Litter humidity monitoring method, electronic equipment, storage medium and product

Abstract

The invention relates to the technical field of humidity monitoring, in particular to a litter humidity monitoring method, electronic equipment, a storage medium and a product, and the method comprises the steps: obtaining a monitoring image, and recognizing litter feature information of the monitoring image; determining a hidden danger area and hidden danger area feature information from the monitoring image based on the litter feature information and preset hidden danger feature information, and determining a hidden danger area litter humidity value based on the hidden danger area feature information; obtaining influence feature information corresponding to the hidden danger area in the first preset time period, and determining a predicted hidden danger influence value corresponding to the hidden danger area in the first preset time period based on the influence feature information; and updating the hidden danger area litter humidity value based on the predicted hidden danger influence value to obtain a target hidden danger area litter humidity value, and when the target hidden danger area litter humidity value is lower than a preset early warning threshold value, generating prompt information based on the hidden danger area and the target hidden danger area humidity value. The litter humidity monitoring device is convenient for improving the accuracy of monitoring the litter humidity.

Description

Technical Field

[0001] The present application relates to the technical field of humidity monitoring, and in particular to a litter humidity monitoring method, electronic equipment, storage medium and product. Background Art

[0002] Forest fire refers to forest fire behavior that loses human control, spreads and expands freely in the woodland, causing certain harm and losses to the forest, forest ecosystem and humans. Litter, as an important cover of the forest surface, has a direct impact on the forest fire risk due to its composition and characteristics. Litter with higher humidity is relatively non-flammable, while dry litter is extremely easy to ignite. By monitoring the changes in the humidity of the litter, its flammability can be assessed in real time, thereby predicting the risk of fire.

[0003] In the relevant technology, sensors are generally used directly to monitor the humidity of litter. However, due to the insufficient accuracy and stability of some sensors, they may not be able to accurately reflect the humidity of the litter. In addition, the forest environment is complex and changeable, including changes in various factors such as temperature, humidity, and light. Some sensor monitoring equipment may not be able to fully adapt to these changes, which may affect the accuracy of the monitoring results, and may further reduce the accuracy of fire warnings based on litter humidity. Summary of the invention

[0004] In order to improve the accuracy of monitoring litter humidity and thus improve the accuracy of fire warning, the present application provides a litter humidity monitoring method, electronic device, storage medium and product.

[0005] In the first aspect, the present application provides a method for monitoring the humidity of litter, which adopts the following technical solution: A method for monitoring humidity of litter, comprising: Acquire a monitoring image, and identify litter feature information in the monitoring image, wherein the litter feature information includes litter color features, litter texture features, and litter shape features; Based on the litter feature information and the preset hidden danger feature information, determine the hidden danger area and the hidden danger area feature information from the monitoring image, and determine the litter humidity value of the hidden danger area based on the hidden danger area feature information, and the litter feature information corresponding to the hidden danger area meets the preset hidden danger feature information; Acquire impact characteristic information corresponding to the hidden danger area in a first preset time period, and determine a predicted hidden danger impact value corresponding to the hidden danger area in the first preset time period based on the impact characteristic information, wherein the impact characteristic information includes soil moisture information and animal activity information; The litter humidity value of the hidden danger area is updated based on the predicted hidden danger impact value to obtain the litter humidity value of the target hidden danger area. When the litter humidity value of the target hidden danger area is lower than a preset warning threshold, prompt information is generated based on the hidden danger area and the humidity value of the target hidden danger area.

[0006] By adopting the above technical scheme, by performing feature recognition on the monitoring images, it is convenient to timely discover the hidden dangers in the monitoring area. When the hidden danger area is determined, it is not directly warned, but the humidity change of the litter in the hidden danger area in the future is analyzed and predicted, so as to reduce the false alarm or missed alarm. Among them, since the soil moisture and animal activities in the hidden danger area will directly affect the humidity change of the litter, therefore, by comprehensively considering the humidity change of the litter in the hidden danger area through the soil moisture information and animal activity information of the hidden danger area in the future, it is convenient to improve the adaptability between the prediction result and the actual situation, so as to improve the accuracy of monitoring and warning of the humidity of the litter.

[0007] In a possible implementation, determining the predicted hidden danger impact value corresponding to the hidden danger area in the first preset time period based on the impact feature information includes: Identifying the soil moisture information, and determining soil moisture change information of the hidden danger area within the first preset time period; Identifying the animal behavior type contained in the animal activity information, and determining the morphological change information of the hidden danger fallen objects in the hidden danger area within the first preset time period based on the animal behavior type; Acquire a regional simulation model corresponding to the hidden danger area, and import the characteristic information of the hidden danger area, the soil moisture change information, and the morphological change information into the regional simulation model for simulation, so as to obtain a litter simulation change image corresponding to the first preset time period; The simulated litter feature information contained in the simulated litter change image is identified, and the predicted hidden danger impact value corresponding to the hidden danger area in the first preset time period is determined based on the simulated litter feature information.

[0008] By adopting the above technical scheme, since soil moisture and animal behavior type can directly affect the moisture of litter, therefore, by importing the soil moisture change information corresponding to the hidden danger area within the first preset time period and the morphological change information caused by the animal behavior type into the regional simulation model for simulation, it is convenient to improve the adaptability between the change of litter moisture in the simulated change image of litter and the actual change, thereby facilitating improving the accuracy of determining the predicted hidden danger impact value corresponding to the hidden danger area.

[0009] In one possible implementation, the method further includes: Acquire weather characteristic information corresponding to the hidden danger area in the first preset time period, and determine the weather change type corresponding to the hidden danger area in the first preset time period based on the weather characteristic information, wherein the weather change type includes a stable type and an unstable type, and the weather characteristic information includes a wind level and a rainfall amount; When the weather change type is unstable, determining a weather impact level based on the weather characteristic information; Determine a first litter humidity impact value caused by the weather characteristic information on the hidden danger area within the first preset time period based on the morphological change information of the hidden danger litter within the first preset time period and the weather impact level; The predicted hidden danger impact value corresponding to the hidden danger area in the first preset time period is optimized based on the first litter humidity impact value.

[0010] By adopting the above technical scheme, by analyzing the weather characteristic information of the hidden danger area within the preset time period, it is convenient to analyze the weather changes faced by the hidden danger area within the preset time period. When it is determined that the weather change type is unstable, the degree of humidity impact that the hidden danger litter may be affected by analyzing the morphological change information and humidity level can be analyzed, and the predicted hidden danger impact value can be optimized based on the degree of impact, so as to improve the accuracy of the predicted hidden danger impact value.

[0011] In a possible implementation, when the impact feature information corresponding to the hidden danger area does not include animal activity information, the method further includes: Determine the impact area of ​​the hidden danger area and the impact interval between the hidden danger area and the impact area from the monitoring image based on the weather characteristic information, and judge whether there is corresponding animal activity information affecting the impact area; If yes, determining the morphological change information of the litter in the affected area within the first preset time period according to the affected animal activity information; Based on the impact morphological change information and the impact interval, determining a second litter humidity impact value caused by the impact area on the hidden danger area within the first preset time period; The predicted hidden danger impact value corresponding to the hidden danger area in the first preset time period is optimized based on the second litter humidity impact value.

[0012] By adopting the above technical scheme, since animal activities may affect the morphology of litter and thus the humidity of litter, it is necessary to analyze the animal activities in the hidden danger area. However, since the routes and ranges of animal activities may change, when there is no animal activity in the hidden danger area, the possible impacts on the hidden danger area can be indirectly analyzed by analyzing the information of animal activities in the affected area, so as to improve the accuracy of determining the predicted hidden danger impact value.

[0013] In a possible implementation, determining the second litter humidity impact value caused by the impact area on the hidden danger area within the first preset time period based on the impact morphological change information and the impact interval includes: Determine an impact range wave diagram based on the impact morphology change information, wherein the impact range wave diagram includes multiple range wave dimensions, and different range wave dimensions correspond to different impact rates; The target impact rate of the impact area on the hidden danger area within the first preset time period is determined from the impact range wave diagram based on the impact interval, and the second litter humidity impact value is determined based on the target impact rate.

[0014] By adopting the above technical scheme, different influencing morphological change information has different impacts on the hidden danger area at different impact intervals. Therefore, different impact range wave maps are divided according to the influencing morphological change information, and then the corresponding impact degree is determined from the range wave map based on the impact interval between the hidden danger area and the impact area. That is, the impact of the impact area on the hidden danger area is refined through the impact interval, so as to improve the accuracy of determining the impact value of litter humidity.

[0015] In one possible implementation, it also includes: If no response information is detected within the second preset time period, determining the litter spraying amount based on the humidity difference between the litter humidity value in the target hidden danger area and the preset warning threshold; A spraying time is determined based on the second preset time period, and spraying instruction information is generated based on the spraying time and the litter spraying amount.

[0016] By adopting the above technical solution, if the relevant staff does not respond to the abnormal situation for a long period of time, appropriate spraying operations can be performed on the potential danger area based on the humidity difference to increase the humidity of the fallen leaves, thereby facilitating reducing the probability of fire hazards.

[0017] In a second aspect, the present application provides an electronic device, which adopts the following technical solution: An electronic device, comprising: at least one processor; Memory; At least one application, wherein the at least one application is stored in a memory and configured to be executed by at least one processor, and the at least one application is configured to: execute the above-mentioned litter moisture monitoring method.

[0018] In a third aspect, the present application provides a computer-readable storage medium, which adopts the following technical solution: A computer-readable storage medium includes: a computer program that can be loaded by a processor and execute the above-mentioned litter moisture monitoring method.

[0019] In a fourth aspect, the present application provides a computer program product, which adopts the following technical solution: A computer program product includes a computer program, and when the computer program is executed by a processor, the above-mentioned litter moisture monitoring method is implemented.

[0020] In summary, the present application includes at least one of the following beneficial technical effects: By performing feature recognition on monitoring images, hidden dangers in the monitoring area can be discovered in time. When a hidden danger area is identified, an early warning is not issued directly, but an analysis and prediction of the changes in the humidity of the litter in the hidden danger area in the future is made, so as to reduce false alarms or missed alarms. Among them, since the soil moisture and animal activities in the hidden danger area will directly affect the changes in the humidity of the litter, the changes in the humidity of the litter in the hidden danger area are comprehensively considered through the soil moisture information and animal activity information of the hidden danger area in the future, so as to improve the adaptability between the prediction results and the actual situation, thereby improving the accuracy of monitoring and early warning of the humidity of the litter.

[0021] Since animal activities may affect the morphology of litter and thus the humidity of litter, it is necessary to analyze the animal activities in the hidden danger area. However, since the routes and ranges of animal activities may change, when there is no animal activity in the hidden danger area, the possible impacts on the hidden danger area can be analyzed indirectly by analyzing the information of animal activities in the affected area, so as to improve the accuracy of determining the predicted hidden danger impact value. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] Figure 1 It is a flow chart of a method for monitoring humidity of litter in an embodiment of the present application; Figure 2 This is a schematic diagram of an optimization process for predicting the impact value of hidden dangers in an embodiment of the present application; Figure 3 It is a structural schematic diagram of an electronic device in an embodiment of the present application. DETAILED DESCRIPTION

[0023] The following is combined with Figures 1 to 3 This application is described in further detail.

[0024] After reading this specification, those skilled in the art may make non-creative modifications to this embodiment as needed, but such modifications are protected by patent law as long as they are within the scope of the claims of this application.

[0025] In order to make the purpose, technical solution and advantages of the embodiments of the present application clearer, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0026] It should be noted that in the optional embodiments of the present application, the object information and other related data involved, when the embodiments in the present application are applied to specific products or technologies, need to obtain the permission or consent of the object, and the collection, use and processing of the relevant data need to comply with the relevant laws, regulations and standards of the relevant countries and regions. In other words, if the embodiments of the present application involve data related to the object, it needs to be obtained with the authorization and consent of the object, the authorization and consent of the relevant departments, and in compliance with the relevant laws, regulations and standards of the country and region. If personal information is involved in the embodiments, the acquisition of all personal information needs to obtain the consent of the individual. If sensitive information is involved, the separate consent of the information subject needs to be obtained. The embodiments also need to be implemented with the authorization and consent of the object.

[0027] Specifically, the embodiment of the present application provides a method for monitoring the humidity of fallen leaves, which is performed by an electronic device, which can be a server or a terminal device, wherein the server can be an independent physical server, or a server cluster or distributed system composed of multiple physical servers, or a cloud server providing cloud computing services. The terminal device can be a smart phone, a tablet computer, a laptop computer, a desktop computer, etc., but is not limited thereto. The terminal device and the server can be directly or indirectly connected via wired or wireless communication, which is not limited in the embodiment of the present application.

[0028] refer to Figure 1 , Figure 1 : is a flow chart of a method for monitoring humidity of litter in an embodiment of the present application, the method comprising steps S110 to S140, wherein: Step S110: Acquire a monitoring image, and identify litter feature information in the monitoring image, where the litter feature information includes litter color features, litter texture features, and litter shape features.

[0029] Specifically, the monitoring image is an environmental image corresponding to the area to be monitored, which can be collected by an image acquisition device set in the environment to be monitored and uploaded to an electronic device, wherein the area to be monitored is an area where litter humidity monitoring is required, and can be a forest, grassland, or other area containing litter. Litter is organic matter that naturally falls off from vegetation in a natural ecosystem, such as fallen leaves, fallen fruits, and bark fragments. The litter in the area to be monitored is often distributed continuously. If the humidity of the litter is low, once the litter is ignited somewhere, the flames generated by the combustion will spread rapidly along the continuous litter. However, the probability of burning litter with high humidity is low, and even after being ignited, the spread of the fire will be suppressed. Therefore, the humidity of the litter is a key indicator for assessing the fire risk in the area to be monitored.

[0030] Because the color of litter changes with the change of its own humidity, for example, when the humidity of litter is high, water will make the pigment in litter more saturated, so the color presented is usually darker, correspondingly, when the humidity of litter is low, the concentration of pigment in litter decreases, so the color presented is generally lighter. The texture of litter will also change due to the change of its own humidity. When the humidity of litter is high, water will expand the cell structure, so the texture of litter is usually soft and changeable when the humidity is high, and when the humidity of litter is low, the cell structure will shrink due to the loss of water, therefore, the texture of litter may become hard and rough, thus easy to break. In addition, the shape change of litter can also indirectly reflect humidity, for example, litter with high humidity tends to keep its original shape, not easy to deform or break, but litter with low humidity may appear curled, contracted or fragmented. Therefore, in the embodiment of the present application, the humidity of litter is preliminarily analyzed and confirmed by analyzing the color characteristics of litter, the texture characteristics of litter and the shape characteristics of litter.

[0031] The preset feature recognition algorithm can be directly used to identify the litter feature information contained in the monitoring image, wherein a color histogram or a color matrix can be used to quantify the color features of the litter in the monitoring image, a grayscale co-occurrence matrix can be used to describe the texture features of the litter in the monitoring image, and an edge detection algorithm can be used to identify the shape features of the litter in the monitoring image. The specific preset feature recognition algorithm is not specifically limited in the embodiments of the present application, as long as the litter feature information can be determined from the monitoring image.

[0032] Step S120: Based on the litter characteristic information and the preset hidden danger characteristic information, determine the hidden danger area and the hidden danger area characteristic information from the monitoring image, and determine the litter humidity value of the hidden danger area based on the hidden danger area characteristic information, and the litter characteristic information corresponding to the hidden danger area meets the preset hidden danger characteristic information.

[0033] Specifically, the preset hidden danger characteristic information is the hidden danger judgment condition. For example, the preset hidden danger characteristic information can be that the color depth of the litter is lower than the preset depth value, the texture of the litter is the preset hidden danger texture, and the shape of the litter is the preset hidden danger shape. The specific preset depth, preset hidden danger texture and preset hidden danger shape are not specifically limited in the embodiment of the present application, and can be determined by relevant staff based on historical experimental data and uploaded to the electronic device. When determining the hidden danger area, the monitoring image can be divided into regions first. Correspondingly, the litter characteristic information corresponding to the monitoring image is also divided into multiple divided regions, and the regional characteristic information corresponding to each divided region is compared with the preset hidden danger characteristic information to determine whether the regional characteristic information meets the hidden danger judgment condition, and the divided region that meets the hidden danger judgment condition is determined as the hidden danger area. The number of hidden danger areas can be 0, 1, or multiple, and the specific number is not specifically limited in the embodiment of the present application.

[0034] Different hidden danger area characteristics correspond to different hidden danger area litter humidity values, wherein the first humidity value corresponding to the litter color feature, the second humidity value corresponding to the litter texture feature, and the third humidity value corresponding to the litter texture feature can be determined respectively first, and then the first humidity value, the second humidity value, and the third humidity value are summarized to obtain the hidden danger area litter humidity value. Specifically, the first humidity value corresponding to the color depth of the litter can be determined according to a preset color mapping relationship, and the preset color mapping relationship includes the first humidity values ​​corresponding to different color depth intervals; the second humidity value corresponding to the texture of the litter can be determined according to a preset texture mapping relationship, and the preset texture mapping relationship includes the second humidity values ​​corresponding to different texture features; the third humidity value corresponding to the shape of the litter can be determined according to a preset shape mapping relationship, and the preset shape mapping relationship includes the third humidity values ​​corresponding to different shape features, wherein the specific contents of the preset color mapping relationship, the preset texture mapping relationship, and the preset shape mapping relationship are not specifically limited in the embodiments of the present application, and can be determined by relevant staff based on historical experimental data and uploaded to the electronic device.

[0035] Step S130: Obtain impact characteristic information corresponding to the hidden danger area in the first preset time period, and determine the predicted hidden danger impact value corresponding to the hidden danger area in the first preset time period based on the impact characteristic information, wherein the impact characteristic information includes soil moisture information and animal activity information.

[0036] Specifically, the humidity value of litter in the hidden danger area determined based on the characteristic information of the hidden danger area is the initial value, that is, the humidity value determined by analyzing the color, texture, shape and other characteristics of the litter itself. However, the humidity of the litter is also easily affected by the surrounding environment, such as the soil in direct or indirect contact with the litter, and the activity information of animals in direct or indirect contact with the litter. Among them, due to the water exchange between the soil and the litter, the water in the soil can rise to the litter through capillary action, and the water in the litter can also penetrate into the soil. Therefore, the change of soil humidity will directly affect the humidity of the litter. When animals gnaw, nest or move above the litter, it will cause the litter to become loose or piled up to a certain extent, thereby affecting the result and humidity of the litter. The first preset time period is a period of time after the current moment. The duration corresponding to the first preset time period can be 1 hour or 2 hours. The specific duration is not specifically limited in the embodiment of the present application. By comprehensively considering the soil moisture information and animal activity information of the hidden danger area in the future period, the humidity change of the fallen leaves in the hidden danger area is convenient to improve the adaptability between the prediction result and the actual situation. Among them, the soil moisture information can be collected by the soil moisture collection device set in the monitored area and uploaded to the electronic device. The animal activity information can be obtained by the image collection device set in the monitored area after collecting the active image and performing motion recognition on the active image, and can also be collected by the tracking device set in the monitored area and uploaded to the electronic device.

[0037] Further, determining the predicted hidden danger impact value corresponding to the hidden danger area in the first preset time period based on the impact characteristic information may specifically include: Identify soil moisture information and determine soil moisture change information of the hidden danger area within a first preset time period; identify animal behavior types contained in animal activity information, and determine morphological change information of hidden danger litter in the hidden danger area within the first preset time period based on the animal behavior types; obtain a regional simulation model corresponding to the hidden danger area, and import the hidden danger area characteristic information, soil moisture change information and morphological change information into the regional simulation model for simulation to obtain a simulated litter change image corresponding to the first preset time period; identify simulated litter characteristic information contained in the simulated litter change image, and determine the predicted hidden danger impact value of the hidden danger area corresponding to the first preset time period based on the simulated litter characteristic information.

[0038] Specifically, the soil change information is the soil moisture value corresponding to each moment in the first preset time period. The animal behavior types contained in the animal activity information include loose behavior types and stacking behavior types, wherein loose behavior refers to the behavior that causes the original litter structure to change loosely after animal activity, such as insects gnawing on the litter or turning over fallen food; stacking behavior type refers to the behavior that causes the original litter structure to change in stacking after animal activity, such as birds or insects piling up litter when building nests or looking for shelter. The morphological change information is the litter morphological edge value corresponding to each moment in the first preset time period.

[0039] When obtaining the regional simulation model corresponding to the hidden danger area, it can be intercepted from the preset simulation model based on the regional characteristics corresponding to the hidden danger area, wherein the preset simulation model has consistent basic biological data with the area to be monitored, and the changes in the moisture content of litter in the area to be monitored can be simulated based on the preset simulation model. The method of determining the regional simulation model is not specifically limited in the embodiment of the present application. The simulated change image of litter corresponding to the first preset time period is the simulated change of the moisture content of litter under the influence of soil moisture change information and morphological change information.

[0040] The simulated litter characteristic information includes simulated litter color characteristics, simulated litter texture characteristics and simulated litter shape characteristics. The method of identifying the simulated litter characteristic information from the litter simulation change image can refer to the method of identifying the litter characteristic information from the monitoring image in the above-mentioned embodiment. The method of determining the predicted hidden danger impact value corresponding to the hidden danger area in the first preset time period based on the simulated litter characteristic information can refer to the method of determining the predicted hidden danger impact value corresponding to the hidden danger area in the first preset time period based on the impact characteristic information in the above-mentioned embodiment, which will not be elaborated here.

[0041] Step S140: Update the litter humidity value in the hidden danger area based on the predicted hidden danger impact value to obtain the litter humidity value in the target hidden danger area. When the litter humidity value in the target hidden danger area is lower than the preset warning threshold, generate prompt information based on the humidity values ​​of the hidden danger area and the target hidden danger area.

[0042] Specifically, the target hidden danger area litter humidity value is the optimization result of the hidden danger area litter humidity value, which may be higher than the hidden danger area litter humidity value, or it may be lower than the hidden danger area litter humidity value, but the updated or optimized target hidden danger area litter humidity value has higher accuracy. When the target hidden danger area litter humidity value is lower than the preset warning threshold, a prompt message needs to be generated to remind the relevant responsible personnel to check the hidden danger abnormalities in the hidden danger area and eliminate the abnormalities, wherein the specific preset warning threshold is not specifically limited in the embodiment of the present application and can be set by the technical personnel.

[0043] For the embodiments of the present application, by performing feature recognition on the monitoring images, it is convenient to timely discover the hidden dangers in the monitoring area. When the hidden danger area is determined, it is not directly warned, but the humidity changes of the litter in the hidden danger area in the future are analyzed and predicted, so as to reduce the false alarms or missed alarms. Among them, since the soil moisture and animal activities in the hidden danger area will directly affect the humidity changes of the litter, therefore, by comprehensively considering the humidity changes of the litter in the hidden danger area through the soil moisture information and animal activity information of the hidden danger area in the future, it is convenient to improve the adaptability between the prediction result and the actual situation, so as to improve the accuracy of monitoring and warning of the humidity of the litter.

[0044] Furthermore, in order to improve the accuracy of predicting the impact value of hidden dangers, the method provided in the embodiment of the present application also includes: Obtain weather characteristic information corresponding to the hidden danger area in the first preset time period, and based on the weather characteristic information, determine the weather change type corresponding to the hidden danger area in the first preset time period, the weather change type includes a stable type and an unstable type, and the weather characteristic information includes wind level and rainfall; when the weather change type is unstable, determine the weather impact level based on the weather characteristic information; based on the morphological change information of the hidden danger litter in the first preset time period and the weather impact level, determine the first litter humidity impact value caused by the weather characteristic information on the hidden danger area in the first preset time period; based on the first litter humidity impact value, optimize the predicted hidden danger impact value corresponding to the hidden danger area in the first preset time period.

[0045] Specifically, the weather changes in the potential danger area in the future period can be obtained through satellite remote sensing data or radar data, where weather characteristic information includes but is not limited to wind direction and rainfall. By analyzing the weather characteristic information corresponding to the first time period, it is convenient to judge whether the weather changes in the first preset time period are stable. When the weather changes are relatively stable, the impact of weather changes on the humidity of litter can be ignored; when the weather changes are unstable, it is necessary to further analyze the impact of weather characteristic information on the humidity of litter.

[0046] Among them, when determining the type of weather change based on weather characteristic information, the weather characteristic information corresponding to the first preset time period can be compared with the preset judgment condition. When the weather characteristic information meets the preset judgment condition, the weather change type can be determined to be a stable type; when the weather characteristic information does not meet the preset judgment condition, the weather change type can be determined to be an unstable type, wherein the preset judgment condition may include that the wind level is not higher than the preset level, and the rainfall is not higher than the preset rainfall. The specific preset level and preset rainfall are not specifically limited in the embodiment of the present application. When a wind level higher than the preset level or a rainfall higher than the preset rainfall appears in the weather characteristic information, it is necessary to further analyze the impact of different wind levels and rainfall on the humidity of litter. Different wind levels and different rainfall have different levels of weather impact on the humidity of litter in the hidden danger area.

[0047] Since the change in the morphology of litter will affect the contact area between the litter and the surrounding air and moisture, the same wind level and rainfall will have different effects on the humidity of the litter when facing litter of different forms, that is, when determining the first litter humidity impact value, it is necessary to consider both the weather impact level and the morphological change information. Different wind levels and rainfalls correspond to different weather impact levels, which can be determined according to a preset level mapping relationship. The preset level mapping relationship includes weather impact levels corresponding to different wind level and rainfall combinations. The specific content of the mapping relationship is not specifically limited in the embodiments of the present application. When determining the first litter humidity impact value, the humidity impact value corresponding to each moment can be determined according to the litter morphology edge value corresponding to each moment in the first preset time period in the morphology change information, and finally the first litter humidity impact value is determined by summarizing the humidity impact values ​​corresponding to each moment, wherein the humidity impact values ​​corresponding to different weather impact levels at different litter morphology edge values ​​can be determined according to a preset control impact mapping relationship, wherein the preset control impact mapping relationship includes humidity impact values ​​corresponding to different weather impact levels when facing different litter morphology edge values, which can be determined by relevant staff based on historical experimental data and uploaded to the electronic device, and the specific content is not specifically limited in the embodiments of the present application.

[0048] The predicted hidden danger impact value corresponding to the hidden danger area in the first preset time period is optimized based on the first litter humidity impact value, that is, the predicted hidden danger impact value is updated based on the first litter humidity impact value, thereby improving the accuracy of the predicted hidden danger impact value.

[0049] Furthermore, when the impact feature information corresponding to the hidden danger area does not include animal activity information, the method provided in the embodiment of the present application further includes: Based on the weather characteristic information, the impact area of ​​the hidden danger area and the impact interval between the hidden danger area and the impact area are determined from the monitoring image, and it is judged whether there is corresponding impact animal activity information in the impact area; if so, the impact morphological change information of the litter in the impact area within the first preset time period is determined according to the impact animal activity information; based on the impact morphological change information and the impact interval, the second litter humidity impact value caused by the impact area on the hidden danger area within the first preset time period is determined; based on the second litter humidity impact value, the predicted hidden danger impact value corresponding to the hidden danger area in the first preset time period is optimized.

[0050] Specifically, since animal activities may affect the morphology of litter and thus the humidity of litter, it is necessary to analyze the animal activities in the hidden danger area. However, since the animal activity routes and ranges may change, when there is no animal activity in the hidden danger area, the possible impacts in the hidden danger area can be analyzed indirectly by analyzing the animal activity information affecting the affected area. When determining the impact area corresponding to the hidden danger area, the wind direction contained in the weather characteristic information can be identified first. The impact area is an area where the air flow range can include the hidden danger area under the action of the wind direction. For example, there are two existing areas, which are area a and area b from west to east, and the wind direction is from west to east. At this time, area a can be determined as the impact area of ​​area b. When determining the impact interval between the impact area and the hidden danger area, the hidden danger center point of the hidden danger area and the impact center point of the impact area can be determined from the monitoring image first, and then the impact interval is determined by calculating the pixel distance between the hidden danger center point and the impact center point. The method of determining the impact interval is not specifically limited in the embodiment of the present application.

[0051] The method of determining the corresponding morphological change information of the affected area based on the affected animal activity information can refer to the method of determining the corresponding morphological change information based on the animal activity information in the above embodiment, which will not be described in detail here. When there are animal activities in the affected area, the animal activities may cause the humidity of the litter in the affected area to change, thereby causing the corresponding air humidity in the affected area to change. The change in the air humidity in the affected area under the influence of weather characteristic information may cause the air humidity in the hidden danger area to change, thereby causing the humidity of the litter in the hidden danger area to change.

[0052] Different impact morphological change information leads to different degrees of impact of the impact area on the litter humidity in the hidden danger area. Further, based on the impact morphological change information and the impact interval, the specific process of determining the second litter humidity impact value caused by the impact area on the hidden danger area within the first preset time period includes step S1 and step S2, such as Figure 2 As shown, where: Step S1: determining an impact range wave diagram based on the impact morphology change information, wherein the impact range wave diagram includes multiple range wave dimensions, and different range wave dimensions correspond to different impact rates.

[0053] Specifically, the weather characteristic information affecting the affected area in the first preset time period is obtained, and then the target impact value corresponding to the affected area is determined based on the weather characteristic information and the morphological change information. The specific method of determining the impact value can refer to the method of determining the first litter humidity impact value caused by the weather characteristic information on the hidden danger area in the first preset time period based on the morphological change information and weather impact level of the hidden danger litter in the first preset time period in the above-mentioned embodiment, which will not be elaborated here. After determining the impact rate, it is necessary to divide the impact interval into multiple range wave dimensions. The interval difference between different range wave dimensions can be consistent or show an increasing trend, which is not specifically limited in the embodiments of the present application. The impact range wave diagram contains multiple range wave dimensions and the impact rate corresponding to each range wave dimension. For example, the impact rate corresponding to range wave dimension a is 100%, the impact rate corresponding to range wave dimension b is 80%, and the impact rate corresponding to range wave dimension c is 60%.

[0054] Step S2: determining a target impact rate of the impact area on the hidden danger area within a first preset time period from the impact range wave diagram based on the impact interval, and determining a second litter humidity impact value based on the target impact rate.

[0055] Specifically, after determining the impact range wave map, it is necessary to first determine the corresponding target impact rate from the impact range wave map according to the impact interval, and then determine the corresponding second litter humidity impact value according to the target impact rate and the target impact value. The impact of the impact area on the hidden danger area is refined through the impact interval, so as to improve the accuracy of determining the second litter humidity impact value. Then, the second litter humidity impact value is used to optimize the predicted hidden danger impact value corresponding to the hidden danger area in the first preset time period, so as to further improve the accuracy of determining the predicted hidden danger impact value.

[0056] Furthermore, in order to reduce the probability of fire hazards, the method provided in the embodiment of the present application also includes: If no response information is detected within the second preset time period, the amount of litter spraying is determined based on the humidity difference between the litter humidity value in the target hazard area and the preset warning threshold; the spraying time is determined based on the second preset time period, and the spraying instruction information is generated based on the spraying time and the litter spraying amount.

[0057] Specifically, the second preset time period is a period of time after the prompt information is generated. The specific duration is not specifically limited in the embodiment of the present application. The generated prompt information indicates that the humidity of litter in the hidden danger area is low and the probability of fire hazard is high. The generated prompt information at this time is used to prompt relevant staff to increase the humidity of litter in the hidden danger area, or to check the ignition point in the hidden danger area to prevent fire in the hidden danger area. If the relevant staff fails to respond in time, the hidden danger area can be sprayed by controlling the sprinkler device set in the monitored area.

[0058] When spraying the hidden danger area, the specific spraying amount can be set or the spraying operation can be performed randomly, as long as the humidity of the litter in the spraying area can be increased. The litter spraying amount can be determined based on the humidity difference between the litter humidity value of the target hidden danger area and the preset warning threshold. That is, after the spraying is completed, the litter humidity value of the target hidden danger area is not lower than the preset warning threshold. The specific preset warning threshold is not specifically limited in the embodiments of the present application.

[0059] An electronic device is provided in an embodiment of the present application, such as Figure 3 As shown, Figure 3 The electronic device 300 shown includes: a processor 301 and a memory 303. The processor 301 and the memory 303 are connected, such as through a bus 302. Optionally, the electronic device 300 may also include a transceiver 304. It should be noted that in actual applications, the transceiver 304 is not limited to one, and the structure of the electronic device 300 does not constitute a limitation on the embodiments of the present application.

[0060] The processor 301 may be a CPU (Central Processing Unit), a general-purpose processor, a DSP (Digital Signal Processor), an ASIC (Application Specific Integrated Circuit), an FPGA (Field Programmable Gate Array) or other programmable logic devices, transistor logic devices, hardware components or any combination thereof. It may implement or execute various exemplary logic blocks, modules and circuits described in conjunction with the disclosure of this application. The processor 301 may also be a combination that implements computing functions, such as a combination of one or more microprocessors, a combination of a DSP and a microprocessor, etc.

[0061] The bus 302 may include a path to transmit information between the above components. The bus 302 may be a PCI (Peripheral Component Interconnect) bus or an EISA (Extended Industry Standard Architecture) bus. The bus 302 may be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Figure 3 The fact that only one line is used in the diagram does not mean that there is only one bus or only one type of bus.

[0062] The memory 303 may be a ROM (Read Only Memory) or other types of static storage devices that can store static information and instructions, a RAM (Random Access Memory) or other types of dynamic storage devices that can store information and instructions, or an EEPROM (Electrically Erasable Programmable Read Only Memory), a CD-ROM (Compact Disc Read Only Memory) or other optical disk storage, optical disk storage (including compressed optical disk, laser disk, optical disk, digital versatile disk, Blu-ray disk, etc.), a magnetic disk storage medium or other magnetic storage device, or any other medium that can be used to carry or store the desired program code in the form of instructions or data structures and can be accessed by a computer, but is not limited thereto.

[0063] The memory 303 is used to store the application code for executing the solution of the present application, and the execution is controlled by the processor 301. The processor 301 is used to execute the application code stored in the memory 303 to implement the contents shown in the above method embodiment.

[0064] The electronic devices include, but are not limited to, mobile terminals such as mobile phones, laptop computers, digital broadcast receivers, PDAs (personal digital assistants), PADs (tablet computers), PMPs (portable multimedia players), vehicle-mounted terminals (such as vehicle-mounted navigation terminals), and fixed terminals such as digital TVs, desktop computers, etc. It can also be a server, etc. Figure 3 The electronic device shown is merely an example and should not bring any limitation to the functions and scope of use of the embodiments of the present application.

[0065] An embodiment of the present application provides a computer-readable storage medium, on which a computer program is stored. When the computer-readable storage medium is run on a computer, the computer can execute the corresponding content in the aforementioned method embodiment.

[0066] The embodiment of the present application provides a computer program product, which includes a computer program, and the computer program, when executed by a processor, implements the method in any of the above embodiments. Compared with the related art, the embodiment of the present application performs feature recognition on the monitoring image to facilitate timely discovery of hidden dangers in the monitoring area. When the hidden danger area is determined, it does not directly issue an early warning prompt, but analyzes and predicts the humidity changes of the litter in the hidden danger area in the future period of time, so as to reduce false alarms or missed alarms. Among them, since the soil moisture and animal activities in the hidden danger area will directly affect the humidity changes of the litter, therefore, by comprehensively considering the humidity changes of the litter in the hidden danger area through the soil moisture information and animal activity information of the hidden danger area in the future period of time, it is convenient to improve the adaptability between the prediction result and the actual situation, so as to facilitate the improvement of the accuracy of monitoring and early warning of the humidity of the litter.

[0067] It should be understood that, although the steps in the flowchart of the accompanying drawings are displayed in sequence as indicated by the arrows, these steps are not necessarily executed in sequence in the order indicated by the arrows. Unless otherwise specified herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least a part of the steps in the flowchart of the accompanying drawings may include multiple sub-steps or multiple stages, and these sub-steps or stages are not necessarily executed at the same time, but can be executed at different times, and their execution order is not necessarily sequential, but can be executed in turn or alternately with other steps or at least a part of the sub-steps or stages of other steps.

[0068] The above description is only a partial implementation method of the present application. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles of the present application. These improvements and modifications should also be regarded as the scope of protection of the present application.

Claims

1. A method for monitoring litter humidity, characterized in that: include: Acquire a monitoring image, and identify litter feature information in the monitoring image, wherein the litter feature information includes litter color features, litter texture features, and litter shape features; Based on the litter feature information and the preset hidden danger feature information, determine the hidden danger area and the hidden danger area feature information from the monitoring image, and determine the litter humidity value of the hidden danger area based on the hidden danger area feature information, and the litter feature information corresponding to the hidden danger area meets the preset hidden danger feature information; Acquire impact characteristic information corresponding to the hidden danger area in a first preset time period, and determine a predicted hidden danger impact value corresponding to the hidden danger area in the first preset time period based on the impact characteristic information, wherein the impact characteristic information includes soil moisture information and animal activity information; The litter humidity value of the hidden danger area is updated based on the predicted hidden danger impact value to obtain the litter humidity value of the target hidden danger area. When the litter humidity value of the target hidden danger area is lower than a preset warning threshold, prompt information is generated based on the hidden danger area and the humidity value of the target hidden danger area.

2. A method for monitoring litter humidity according to claim 1, characterized in that: The determining, based on the impact characteristic information, a predicted hidden danger impact value corresponding to the hidden danger area in the first preset time period includes: Identifying the soil moisture information, and determining soil moisture change information of the hidden danger area within the first preset time period; Identifying the animal behavior type contained in the animal activity information, and determining the morphological change information of the hidden danger fallen objects in the hidden danger area within the first preset time period based on the animal behavior type; Acquire a regional simulation model corresponding to the hidden danger area, and import the characteristic information of the hidden danger area, the soil moisture change information, and the morphological change information into the regional simulation model for simulation, so as to obtain a litter simulation change image corresponding to the first preset time period; The simulated litter feature information contained in the simulated litter change image is identified, and the predicted hidden danger impact value corresponding to the hidden danger area in the first preset time period is determined based on the simulated litter feature information.

3. A litter humidity monitoring method according to claim 2, characterized in that: Also includes: Acquire weather characteristic information corresponding to the hidden danger area in the first preset time period, and determine the weather change type corresponding to the hidden danger area in the first preset time period based on the weather characteristic information, wherein the weather change type includes a stable type and an unstable type, and the weather characteristic information includes a wind level and a rainfall amount; When the weather change type is unstable, determining a weather impact level based on the weather characteristic information; Determine a first litter humidity impact value caused by the weather characteristic information on the hidden danger area within the first preset time period based on the morphological change information of the hidden danger litter within the first preset time period and the weather impact level; The predicted hidden danger impact value corresponding to the hidden danger area in the first preset time period is optimized based on the first litter humidity impact value.

4. A litter humidity monitoring method according to claim 3, characterized in that: When the impact feature information corresponding to the potential danger area does not include animal activity information, it also includes: Determine the impact area of ​​the hidden danger area and the impact interval between the hidden danger area and the impact area from the monitoring image based on the weather characteristic information, and judge whether there is corresponding animal activity information affecting the impact area; If so, determining the morphological change information of the litter in the affected area within the first preset time period according to the affected animal activity information; Based on the impact morphological change information and the impact interval, determining a second litter humidity impact value caused by the impact area on the hidden danger area within the first preset time period; The predicted hidden danger impact value corresponding to the hidden danger area in the first preset time period is optimized based on the second litter humidity impact value.

5. A method for monitoring litter humidity according to claim 4, characterized in that: The determining, based on the impact morphological change information and the impact interval, a second litter humidity impact value caused by the impact area on the hidden danger area within the first preset time period includes: Determine an impact range wave diagram based on the impact form change information, wherein the impact range wave diagram includes multiple range wave dimensions, and different range wave dimensions correspond to different impact rates; The target impact rate of the impact area on the hidden danger area within the first preset time period is determined from the impact range wave diagram based on the impact interval, and the second litter humidity impact value is determined based on the target impact rate.

6. A method for monitoring litter humidity according to claim 1, characterized in that: Also includes: If no response information is detected within the second preset time period, determining the litter spraying amount based on the humidity difference between the litter humidity value in the target hidden danger area and the preset warning threshold; A spraying time is determined based on the second preset time period, and spraying instruction information is generated based on the spraying time and the litter spraying amount.

7. An electronic device, characterized in that: The electronic device includes: at least one processor; Memory; At least one application, wherein the at least one application is stored in a memory and configured to be executed by at least one processor, and the at least one application is configured to: execute a litter moisture monitoring method according to any one of claims 1-6.

8. A computer-readable storage medium, characterized in that: include: A computer program is stored which can be loaded by a processor and execute a method for monitoring the moisture content of litter as described in any one of claims 1 to 6.

9. A computer program product, characterized in that It comprises a computer program, which, when executed by a processor, implements the steps of a litter moisture monitoring method according to any one of claims 1 to 6.

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