient of the to-be-measured region 700 at a monitoring starting point according to the monitoring information of the to-be-measured region 700 at the monitoring starting point, wherein the monitoring starting point is a time node when the to-be-measured region 700 transits from the normal state to the pre-warning state; and obtain the risk coefficient of the to-be-measured region 700 at a monitoring ending point according to the monitoring information of the to-be-measured region 700 at the monitoring ending point, wherein the monitoring ending point is a time node when the to-be-measured region 700 transits from the pre-warning state to the normal state after the monitoring starting point.

[0106] In one of the embodiments, the information obtaining module 610 is further configured to obtain a first coefficient value according to the coefficient evaluation calculation based on the waterlogging pre-warning information, and / or obtain a second coefficient value according to the coefficient evaluation calculation based on the electric shock pre-warning information; and obtain the risk coefficient according to the first coefficient value and / or the second coefficient value.

[0107] In one of the embodiments, the level evaluating module 620 is further configured to obtain the risk level information according to a comparison result of the risk coefficient and a preset second pre-warning threshold.

[0108] Those skilled in the art can understand that Figure 6 The skilled in the art can understand that

[0109] A risk monitoring system includes a memory and a processor, the memory stores a computer program, and the processor implements the steps of the risk monitoring method described above when executing the computer program.

[0110] A computer readable storage medium stores a computer program, and the computer program implements the steps of the risk monitoring method described above when executed by a processor.

[0111] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a non-volatile computer readable storage medium, and when executed, can include the processes of the above-mentioned embodiment methods. Any reference to memory, storage, database or other medium used in the embodiments provided in the present 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 or optical memory. Volatile memory can include random access memory (RAM) or external cache memory. As an illustration but not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM).

[0112] Any combination of the technical features of the above embodiments can be made. In order to make the description simple, all possible combinations of the technical features in the above embodiments are not described, but as long as the combination of the technical features does not exist, it should be considered as the scope of the present application.

[0113] The above embodiments only express several embodiments of the present application, and the description is more specific and detailed, but it should not be understood as a limitation on the scope of the patent. It should be pointed out that for those skilled in the art, without departing from the concept of the present application, a number of modifications and improvements can be made, which are all within the scope of the present application. Therefore, the scope of the patent of the present application should be subject to the appended claims.

Claims

1. A risk monitoring method, characterized by, The method comprises: calculating the monitoring information of the to-be-tested region by using a preset risk assessment model to obtain risk trend information; wherein the to-be-tested region is a region range for setting power equipment, the monitoring information comprises at least one of waterlogging prevention early warning information and electric shock prevention early warning information, and the risk trend information is used to represent the trend of the probability of the to-be-tested region occurring a risk changing over time; obtaining risk level information of the to-be-tested region according to the risk trend information; wherein the risk level information is used to represent the probability of the to-be-tested region occurring a risk; the step of calculating the monitoring information of the to-be-tested region by using a preset risk assessment model to obtain risk trend information comprises: calculating the risk coefficient of the to-be-tested region according to the monitoring information; wherein the risk coefficient is a parameter associated with the probability of the to-be-tested region occurring a risk; obtaining the risk trend information according to the risk coefficient; the normal state of the to-be-tested region is the operating state of the to-be-tested region when its risk coefficient is less than a preset first early warning threshold, and the early warning state of the to-be-tested region is the operating state of the to-be-tested region when its risk coefficient is greater than or equal to the preset first early warning threshold; the step of calculating the monitoring information of the to-be-tested region by using a preset risk assessment model to obtain risk trend information comprises: obtaining the risk coefficient of the to-be-tested region at a monitoring starting point according to the monitoring information of the to-be-tested region at the monitoring starting point; wherein the monitoring starting point is a time node when the to-be-tested region changes from the normal state to the early warning state; obtaining the risk coefficient of the to-be-tested region at a monitoring ending point according to the monitoring information of the to-be-tested region at the monitoring ending point; wherein the monitoring ending point is a time node when the to-be-tested region changes from the early warning state to the normal state after the monitoring starting point.

2. The risk monitoring method according to claim 1, characterized in that, the step of calculating the monitoring information of the to-be-tested region by using a preset risk assessment model to obtain risk trend information comprises: obtaining the risk trend information according to at least two monitoring times and risk coefficients corresponding to the monitoring times respectively; wherein each monitoring time is a time when the to-be-tested region occurs an early warning event, or any time after the to-be-tested region occurs an early warning event.

3. The risk monitoring method of claim 1, wherein, The risk trend information comprises a trend curve diagram of the risk coefficient of the to-be-tested region changing over time and / or an association table of the risk coefficient of the to-be-tested region and time.

4. The risk monitoring method of claim 1, wherein, the step of calculating the risk coefficient of the to-be-tested region according to the monitoring information comprises: calculating a first coefficient value according to the waterlogging prevention early warning information; and / or, calculating a second coefficient value according to the electric shock prevention early warning information; calculating the risk coefficient according to the first coefficient value and / or the second coefficient value.

5. The risk monitoring method of claim 1, wherein, The method further comprises: obtaining the risk level information according to the comparison result of the risk coefficient and a preset second early warning threshold.

6. A risk monitoring system characterized in that, The system comprises an information acquisition module and a level assessment module, wherein: The information acquisition module is configured to calculate monitoring information of a to-be-tested region by using a preset risk assessment model to obtain risk trend information, wherein the to-be-tested region is a region range in which power equipment is arranged, the monitoring information comprises at least one of waterlogging prevention early warning information and electric shock prevention early warning information, and the risk trend information is used to represent a trend of a probability of the to-be-tested region occurring a risk changing over time. The grade assessment module is configured to obtain risk grade information of the to-be-tested region according to the risk trend information, wherein the risk grade information is used to represent a probability of the to-be-tested region occurring a risk. The step of calculating the monitoring information of the to-be-tested region by using the preset risk assessment model to obtain the risk trend information comprises the following steps: calculating the to-be-tested region according to the monitoring information to obtain a risk coefficient of the to-be-tested region, wherein the risk coefficient is a parameter associated with a probability of the to-be-tested region occurring a risk; obtaining the risk trend information according to the risk coefficient. The normal state of the to-be-tested region is an operating state of the to-be-tested region when a risk coefficient of the to-be-tested region is less than a preset first early warning threshold, and the early warning state of the to-be-tested region is an operating state of the to-be-tested region when the risk coefficient of the to-be-tested region is greater than or equal to the preset first early warning threshold. The step of calculating the monitoring information of the to-be-tested region by using the preset risk assessment model to obtain the risk trend information comprises the following steps: obtaining a risk coefficient of the to-be-tested region at a monitoring starting point according to monitoring information of the to-be-tested region at the monitoring starting point, wherein the monitoring starting point is a time node at which the to-be-tested region is switched from the normal state to the early warning state; obtaining a risk coefficient of the to-be-tested region at a monitoring ending point according to monitoring information of the to-be-tested region at the monitoring ending point, wherein the monitoring ending point is a time node at which the to-be-tested region is switched from the early warning state to the normal state after the monitoring starting point.

7. A risk monitoring system comprising a memory and a processor, the memory storing a computer program, characterized in that, The processor executes the computer program to implement the steps of the risk monitoring method in any one of claims 1 to 5.

8. A computer-readable storage medium having stored thereon a computer program, characterized in that, The computer program is executed by the processor to implement the steps of the risk monitoring method in any one of claims 1 to 5.

Images