Hydraulic engineering risk assessment method and device for identifying termitarium nest age based on image
By automatically collecting termite images using image recognition methods and calculating the age and size of termite nests, the problem of low efficiency, insufficient accuracy, and high cost in dike inspection and detection has been solved. This has enabled efficient and accurate risk assessment of termite nests and reduced monitoring costs.
Patent Information
- Application Number
- CN202511447780.9
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-10-11
- Publication Date
- 2026-03-03
AI Technical Summary
Existing technologies for inspecting and testing dams suffer from problems such as high destructiveness, low efficiency, poor timeliness, insufficient accuracy, and high cost, making it difficult to achieve efficient, comprehensive, and accurate termite nest detection.
Using an image recognition-based method, termite images are automatically collected, and worker ant body length data is extracted using image recognition algorithms. Combined with a colony size and nest age estimation model, a comprehensive risk score is calculated and the risk level is determined, issuing corresponding early warning information and patrol strategies.
It enables efficient, all-weather termite nest detection, provides objective and quantitative assessment standards, avoids physical damage to dam structures, reduces risk lag, and lowers monitoring costs.
Smart Images
Figure CN121598128A_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of electronic digital data processing technology, and in particular to a method, apparatus, equipment and medium for risk assessment of water conservancy projects based on image recognition of termite nest age. Background Technology
[0002] Earthen dams constitute a significant proportion of water conservancy projects. Termite nests can severely damage the structure of earthen dams, leading to seepage, piping, and other dangerous situations, posing a major threat to dam safety and even causing dam collapse.
[0003] Water conservancy projects such as dikes are usually large in scale, and termite breeding activities result in multiple nests. Therefore, in addition to monitoring the nests that have been discovered and repaired, it is also necessary to continuously patrol and inspect the dikes to prevent new termite nests from going undetected and causing the damage to spread.
[0004] In related technologies, traditional methods for inspecting and testing dikes mainly rely on manual inspection, digging test pits, or using ground-penetrating radar, which have the following drawbacks: 1. Destructive: Excavating test pits can damage the structural integrity of the dam; 2. Low efficiency: Manual patrols have limited coverage and are affected by terrain, weather, and other factors, making it difficult to achieve a comprehensive and efficient census; 3. Poor timeliness: The above methods all require periodic checks, which introduces a certain risk of delay. 4. Insufficient accuracy: The assessment of the extent of termite nest damage relies heavily on the personal experience of inspectors and lacks objective and quantitative evaluation standards. It is highly subjective and can easily lead to misjudgment or omission.
[0005] 5. High cost: It relies on a large number of people to carry out repetitive inspections, and the purchase and maintenance costs of advanced physical detection equipment are high, resulting in high long-term monitoring costs. Summary of the Invention
[0006] This invention provides a method, device, equipment, and medium for risk assessment of water conservancy projects based on image recognition of termite nest age, solving the problem of how to efficiently inspect and detect dikes.
[0007] To achieve the above objectives, this application adopts the following technical solution: Firstly, a method for risk assessment of water conservancy projects based on image recognition of termite nest age is provided, including: The device automatically collects images of termites based on a pre-set trapping and monitoring system; The image is processed using an image recognition algorithm to identify worker ants and extract the body length data X of the worker ants; Input the body length data X into a preset colony size estimation model to obtain the termite colony size data Y; Input the population size data Y into the preset nest age estimation model to obtain the nest age data Age of the termite nest; Based on the nest age data Age, the population size data Y, and the geographical location information of the monitoring point where the image acquisition device is located, the comprehensive risk score RS is calculated through the preset water conservancy project risk assessment model, and the risk level is determined. Based on the determined risk level, corresponding early warning information and patrol strategies will be issued.
[0008] Secondly, a water conservancy project risk assessment device based on image recognition of termite nest age is provided, comprising: The image acquisition module is used to automatically acquire images of termites based on a preset trapping and monitoring device; The worker ant body length acquisition module is used to process the image using an image recognition algorithm, identify worker ants, and extract the body length data X of the worker ants. The colony size estimation module is used to input the body length data X into a preset colony size estimation model to obtain the termite colony size data Y; The nest age estimation module is used to input the population size data Y into a preset nest age estimation model to obtain the nest age data Age of the termite nest; The water conservancy project risk assessment module is used to calculate the comprehensive risk score RS and determine the risk level based on the nest age data Age, the population size data Y, and the geographical location information of the monitoring point where the image acquisition device is located, through a preset water conservancy project risk assessment model. The early warning module is used to issue corresponding early warning information and patrol strategies based on the determined risk level.
[0009] Thirdly, an electronic device is provided, comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the computer program, when executed by the processor, implements the steps of the water conservancy project risk assessment method based on image recognition of termite nest age as described in the first aspect.
[0010] Fourthly, a readable storage medium is provided, on which a program or instructions are stored, which, when executed by a processor, implement the steps of the water conservancy project risk assessment method based on image recognition of termite nest age as described in the first aspect.
[0011] The water conservancy project risk assessment method based on image recognition of termite nest age of the present invention has the following beneficial effects: This application utilizes a waterproof, high-definition image acquisition device to automatically collect images of termite worker ants at water conservancy facilities. After image enhancement and segmentation, the worker ant body length is accurately measured using a worker ant body length recognition algorithm, and the colony size is estimated using an exponential model. Nest age is estimated based on the mapping relationship between colony size and nest age. After obtaining nest age and colony size, a comprehensive risk level assessment is conducted using a risk assessment model, combined with the geographical location information of the monitoring points. This provides an objective and quantitative assessment standard for precise prevention and control in water conservancy projects, enabling managers to implement tiered responses and optimize resource allocation for efficient inspection and detection. This application also completely avoids the physical damage to the main structure of dams caused by traditional methods such as excavation and drilling, eliminating the risk of structural damage introduced by the monitoring operation itself. The system can be deployed at key points along the water conservancy project, achieving efficient and large-scale real-time monitoring. This application enables continuous, 24 / 7 automatic monitoring, quickly identifying early, small-scale termite activity, avoiding delayed risk assessment, and significantly reducing the possibility of termite infestations on water conservancy facilities.
[0012] The device, electronic equipment, and readable storage medium corresponding to the water conservancy project risk assessment method based on image recognition of termite nest age can achieve the same technical effect, and will not be described in detail here to avoid duplication. Attached Figure Description
[0013] Figure 1 A schematic flowchart illustrating a method for risk assessment of water conservancy projects based on image recognition of termite nest age, provided for an embodiment of this application; Figure 2 A schematic diagram of the structure of a water conservancy project risk assessment device based on image recognition of termite nest age provided in this application embodiment; Figure 3 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application. Detailed Implementation
[0014] To further illustrate the technical means and effects adopted by the present invention to achieve its intended purpose, the technical solutions in the embodiments of this application are clearly described. Obviously, the described embodiments are only some embodiments of this application, not all embodiments. Based on the embodiments in this application, all other embodiments obtained by those skilled in the art are within the scope of protection of this application.
[0015] The terms "first," "second," etc., used in the specification and claims of this application are used to distinguish similar objects and not to describe a specific order or sequence. It should be understood that such terms can be used interchangeably where appropriate so that embodiments of this application can be implemented in orders other than those illustrated or described herein, and the objects distinguished by "first," "second," etc., are generally of the same class and the number of objects is not limited; for example, a first object can be one or more. Furthermore, in the specification, "and / or" indicates at least one of the connected objects, and the character " / " generally indicates that the preceding and following objects are in an "or" relationship.
[0016] The steps described in the specification and the flowcharts in the accompanying drawings of this invention are not necessarily strictly executed according to the step numbers; the execution order of the method steps can be changed. Furthermore, certain steps can be omitted, multiple steps can be combined into one step, and / or one step can be broken down into multiple steps.
[0017] This specification provides a method for risk assessment of water conservancy projects based on image recognition of termite nest age, and also relates to a device for risk assessment of water conservancy projects based on image recognition of termite nest age, a computer device, and a computer-readable storage medium. The following describes each item in detail with reference to the accompanying drawings and preferred embodiments.
[0018] Please see Figure 1 This application provides a method for risk assessment of water conservancy projects based on image recognition of termite nest age, such as... Figure 1 As shown, it includes: Step S1: Automatically collect images of termites based on a preset trapping and monitoring device.
[0019] In this step, the trapping and monitoring device includes a trapping section and a monitoring section. The trapping section is used to lure and confine termites active near the device to a set range; the monitoring section is used to automatically collect images of the trapped termites.
[0020] Step S2: The image is processed using an image recognition algorithm to identify worker ants and extract the body length data X of the worker ants.
[0021] Step S3: Input the body length data X into the preset population size estimation model to obtain the population size data Y of termites.
[0022] Furthermore, the population size estimation model is configured to perform the following calculations: Where a and b are model parameters, and e is the natural constant, approximately equal to 2.71828. This forms the basis of the natural exponential function.
[0023] Furthermore, for the black-winged subterranean termite species, the population size estimation model is configured to perform the following calculations: ( ) in, It is a statistic used to measure how well a linear regression model fits the data.
[0024] Step S4: Input the population size data Y into the preset nest age estimation model to obtain the nest age data Age of the termite nest.
[0025] Furthermore, the nest age estimation model is configured to perform the following calculations: Where c and d are two constants.
[0026] In practice, it is necessary to track and observe multiple termite nests with known establishment times (or estimate nest age using methods such as molecular biology), measure their colony size, and then perform linear regression fitting to obtain the values of parameters c and d. It is worth noting that the determination of parameters c and d depends on the accuracy of the colony size estimation model. This application requires first estimating the Y value of multiple nests using the colony size estimation model, and then fitting this model with the actual age of these nests.
[0027] Step S5: Based on the nest age data Age, population size data Y, and the geographical location information of the monitoring point where the image acquisition device is located, calculate the comprehensive risk score RS through the preset water conservancy project risk assessment model, and determine the risk level.
[0028] Furthermore, the step of calculating a comprehensive risk score RS based on the nest age data Age, the population size data Y, and the geographical location information of the monitoring point where the image acquisition device is located, using a preset water conservancy project risk assessment model, and determining the risk level based on the RS value, includes: Step S51: The nest age data Age, population size data Y, and geographical location information are scored according to preset rules.
[0029] Specifically, the rules include: Different scores are assigned based on the age of the nest; different scores are assigned based on the size and number of the population; and different scores are assigned based on the importance of the geographical location of the unit to which it belongs in the water conservancy facilities.
[0030] For example, the scoring criteria are as follows: Nest age: Age < 3 years (1 point), 3 ≤ Age < 6 years (2 points), 6 ≤ Age < 10 years (3 points), Age ≥ 10 years (4 points); Population size (Y): Y < 2000 (1 point), 2000 ≤ Y < 10000 (2 points), 10000 ≤ Y < 50000 (3 points), Y ≥ 50000 (4 points); Location: Canals and ancillary facilities (1 point), back slope (2 points), front slope (3 points), core wall (4 points).
[0031] Step S52, configure the water conservancy project risk assessment model to perform the following calculations: in, Score based on nest age; Score based on group size; The geographical location score is represented by w1, w2, and w3, which are weighting coefficients. Preferably, w1 = 0.4, w2 = 0.4, and w3 = 0.2.
[0032] In some possible implementations, the weighting coefficients are configured to be dynamically adjusted based on historical monitoring data, meteorological data, and seasonal characteristic data. Accuracy is improved by integrating external data such as meteorological and seasonal data.
[0033] Step S53: Configure the water conservancy project risk assessment model to determine the risk level based on the RS value; Specifically, the risk level and the corresponding RS value are: Low risk: 1.0 ≤ RS < 2.5; Medium risk: 2.5 ≤ RS < 4.0; High risk: 4.0 ≤ RS < 5.5; Severe risk: RS ≥ 5.5.
[0034] In some possible implementations, the correspondence between the risk level and the RS value is configured to be dynamically adjusted based on historical monitoring data, meteorological data, and seasonal characteristic data. Accuracy is improved by integrating external data such as meteorological and seasonal data.
[0035] Step S6: Issue corresponding early warning information and patrol strategies based on the determined risk level.
[0036] Specifically: Low risk: 1.0 ≤ RS < 2.5, triggering routine monitoring and early warning; annual review; Medium risk: 2.5 ≤ RS < 4.0, triggering enhanced monitoring and early warning; quarterly review; High risk: 4.0 ≤ RS < 5.5, triggering key monitoring and early warning; monthly review and on-site verification; Severe risk: RS ≥ 5.5, triggering an immediate warning; special handling and engineering reinforcement.
[0037] This application embodiment automatically collects images of termite worker ants at water conservancy facilities using a waterproof high-definition image acquisition device. After image enhancement and segmentation, the worker ant body length (X) is accurately measured using a worker ant body length recognition algorithm, and the colony size (Y) is estimated using an exponential model. The nest age is estimated based on the mapping relationship between colony size and nest age. After obtaining the nest age (Age) and colony size (Y), a comprehensive risk level assessment is conducted using a risk assessment model, combined with the geographical location information of the monitoring point. This provides an objective and quantitative assessment standard for precise prevention and control of water conservancy projects, allowing managers to conduct graded responses and optimize resource allocation. This application also completely avoids the physical damage to the main structure of the dam caused by traditional excavation and drilling, eliminating the risk of structural damage introduced by the monitoring operation itself. The system can be deployed at key points along the water conservancy project, achieving efficient and large-scale real-time monitoring. This application enables continuous automatic monitoring around the clock, quickly identifying early and small-scale termite infestations, avoiding risk lag, and greatly reducing the possibility of termite infestations on water conservancy facilities.
[0038] See Figure 2 Corresponding to the above-described embodiment of the water conservancy project risk assessment method based on image recognition of termite nest age, this application embodiment provides a water conservancy project risk assessment device based on image recognition of termite nest age, comprising: The image acquisition module is used to automatically acquire images of termites based on a preset trapping and monitoring device; The worker ant body length acquisition module is used to process the image using an image recognition algorithm, identify worker ants, and extract the body length data X of the worker ants. The colony size estimation module is used to input the body length data X into a preset colony size estimation model to obtain the termite colony size data Y; The nest age estimation module is used to input the population size data Y into a preset nest age estimation model to obtain the nest age data Age of the termite nest; The water conservancy project risk assessment module is used to calculate the comprehensive risk score RS and determine the risk level based on the nest age data Age, the population size data Y, and the geographical location information of the monitoring point where the image acquisition device is located, through a preset water conservancy project risk assessment model. The early warning module is used to issue corresponding early warning information and patrol strategies based on the determined risk level.
[0039] Furthermore, the population size estimation model is configured to perform the following calculations: Where a and b are model parameters, and e is a natural constant, approximately equal to 2.71828.
[0040] Furthermore, for the black-winged subterranean termite species, the population size estimation model is configured to perform the following calculations: ( ) in, It is a statistic used to measure how well a linear regression model fits the data.
[0041] Furthermore, the nest age estimation model is configured to perform the following calculations: Where c and d are two constants.
[0042] Furthermore, the water conservancy project risk assessment module is specifically used for: The nest age data Age, population size data Y, and geographical location information are scored according to preset rules; Configure the water conservancy project risk assessment model to perform the following calculations: in, Score based on nest age; Score based on group size; The score represents the geographical location; w1, w2, and w3 are the weighting coefficients. The risk assessment model for the water conservancy project is configured to determine the risk level based on the RS value.
[0043] Furthermore, the rules include: Different scores are assigned based on the age of the nest; different scores are assigned based on the size and number of the population; and different scores are assigned based on the importance of the geographical location of the unit to which it belongs in the water conservancy facilities.
[0044] Furthermore, the weighting coefficients are configured to be dynamically adjusted based on historical monitoring data, meteorological data, and seasonal characteristic data; The correspondence between the risk level and the RS value is configured to be dynamically adjusted based on historical monitoring data, meteorological data, and seasonal characteristic data.
[0045] The above-mentioned water conservancy project risk assessment device based on image recognition of termite nest age implements the steps and processes of the above-mentioned water conservancy project risk assessment method based on image recognition of termite nest age, and can achieve the same technical effect. To avoid repetition, it will not be described again here.
[0046] See Figure 3Corresponding to the above-described embodiment of the water conservancy project risk assessment method based on image recognition of termite nest age, this application embodiment provides an electronic device, which includes: a memory, a processor, and a computer program stored in the memory and executable on the processor. When the computer program is executed by the processor, it implements the steps and processes of the above-described embodiment of the water conservancy project risk assessment method based on image recognition of termite nest age, and can achieve the same technical effect. To avoid repetition, it will not be described again here.
[0047] The memory 1009 can be used to store software programs and various data. The memory 1009 may primarily include a first storage area for storing programs or instructions and a second storage area for storing data. The first storage area may store the operating system, application programs or instructions required for at least one function (such as sound playback function, image playback function, etc.). Furthermore, the memory 1009 may include volatile memory or non-volatile memory, or both. The non-volatile memory may be read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), or flash memory. Volatile memory can be random access memory (RAM), static random access memory (SRAM), dynamic random access memory (DRAM), synchronous dynamic random access memory (SDRAM), double data rate synchronous dynamic random access memory (DDRSDRAM), enhanced synchronous dynamic random access memory (ESDRAM), synchronous linked dynamic random access memory (SLDRAM), and direct memory bus RAM (DRRAM). The memory 1009 in the embodiments of this application includes, but is not limited to, these and any other suitable types of memory.
[0048] The processor 1010 may include one or more processing units; optionally, the processor 1010 integrates an application processor and a modem processor, wherein the application processor mainly handles operations involving the operating system, user interface, and applications, and the modem processor mainly handles wireless communication signals, such as a baseband processor. It is understood that the aforementioned modem processor may also not be integrated into the processor 1010.
[0049] Corresponding to the above-described embodiment of the water conservancy project risk assessment method based on image recognition of termite nest age, this application embodiment also provides a readable storage medium storing a program or instructions. When the program or instructions are executed by a processor, they implement the steps and processes of the above-described embodiment of the water conservancy project risk assessment method based on image recognition of termite nest age, and achieve the same technical effect. To avoid repetition, they will not be described again here.
[0050] The processor is the processor in the electronic device described in the above embodiments of this application. The readable storage medium includes a computer-readable storage medium, such as a computer read-only memory (ROM), random access memory (RAM), a magnetic disk, or an optical disk.
[0051] It should be noted that, in this document, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes that element. Furthermore, it should be noted that the scope of the methods and apparatuses in the embodiments of this application is not limited to performing functions in the order shown or discussed, but may also include performing functions substantially simultaneously or in the reverse order, depending on the functions involved. For example, the described methods may be performed in a different order than described, and various steps may be added, omitted, or combined. Additionally, features described with reference to certain examples may be combined in other examples.
[0052] Through the above description of the embodiments, those skilled in the art can clearly understand that the methods of the above embodiments can be implemented by means of software plus necessary general-purpose hardware platforms. Of course, they can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, can be embodied in the form of a computer software product. This computer software product is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk) and includes several instructions to cause a terminal (which may be a mobile phone, computer, server, or network device, etc.) to execute the methods described in the various embodiments of this application.
[0053] It is understood that the embodiments of this application have been described above in conjunction with the accompanying drawings. However, this application is not limited to the specific embodiments described above. The specific embodiments described above are merely illustrative and not restrictive. As those skilled in the art will know, various changes or equivalent substitutions can be made to these features and embodiments without departing from the spirit and scope of the invention. Furthermore, those skilled in the art, under the guidance or instruction of this application, can modify these features and embodiments to adapt to specific situations and materials without departing from the spirit and scope of the invention. Therefore, this invention is not limited to the specific embodiments disclosed herein, and all embodiments falling within the scope of the claims of this application are within the protection scope of this invention.
Claims
1. A method for risk assessment of water conservancy projects based on image recognition of termite nest age, characterized in that, include: The device automatically collects images of termites based on a pre-set trapping and monitoring system; The image is processed using an image recognition algorithm to identify worker ants and extract the body length data X of the worker ants; Input the body length data X into a preset colony size estimation model to obtain the termite colony size data Y; Input the population size data Y into the preset nest age estimation model to obtain the nest age data Age of the termite nest; Based on the nest age data Age, the population size data Y, and the geographical location information of the monitoring point where the image acquisition device is located, the comprehensive risk score RS is calculated through the preset water conservancy project risk assessment model, and the risk level is determined. Based on the determined risk level, corresponding early warning information and patrol strategies will be issued.
2. The water conservancy project risk assessment method based on image recognition of termite nest age according to claim 1, characterized in that, The population size estimation model is configured to perform the following calculations: Where a and b are model parameters, and e is a natural constant, approximately equal to 2.71828.
3. The water conservancy project risk assessment method based on image recognition of termite nest age according to claim 2, characterized in that, For the black-winged subterranean termite species, the population size estimation model is configured to perform the following calculations: ( ) in, It is a statistic used to measure how well a linear regression model fits the data.
4. The water conservancy project risk assessment method based on image recognition of termite nest age according to claim 1, characterized in that, The nest age estimation model is configured to perform the following calculations: Where c and d are two constants.
5. The water conservancy project risk assessment method based on image recognition of termite nest age according to claim 1, characterized in that, Based on the nest age data Age, population size data Y, and the geographical location information of the monitoring point where the image acquisition device is located, a comprehensive risk score RS is calculated using a preset water conservancy project risk assessment model, and the risk level is determined according to the RS value, including: The nest age data Age, population size data Y, and geographical location information are scored according to preset rules; Configure the water conservancy project risk assessment model to perform the following calculations: in, Score based on nest age; Score based on group size; The score represents the geographical location; w1, w2, and w3 are the weighting coefficients. The risk assessment model for the water conservancy project is configured to determine the risk level based on the RS value.
6. The water conservancy project risk assessment method based on image recognition of termite nest age according to claim 5, characterized in that, The rules include: Different scores are assigned based on the age of the nest; different scores are assigned based on the size and number of the population; and different scores are assigned based on the importance of the geographical location of the unit to which it belongs in the water conservancy facilities.
7. The water conservancy project risk assessment method based on image recognition of termite nest age according to claim 5, characterized in that, The weighting coefficients are configured to be dynamically adjusted based on historical monitoring data, meteorological data, and seasonal characteristic data. The correspondence between the risk level and the RS value is configured to be dynamically adjusted based on historical monitoring data, meteorological data, and seasonal characteristic data.
8. A water conservancy project risk assessment device based on image recognition of termite nest age, characterized in that, include: The image acquisition module is used to automatically acquire images of termites based on a preset trapping and monitoring device; The worker ant body length acquisition module is used to process the image using an image recognition algorithm, identify worker ants, and extract the body length data X of the worker ants. The colony size estimation module is used to input the body length data X into a preset colony size estimation model to obtain the termite colony size data Y; The nest age estimation module is used to input the population size data Y into a preset nest age estimation model to obtain the nest age data Age of the termite nest; The water conservancy project risk assessment module is used to calculate the comprehensive risk score RS and determine the risk level based on the nest age data Age, the population size data Y, and the geographical location information of the monitoring point where the image acquisition device is located, through a preset water conservancy project risk assessment model. The early warning module is used to issue corresponding early warning information and patrol strategies based on the determined risk level.
9. An electronic device, characterized in that, The electronic device includes: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the computer program is executed by the processor, it implements the steps of the water conservancy project risk assessment method based on image recognition of termite nest age as described in any one of claims 1 to 7.
10. A readable storage medium, characterized in that, The readable storage medium stores a program or instructions that, when executed by a processor, implement the steps of the water conservancy project risk assessment method based on image recognition of termite nest age as described in any one of claims 1 to 7.