A vector population density monitoring system

Abstract

The application relates to the technical field of vector biological monitoring, and discloses a vector biological population density monitoring system, which comprises a multi-source sensing module, a blind area identification module, an ecological diffusion link module, a vector breeding analysis module and an intelligent prevention and control module. The system obtains vector biological monitoring data, propagation space environment sensing data and human activity management data through the multi-source sensing module, sets a differential dynamic sliding window for the blind area identification module, outputs a population hidden index, improves the global space risk coverage capability, masters population dynamic changes through the ecological diffusion link module, outputs a diffusion intensity index, restores the vector cross-regional propagation path, active intensity and overflow risk, outputs a synergistic deterioration index through the vector breeding analysis module, judges the blind area risk level, population diffusion trend level and infectious disease outbreak hidden danger level through the intelligent prevention and control module, and outputs a judgment result and a response measure, so that the prevention and control manpower and materials are saved, and the pertinence and disposal efficiency of vector control are improved.

Description

Technical Field

[0001] This invention relates to the field of vector-borne disease monitoring technology, specifically a vector-borne disease population density monitoring system. Background Technology

[0002] Vector-borne organisms are those capable of carrying and transmitting pathogens and transmitting diseases to humans or animals. They mainly include rodents, mosquitoes, flies, cockroaches, ticks, and fleas. These organisms themselves are not necessarily pathogenic, but they play a crucial role in disease transmission as vectors. For example, mosquitoes can transmit dengue fever, malaria, and Japanese encephalitis; rodents can transmit plague and hemorrhagic fever; and ticks can transmit Lyme disease. Therefore, vector-borne disease control is an important component of the public health system. Vector-borne organism population density monitoring is the continuous investigation of the number, distribution, activity patterns, and trends of vector-borne organisms to understand their population dynamics. This monitoring can promptly detect large-scale proliferation of vector-borne organisms, providing a basis for infectious disease risk early warning, while also assessing the effectiveness of environmental remediation and pest control, and optimizing vector-borne disease control programs. Long-term monitoring can also clarify the characteristics of vector activity in different seasons and regions, assisting in resource allocation and precise prevention and control, reducing the risk of infectious disease outbreaks, and protecting public health.

[0003] Currently, traditional vector-borne disease population density monitoring systems mainly adopt a fixed monitoring point deployment method, which lacks the ability to cover potentially high-risk areas and easily creates monitoring blind spots. This results in important breeding sources being missed for a long time. They can only detect changes in vector density in different areas, but cannot analyze the spatial migration and diffusion relationships of vectors, identify diffusion sources, diffusion paths and diffusion links, reveal the dynamic process of vectors spreading from breeding grounds to sensitive areas, and lack the ability to conduct cross-vector population correlation analysis, making it difficult to support accurate source tracing, risk warning and scientific prevention and control decisions. Summary of the Invention

[0004] To address the shortcomings of existing technologies, this invention provides a vector-borne organism population density monitoring system, which has advantages such as improving the ability to cover risks across the entire space, saving manpower and resources for prevention and control, and improving the targeting and efficiency of vector control. It solves the problems of traditional vector-borne organism population density monitoring systems that are difficult to identify diffusion links and lack the ability to perform cross-vector population association analysis.

[0005] To achieve the above objectives, the present invention provides the following technical solution: a vector-borne organism population density monitoring system, comprising a multi-source sensing module, a blind zone identification module, an ecological diffusion link module, a vector breeding analysis module, and an intelligent control module; The multi-source sensing module is used to acquire vector-borne organism monitoring data, propagation space environment sensing data, and human activity management data, and classify them into vector datasets, environmental datasets, and human activity datasets. The blind spot identification module constructs dynamic sliding windows based on different disease vectors. Identify monitoring blind spots and quantify the population camouflage index of these blind spots. ; The ecological diffusion link module is used to analyze the migration trend of diffusion links, understand population dynamics, and quantify the output of diffusion intensity index. ; The vector breeding analysis module is used to assess the synergistic contribution of multiple vectors in a total breeding area and quantify the synergistic deterioration index. ; The intelligent prevention and control module is set with a fixed range of threshold intervals to determine the risk level of blind spots, the level of population spread trends, and the level of potential infectious disease outbreaks, and outputs the judgment results and response measures.

[0006] Preferably, the vector dataset includes rodent reproductive cycle, rodent food source migration cycle, coordinates of deployed monitoring points, monitoring point density, monitoring frequency, measured density of vector organisms, migration radius of vector organisms, breeding site detection records, and sampling timestamps, wherein the vector organism species include rodents, mosquitoes, and flies.

[0007] Preferably, the environmental dataset includes effective rainfall intervals, water accumulation cycles, temperature activity cycles, water depth, food density, average temperature, and average relative humidity.

[0008] Preferably, the human activity dataset includes propagation spatial scenarios and garbage collection cycles, wherein the propagation spatial scenarios include waterlogged areas, farmers' markets, stairwells in old residential areas, green belts, underground pipe networks, restaurant kitchens, public toilets, and garbage collection points.

[0009] Preferably, the population concealment index The calculation process is as follows: S11. Adaptive scale grid partitioning is adopted for different spatial scenarios. Enclosed spatial scenarios use 5m×5m encrypted cells, while non-enclosed scenarios use 20m×20m coarse-scale cells to generate a global cell set. All cells are marked simultaneously. Cells containing valid monitoring points are monitored cells, and cells without any monitoring records are cells without monitoring blind spots. Combine vector datasets, environmental data, and human activity datasets to integrate global vector biological monitoring data, spatial environmental perception data, and human activity management data. S12. Construct dynamic sliding windows respectively. All windows slide in daily increments, and each window is bound to a unique current timestamp. ; S13, Based on the current timestamp Dynamic sliding window Quantify output blind zone cells Ecological similarity The range of values ​​is ; S14, Based on the current timestamp Dynamic sliding window Quantize and output a single blind zone cell Information gap The range of values ​​is ; S15, Based on the current timestamp Dynamic sliding window Quantify output blind zone cells abnormal proximity The range of values ​​is ; S16. Based on S11-S15, perform weighted calculation of individual blind zone cells. population hiding index .

[0010] Preferably, the diffusion intensity index The calculation process is as follows: S21, Based on the current timestamp Dynamic sliding window Construct a directed graph of vector spread in the global cell set, including blind zone cells. With any cell A cell is considered to exist when both of the following conditions are met. To cell Effective diffusion links in the direction; (1) Blind spot cells With any cell The spatial distance between the coordinates is less than or equal to the migration radius of the current disease vector organism. (2) Blind spot cells abnormal proximity ≥ Anomaly Proximity Threshold ; S22, Define blind zone cells With any cell diffusion adjacency matrix between ; S23. Based on S21-S22, calculate the blind zone cells. With any cell diffusion intensity index between .

[0011] Preferably, the synergistic deterioration index The calculation process is as follows: S31, Based on the current timestamp Dynamic sliding window Calculate the co-occurrence frequency of rats and mosquitoes in the entire breeding ground. ; S32, Based on the current timestamp Dynamic sliding window Calculate the co-occurrence frequency of rats and flies in the entire breeding ground. ; S33, Based on the current timestamp Dynamic sliding window Calculate the mosquito-fly co-occurrence frequency in the entire breeding ground. ; S34. Based on S31-S33, calculate the overall collaborative deterioration index using a weighted method. .

[0012] Preferably, the blind spot risk level assessment process is as follows: Let the upper limit of the hidden threshold range be denoted as Let the lower limit of the hidden threshold interval be denoted as ; If cell population hiding index > , indicating a cell The monitoring gaps are significant, with blind spots classified as high-risk. Response measures include establishing more routine monitoring points across the entire area, doubling the density of monitoring points compared to regular areas; conducting preventative disinfection twice a month; adding weekly targeted disinfection of hidden breeding sites; designating specific key control areas; and carrying out a comprehensive investigation and cleanup of vector breeding grounds every two weeks. ≤cell population hiding index ≤ , indicating a cell The monitoring gap is moderate, and the blind spot risk level is medium risk. Response measures include regular monthly on-site inspections covering all areas, quarterly supplementary sampling of blind spot locations, and monthly special inspections of corridors, green belts, and underground pipe networks. Any breeding grounds discovered must be cleaned up and disposed of within 3 days. If a cell... population hiding index < , indicating a cell The monitoring gap is low, the blind spot risk level is low, and the response measures include maintaining the basic routine inspection rhythm, conducting a routine inspection of the entire area once a quarter, and carrying out a unified basic sampling and monitoring of the entire area once a year. No additional monitoring points will be added, and the frequency of special disinfection will not be increased. Basic breeding hazards will only be investigated in the routine inspection.

[0013] Preferably, the population diffusion trend level assessment process is as follows: Let the upper limit of the intensity threshold range be denoted as The lower limit of the intensity threshold range is denoted as ; If the diffusion intensity index of a single diffusion link > This indicates that the cell corresponds to a significant vector spillover transmission capability, with a population spread trend level of strong spread. Response measures include activating a cross-regional vector control mechanism, adding mobile monitoring points along the upstream and downstream of the spread path, conducting weekly comprehensive preventative disinfection along the spread channel, carrying out daily patrols and cleanups of breeding grounds covered by the path, and conducting a cross-regional joint breeding ground investigation every ten days. ≤Diffusion intensity index of a single diffusion link ≤ This indicates that the diffusion link corresponding to the cell is in an intermittently active state, and the population diffusion trend level is medium diffusion trend. Response measures include densely deploying routine monitoring points in the diffusion buffer zone, conducting monthly special inspections covering the entire buffer zone, carrying out breeding ground cleanup every half month at farmers' markets, waterlogged areas, and garbage collection points along the link, and dynamically increasing the frequency of basic disinfection in the buffer zone. If the diffusion intensity index of a single diffusion link... < This indicates that the population migration driver of the corresponding diffusion link in the cell is insufficient, and the population diffusion trend level is a weak diffusion trend. The response measures include maintaining the rhythm of routine inspections, monitoring and disinfection in the region, checking the diffusion link only in the quarterly routine inspection of the entire region, not adding any new monitoring points, not increasing the frequency of special disinfection, and only dealing with the scattered breeding points found in the inspection as needed.

[0014] Preferably, the assessment process for the risk level of an infectious disease outbreak is as follows: The upper limit of the deterioration threshold range is denoted as... The lower limit of the deterioration threshold interval is denoted as ; If the overall collaborative deterioration index > This indicates that multiple disease vectors, including rodents, mosquitoes, and flies, coexist frequently and in close proximity, posing a high risk of infectious disease outbreaks. Response measures include activating a regional vector control emergency response, establishing additional temporary monitoring points throughout the region, conducting centralized disinfection twice a week, carrying out daily rolling inspections and cleanups of breeding grounds, designating specific emergency control zones, and conducting joint multi-vector control campaigns weekly. ≤Global Collaborative Deterioration Index ≤ This indicates that a synergistic relationship between disease vectors has formed across the entire region, but is not fully coupled. The risk level for an infectious disease outbreak is considered medium. Response measures include monthly simultaneous sampling and monitoring of multiple disease vectors across the entire region, a comprehensive investigation of breeding grounds across the entire region every two months, and additional monthly special disinfection of old residential areas, underground pipe networks, and restaurant kitchens. If the overall synergistic deterioration index... < This indicates that the vector co-occurrence structure in the entire area is loose, the risk of infectious disease outbreak is low, and the infectious disease outbreak risk level is low. The response measures include maintaining the basic routine inspection rhythm in the entire area, conducting a unified basic sampling and monitoring of multiple vectors in the entire area once a year, not initiating special emergency control, not increasing the frequency of disinfection, and only checking various basic breeding risks in the routine inspection.

[0015] Compared with the prior art, the present invention provides a disease vector population density monitoring system, which has the following beneficial effects: 1. This invention acquires vector-borne disease monitoring data, propagation space environment perception data, and human activity management data through a multi-source sensing module, and classifies them into vector datasets, environmental data, and human activity datasets. This improves the completeness and accuracy of risk assessment across the entire system from the source. The blind spot identification module sets differentiated dynamic sliding windows for the three types of vectors. It can quickly locate hidden risk blind spots without monitoring points and quantify the population concealment index of the monitoring blind spots. This allows for the early identification of areas with no data support but high potential for disease vector breeding, thereby enhancing the ability to cover risks across the entire spatial area.

[0016] 2. This invention constructs a directed vector diffusion graph based on spatial distance and population overflow threshold using an ecological diffusion link module, thereby understanding population dynamics and quantifying the diffusion intensity index. This study reconstructs the cross-regional transmission routes, activity levels, and spillover risks of disease vectors. The vector breeding analysis module distinguishes between single-vector risks and multi-vector collaborative transmission risks, and quantifies the collaborative deterioration index. The system statistically analyzes the co-occurrence frequency of three types of disease vectors: rodents, mosquitoes, and flies. It provides early warning of potential chain outbreaks of infectious diseases induced by combined vectors. The intelligent control module is set with a fixed range of threshold intervals to determine the risk level of blind spots, the level of population spread trends, and the level of potential infectious disease outbreaks. It outputs the judgment results and response measures, saving manpower and resources for prevention and control, and improving the targeting and efficiency of vector control. Attached Figure Description

[0017] Figure 1 This is a system flowchart of the present invention. Detailed Implementation

[0018] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0019] Example Please see Figure 1 Based on the experimental data of population concealment index (Table 1), diffusion intensity index (Table 2), and synergistic deterioration index (Table 3), this invention provides a vector-borne organism population density monitoring system, including a multi-source sensing module, a blind zone identification module, an ecological diffusion link module, a vector breeding analysis module, and an intelligent control module. The multi-source sensing module is used to acquire vector-borne organism monitoring data, propagation space environment sensing data, and human activity management data, and classifies them into vector datasets, environmental datasets, and human activity datasets. The vector dataset includes rodent reproductive cycle, rodent food source migration cycle, coordinates of deployed monitoring points, monitoring point density, monitoring frequency, measured density of vector organisms, migration radius of vector organisms, breeding site detection records, and sampling timestamps. The vector organisms include rodents, mosquitoes, and flies. The environmental dataset includes effective rainfall intervals, waterlogging duration and turnover cycles, temperature activity cycles, waterlogging depth, food source density, average temperature, and average relative humidity. The human activity dataset includes propagation spatial scenarios and garbage collection cycles. The propagation spatial scenarios include waterlogged areas, farmers' markets, stairwells in old residential areas, green belts, underground pipe networks, restaurant kitchens, public toilets, and garbage collection points. The blind spot identification module constructs dynamic sliding windows based on different disease vectors. Identify monitoring blind spots and quantify the population camouflage index of these blind spots. ; population caching index The calculation process is as follows: S11. Adaptive scale grid partitioning is adopted for different spatial scenarios. Enclosed spatial scenarios use 5m×5m encrypted cells, while non-enclosed scenarios use 20m×20m coarse-scale cells to generate a global cell set. All cells are marked simultaneously. Cells containing valid monitoring points are monitored cells, and cells without any monitoring records are cells without monitoring blind spots. Combining vector datasets, environmental data, and human activity datasets, global vector biological monitoring data, spatial environmental perception data, and human activity management data are integrated. For each blind spot cell, environmental factors and human activity factors of the corresponding spatial range are matched. Neighborhood monitoring data is used for subsequent missing degree and proximity calculation. Specifically, underground pipe networks, restaurant kitchens, public toilets, and indoor areas of farmers' markets are enclosed scenarios, using a 5m×5m dense grid; while waterlogged areas, green belts, outdoor areas of old residential areas, and garbage collection points are non-enclosed scenarios, using a 20m×20m coarse-scale grid. S12. Construct dynamic sliding windows respectively. It is designed to adapt to the differences in the growth, reproduction, and activity rhythms of three types of disease vectors: rats, mosquitoes, and flies. All windows use a daily sliding step, and each window is bound to a unique current timestamp. Subsequent calculation steps are all confined to the window. Within the corresponding time interval, its expression is as follows: In the formula, This represents the reproductive cycle of rodents, with the dimension of days. This indicates the food migration cycle of rodents, with the dimension of days. and Let be the periodic weight coefficient, and satisfy . , Indicates the current timestamp A dynamic sliding window for mice; In the formula, This represents the effective rainfall interval period (the average interval between two rainfall events that result in flooding), with the dimension of days. This indicates the period of water accumulation and replacement, measured in days. and Let be the periodic weight coefficient, and satisfy . , Indicates the current timestamp A dynamic sliding window for mosquitoes; In the formula, This indicates the garbage collection cycle, measured in days. It represents the period of temperature activity (the duration of the temperature range in which disease vectors are active), with the dimension of days. and Let be the periodic weight coefficient, and satisfy . , Indicates the current timestamp A dynamic sliding window for flies; Specifically, for the three disease-carrying organisms—rats, mosquitoes, and flies—each is indicated in a corresponding dynamic sliding window. By executing S13-S16 under constraints, we obtain the population concealment index specific to various disease vectors. Results related to monitoring blind spots; S13, Based on the current timestamp Dynamic sliding window Quantify individual blind zone cells Output blind zone cells based on their ecological similarity to the validated high-density vector baseline cells. Ecological similarity The range of values ​​is Its expression is as follows: In the formula, Cells representing blind spots The feature vector can have its core feature factors adjusted based on the species of disease vector. For example, for rodents, the depth of water accumulation can be replaced with concealment level. This represents the baseline value of the feature corresponding to the baseline cell. Indicates the depth of the water. This represents the baseline value for the water depth in the reference cell. Indicates food source density, This represents the baseline value for food source density in the baseline cell. Indicates the average ambient temperature. This represents the baseline environmental temperature value of the reference cell. This indicates the average relative humidity of the environment. This represents the baseline humidity value for the reference cell. Forward normalization of the feature vectors of all cells to ,get The benchmark value is synchronously normalized to The forward general formula is: In the formula, Indicates the original parameter value. This represents the maximum value of the corresponding parameter. This represents the minimum value of the corresponding parameter. The maximum and minimum values ​​are derived from the total statistical interval of the corresponding parameter across all cells in the entire domain. season To avoid division by zero anomalies; In the formula, Cells representing blind spots No. Normalized values ​​of feature vectors in each dimension , Cells representing blind spots The feature distance from the baseline cell indicates that the smaller the distance, the higher the habitat similarity. In the formula, the maximum feature distance across the entire domain is used for linear normalization. This represents the maximum characteristic distance of all blind zone cells in the entire domain; Specifically, The closer the value is to 1, the stronger the homology between the habitat of the blind spot and the known high-incidence area of ​​disease vectors, and the better the environmental basis for the covert breeding and establishment. S14, Based on the current timestamp Dynamic sliding window Quantize and output a single blind zone cell Information gap The range of values ​​is Its expression is as follows: (1) Remove blind cells Distance to the nearest monitoring point Blind spot cells Time interval between the nearest monitoring point and the last monitoring point in the neighborhood and blind spot cells Percentage of unmonitored historical duration in neighboring areas Positive normalization to ; (2) Remove blind cells Density of neighboring monitoring points and blind spot cells Average sampling frequency of neighboring monitoring points Inverse normalization to The reverse general formula is: In the formula, Cells representing blind spots The normalized value of the distance to the nearest monitoring point. Cells representing blind spots Normalized value of neighborhood monitoring point density Cells representing blind spots The normalized value of the average sampling frequency of neighboring monitoring points. Cells representing blind spots Normalized value of the time interval between the nearest neighbor monitoring point and the last monitoring point. Cells representing blind spots The normalized value of the percentage of unmonitored historical time in the neighborhood. Represents the weight, and satisfies ; Specifically, The larger the value, the sparser the monitoring deployment and the more serious the data gaps in the area. "No disease vector detected" is more likely due to monitoring deficiencies rather than a true low population density. S15, Based on the current timestamp Dynamic sliding window Quantify individual blind zone cells Abnormal growth and spread of disease vector populations in surrounding areas, output blind zone cells abnormal proximity The range of values ​​is Its expression is as follows: (1) Using blind zone cells Centered on the target vector, a buffer neighborhood (e.g., 100m for rodents, 50m for mosquitoes, and 80m for flies) is set up as the spatial analysis range based on the migration radius of the corresponding vector. (2) The month-on-month increase in vector density within the buffer zone (Based on the concurrent density of the previous adjacent sliding window) and the density of newly added breeding sites within the buffer neighborhood. Positive normalization to ; In the formula, This represents the normalized value of the month-on-month increase in vector density within the buffer neighborhood. This represents the normalized value of the location density of newly added breeding sites within the buffer neighborhood. Represents the weight, and satisfies ; Specifically, The higher the value, the stronger the abnormal proliferation of the surrounding population, and the higher the spillover risk of migrating and establishing itself in this blind area; S16. Based on S11-S15, perform weighted calculation of individual blind zone cells. population hiding index Its expression is as follows: In the formula, Represents the weight, and satisfies ; Specifically, population camouflage index The higher the value, the greater the probability that there is a hidden vector population in the area, even though there is no monitoring data to support it. The following is the experimental data of the population camouflage index, as shown in Table 1: Table 1: Experimental Data of Population Camouflage Index In Table 1, the experimental data of the population camouflage index were selected, with non-enclosed scenes of green belts as the blind zone cells. The grid size is 20m×20m, and the entire process calculation is performed for mosquitoes. The sliding window step size is 1 day, and the current timestamp is [time stamp missing]. The buffer zone radius for mosquitoes is set to 50m; Table 1 shows the periodic weight coefficients in step S12. Periodic weight coefficient ; Table 1 shows that during the forward normalization process in step S13, the minimum value of water depth across the entire region is 0 mm, and the maximum value is 150 mm; the minimum value of food source density across the entire region is 0 g / m², and the maximum value is 120 g / m²; the minimum value of average ambient temperature across the entire region is 18℃, and the maximum value is 35℃; the minimum value of average ambient relative humidity across the entire region is 40%, and the maximum value is 95%. Table 1 shows that during the normalization process in step S14, the minimum distance to the nearest monitoring point across the entire region is 10m, and the maximum distance across the entire region is 300m. The minimum density of neighboring monitoring points across the entire region is 0.2 points / hm², and the maximum density across the entire region is 5 points / hm². The minimum average sampling frequency of the neighboring region is 1 time / month, and the maximum average sampling frequency across the entire region is 12 times / month. The minimum duration of the last monitoring interval across the entire region is 3 days, and the maximum duration across the entire region is 60 days. The minimum percentage of historical non-monitoring time is 0, and the maximum percentage across the entire region is 1. In step S14 of Table 1, the weights are set as follows: , , , , ; In step S15 of Table 1, the minimum value of the month-on-month increase in neighborhood density is 0%, and the maximum value is 150%. The minimum value of the density of newly added breeding sites is 0 per hm², and the maximum value is 8 per hm². In step S15 of Table 1, the weights are set as follows: , ; In step S16 of Table 1, the weights are set as follows: , ; The intelligent prevention and control module is set with a fixed range of hidden thresholds. Used to quickly determine the risk level of blind spots and hide threshold ranges. The calibration method is as follows: Using a comprehensive vector population monitoring database, cell samples with varying levels of risk in different blind zones were screened, covering low-risk (low monitoring gap, weak vector lurking risk), medium-risk (moderate monitoring gap, potential vector spillover risk), and high-risk (high monitoring gap, high probability of latent vector populations). Core monitoring data for the population elusiveness index of each sample cell were extracted (e.g., regional monitoring point coverage, historical monitoring gap duration, vector breeding ground distribution density, and previous vector population capture), routine monitoring implementation records, and follow-up results of vector control and management (e.g., population elusiveness index). (Changes, etc.) Different candidate threshold ranges were set. In each calibration experiment, the blind zone risk level of the sample cells was classified according to the candidate threshold range. The matching degree between the classification results and the on-site vector investigation and judgment conclusions was recorded. Then, combined with regional vector population dynamic monitoring data, the population camouflage index of the sample cells under different monitoring and control intervention intensities was simulated. The changing trends were analyzed, and the upper and lower limits of the candidate threshold intervals were adjusted. Multiple cross-validation experiments were conducted to record the impact of threshold interval settings on blind zone risk classification, vector monitoring deployment, and pest control. For each candidate threshold interval, the collected sample monitoring data and population dynamic simulation results were used as inputs. The number of times low-risk cells were misclassified as high-risk cells due to improper interval settings (counted as over-warning), the number of times high-risk cells were misclassified as low-risk cells (counted as under-warning), and the degree of consistency between the blind zone risk level classification results and subsequent vector control effectiveness (such as vector control efficiency, breeding ground management stability, and monitoring resource allocation suitability) were calculated. Finally, the interval range that minimizes both over-warning and under-warning rates and has the highest consistency with subsequent vector monitoring and pest control effectiveness was selected as the hidden threshold interval. The preferred range; Table 1 shows the hiding threshold range in the experimental data of the population hiding index. The preferred range is defined as 0.4-0.7. Based on the judgment, <Population camouflage index in non-enclosed green belt scenarios> < This indicates that the monitoring gaps within the green belt cells are moderate, posing a potential risk of vector population spillover. The risk level of the blind spot is medium. Response measures include fixed monthly periodic full-coverage on-site inspections, quarterly supplementary sampling of blind spot points, monthly special investigations, and cleanup and disposal of breeding grounds within 3 days of discovery. The ecological diffusion link module is used to analyze the migration trends of diffusion links, understand population dynamics, and quantify the output of the diffusion intensity index. ; Diffusion intensity index The calculation process is as follows: S21, Based on the current timestamp Dynamic sliding window Construct a directed graph of vector spread in the global cell set, including blind zone cells. With any cell A cell is considered to exist when both of the following conditions are met. To cell Effective diffusion link in the direction, at this time, blind zone mesh The risk level is determined to be medium or high, indicating a potential risk of vector population spillover or a high probability of concealed vector populations. This study is valuable for analyzing vector spread chains and will utilize a blind-area grid. Using this as the core unit, we conducted vector spread pattern analysis on its surrounding adjacent cells; (1) Blind spot cells With any cell The spatial distance between the coordinates is less than or equal to the migration radius of the current disease vector organism. (2) Blind spot cells abnormal proximity ≥ Anomaly Proximity Threshold (The threshold can be determined by the optimal cutoff value of the ROC curve of historical samples or by empirical quantiles). S22, Define blind zone cells With any cell diffusion adjacency matrix between Its expression is as follows: In the formula, Cells representing blind spots With cells Coordinate space distance between them This indicates the current migration radius of the disease vector. Indicates the spatial attenuation coefficient. This represents the diffusion triggering factor, which is 1 when the diffusion condition is met, and 0 otherwise. S23. Based on S21-S22, calculate the blind zone cells. With any cell diffusion intensity index between This is used to characterize the activity level of a link, and its expression is as follows: (1) Using ecological similarity Estimating blind zone cells vector density The expression is as follows: In the formula, This represents the measured vector density of the baseline cell; (2) Remove blind cells Vector density and cells Vector density Positive normalization to The forward general formula is: In the formula, Indicates the original parameter value. This represents the maximum value of the corresponding parameter. This represents the minimum value of the corresponding parameter. The maximum and minimum values ​​are derived from the total statistical interval of the corresponding parameter across all cells in the entire domain. season To avoid division by zero anomalies; (3) Calculate the blind zone cells With any cell The positive diffusion driving value between Its expression is as follows: In the formula, Cells representing blind spots Normalized value of vector density, Represents a cell Normalized value of vector density; Specifically, regarding the spatial heterogeneity and hotspot diffusion characteristics of vector-borne disease transmission, The larger the value, the stronger the "source-sink diffusion driving capacity" of the region, and the more significant the population diffusion trend from local to outward. In the global cell set, the diffusion intensity index is calculated by merging pairs of adjacent cells. The diffusion intensity index between adjacent grid pairs > Neighborhood association threshold When they are in the same diffusion chain, they automatically merge into the same diffusion chain; otherwise, the diffusion intensity index between adjacent grid pairs changes. ≤ Neighborhood association threshold At this time, adjacent grid pairs are not merged and are all independent diffusion chains; The following are the experimental data for the diffusion intensity index, as shown in Table 2: Table 2: Experimental Data for Diffusion Intensity Index In Table 2, the experimental data for the diffusion intensity index were also selected as blind zone cells, using a non-enclosed green belt scenario. Calculate blind zone cells To adjacent monitored cells The unidirectional diffusion link is set with a mosquito buffer neighborhood radius of 50m and an abnormal proximity threshold. ; The intelligent prevention and control module is set with a fixed range of intensity thresholds. Used to quickly determine the level of population diffusion trend and intensity threshold range. The calibration method is as follows: Using a comprehensive vector population spread monitoring database, samples of spread links with different levels of spread trends were screened, covering various situations such as weak spread trend (insufficient population migration drive, vector activity limited to local areas), medium spread trend (intermittent activation of links, local risk of population spillover), and strong spread trend (outstanding transmission capacity, continuously active and enhanced spread links). Core monitoring data on the spread intensity of the sample spread links were extracted (such as vector migration rate, channel connectivity, scale of contiguous breeding grounds, frequency of cross-regional population exchange, etc.), joint prevention and control implementation records, and subsequent monitoring results of vector spread control (such as frequency of cross-regional vector transmission, range of breeding ground spread, and spread intensity index). (Changes, etc.) Different candidate threshold ranges were set. In each calibration experiment, the population diffusion trend level of the sample diffusion link was classified according to the candidate threshold range. The matching degree between the classification results and the field vector survey conclusions was recorded. Then, combined with regional vector population dynamic monitoring data, the diffusion intensity index of the sample link under different joint prevention and control intervention intensities was simulated. The changing trends were analyzed, and the upper and lower limits of the candidate threshold intervals were adjusted. Multiple verification experiments were conducted to record the impact of threshold interval settings on population spread trend assessment, cross-regional monitoring deployment, and vector control. For each candidate threshold interval, the collected sample monitoring data and dynamic simulation results were used as inputs. The number of times a weak spread link was misjudged as a strong spread link due to improper interval setting (counted as over-prediction), the number of times a strong spread link was misjudged as a weak spread link (counted as under-prediction), and the degree of fit between the population spread trend classification results and the subsequent vector control effectiveness (such as cross-regional transmission blocking efficiency, breeding ground control stability, and the suitability of monitoring and control resource allocation) were calculated. Finally, the interval range that minimizes both over-prediction and under-prediction rates and has the highest degree of fit with the subsequent vector control effectiveness was selected as the spread intensity threshold interval. The preferred range; Table 2 shows the diffusion intensity threshold range in the experimental data of the diffusion intensity index. The preferred range is defined as 0.3-0.6. Based on the judgment, <Blind spot cells> To adjacent monitored cells diffusion intensity index < , indicating blind zone cells To adjacent monitored cells The unidirectional diffusion link is in an intermittently active state, and there is a risk of population migration and spillover in some areas. The population diffusion trend level is medium diffusion trend. Response measures include densifying the deployment of regular monitoring points in the diffusion buffer zone, conducting a special inspection covering the entire buffer zone every month, cleaning up breeding grounds in farmers' markets, waterlogged areas and garbage collection points along the link every half month, and dynamically increasing the frequency of basic disinfection in the buffer zone. The vector breeding analysis module is used to assess the synergistic contribution of multiple vectors in a total breeding area and quantify the synergistic deterioration index. ; Synergistic Deterioration Index The calculation process is as follows: S31, Based on the current timestamp Dynamic sliding window Calculate the co-occurrence frequency of rats and mosquitoes in the entire breeding ground. Its expression is as follows: In the formula, This indicates the total number of breeding grounds in the entire area. This indicates the number of rodent-mosquito co-occurrence breeding grounds in the entire area; S32, Based on the current timestamp Dynamic sliding window Calculate the co-occurrence frequency of rats and flies in the entire breeding ground. Its expression is as follows: In the formula, This indicates the number of breeding grounds in the entire region where rats and flies coexist; S33, Based on the current timestamp Dynamic sliding window Calculate the mosquito-fly co-occurrence frequency in the entire breeding ground. Its expression is as follows: In the formula, This indicates the number of mosquito-fly co-occurrence breeding grounds in the entire area; S34. Based on S31-S33, calculate the overall collaborative deterioration index using a weighted method. Its expression is as follows: In the formula, Indicates the total number of cells in the entire field. This represents the average value of the camouflage index across the entire population. Denotes the weight coefficients, and satisfies ; Specifically, The higher the value, the more closely the vectors are coupled and coexist in space and time, and the stronger the trend of systemic deterioration. The following are the experimental data for the Co-deterioration index, as shown in Table 3: Table 3: Experimental Data for the Co-deterioration Index Table 3 shows the experimental data for the Co-deterioration index. Similarly, a non-enclosed green belt scenario was selected as the overall target, with a dynamic sliding window for mosquitoes. (Approximately 7 days); The weights are set as follows: , , , ; The intelligent prevention and control module is set with a fixed range of deterioration threshold intervals. Used to determine the risk level of an infectious disease outbreak and the deterioration threshold range. The calibration method is as follows: Using a multi-vector public health surveillance database, samples from across the region were screened for different levels of infectious disease outbreak risk. These samples covered various scenarios, including low outbreak risk (loose vector co-occurrence structure, low risk of systemic transmission), medium outbreak risk (formed vector co-occurrence and continuous accumulation of public health risks), and high outbreak risk (high-frequency coupling and co-occurrence of multiple vectors, with the risk of regional chain outbreaks). Core monitoring data on infectious disease risks across the entire sample region were extracted (e.g., rodent, mosquito, and fly population density, vector co-occurrence coupling degree, scale of contiguous breeding grounds, frequency of human exposure), vector control project implementation records, and subsequent public health risk monitoring results (e.g., number of infectious disease cases, probability of cross-species transmission, and regional co-deterioration index). (Changes, etc.) Different candidate threshold ranges were set. In each calibration experiment, the potential risk level of infectious disease outbreaks in the entire sample area was classified according to the candidate threshold range. The matching degree between the classification results and the conclusions of the on-site public health survey was recorded. Then, combined with regional multi-vector dynamic monitoring data, the coordinated deterioration index of the entire sample area under different vector control intervention intensities was simulated. The changing trends were analyzed, and the upper and lower limits of the candidate threshold intervals were adjusted. Multiple verification experiments were conducted to record the impact of threshold interval settings on the assessment of infectious disease outbreak risks, vector monitoring and control, and emergency disinfection and control. For each candidate threshold interval, the collected sample monitoring data and dynamic simulation results were used as inputs to count the number of times low-outbreak-risk areas were misclassified as high-outbreak-risk areas due to improper interval settings (counted as over-warning), the number of times high-outbreak-risk areas were misclassified as low-outbreak-risk areas (counted as under-warning), and the degree of consistency between the infectious disease outbreak risk level classification results and the subsequent multi-vector control effectiveness (such as infectious disease blocking efficiency, breeding ground management stability, and emergency control resource allocation suitability). Finally, the interval range that minimizes both over-warning and under-warning rates and has the highest consistency with the subsequent multi-vector control effectiveness was selected as the deterioration threshold interval. The preferred range; Table 3 shows the deterioration threshold range in the experimental data of the co-deterioration index. The preferred range is defined as 0.3-0.5. Based on the judgment, <Global Collaborative Deterioration Index> < This indicates that a collaborative relationship between disease vectors has been formed across the entire region, but it is not fully coupled. Public health risks continue to accumulate, and the risk level for infectious disease outbreaks is at the medium level. Response measures include conducting simultaneous sampling and monitoring of multiple disease vectors across the entire region on a monthly basis, carrying out a comprehensive investigation of breeding grounds across the entire region every two months, and adding a special disinfection campaign to old residential areas, underground pipe networks, and catering kitchens every month. The intelligent prevention and control module is set with a fixed range of thresholds to determine the risk level of blind spots, the level of population spread trends, and the level of potential infectious disease outbreaks, and outputs the judgment results and response measures. The blind spot risk level assessment process is as follows: Let the upper limit of the hidden threshold range be denoted as Let the lower limit of the hidden threshold interval be denoted as The threshold can be determined by the optimal cutoff value of the ROC curve of historical samples or by empirical quantiles; If cell population hiding index > , indicating a cell The monitoring gaps are high, the probability of hidden vector populations is high, and the risk level of blind spots is high. Response measures include increasing the density of monitoring points across the entire area to twice that of regular areas, conducting preventative disinfection twice a month, adding one more localized disinfection session per week for hidden breeding sites, designating specific key control areas, and conducting a comprehensive investigation and cleanup of vector breeding grounds every two weeks. ≤cell population hiding index ≤ , indicating a cell The monitoring gap is moderate, indicating a potential risk of vector population spillover. The risk level of the blind spots is medium. Response measures include regular monthly on-site inspections covering all areas, quarterly supplementary sampling of blind spot locations, and monthly special investigations of corridors, green belts, and underground pipe networks. Any breeding grounds discovered must be cleaned up and disposed of within 3 days. If a cell... population hiding index < , indicating a cell The monitoring gap is low, the hidden risk of pathogens is weak, the risk level of blind spots is low, and the response measures include maintaining the basic routine inspection rhythm, conducting a routine inspection of the entire area once a quarter, and conducting a unified basic sampling and monitoring of the entire area once a year. No additional monitoring points will be added, and the frequency of special disinfection will not be increased. Basic breeding hazards will only be investigated in the routine inspection. The process for assessing the level of population dispersal trends is as follows: Let the upper limit of the intensity threshold range be denoted as The lower limit of the intensity threshold range is denoted as The threshold can be determined by the optimal cutoff value of the ROC curve of historical samples or by empirical quantiles; If the diffusion intensity index of a single diffusion link > This indicates that the corresponding cell represents a significant vector spillover transmission capability, with the diffusion link remaining active and intensifying, indicating a strong diffusion trend. Response measures include activating a cross-regional vector control mechanism, adding mobile monitoring points along the upstream and downstream of the diffusion link, conducting weekly comprehensive preventative disinfection along the diffusion channel, carrying out daily patrols and cleanups of breeding grounds covered by the link, and conducting a cross-regional joint breeding ground investigation every ten days. ≤Diffusion intensity index of a single diffusion link ≤ This indicates that the diffusion link corresponding to the cell is in an intermittently active state, with a risk of population migration and spillover in a local area. The population diffusion trend level is medium diffusion trend. Response measures include densely deploying routine monitoring points in the diffusion buffer zone, conducting monthly special inspections covering the entire buffer zone, carrying out breeding ground cleanup every half month at farmers' markets, waterlogged areas, and garbage collection points along the link, and dynamically increasing the frequency of basic disinfection in the buffer zone. If the diffusion intensity index of a single diffusion link... < This indicates that the population migration drive of the corresponding diffusion link in the cell is insufficient, the vector activity is limited to the local microenvironment, the population diffusion trend level is weak diffusion trend, the response measures include maintaining the rhythm of routine inspection, monitoring and disinfection in the area, checking the diffusion link only in the quarterly routine inspection of the whole area, not adding additional monitoring points, not increasing the frequency of special disinfection, and only dealing with the scattered breeding points found in the inspection as needed; The process for assessing the risk level of infectious disease outbreaks is as follows: The upper limit of the deterioration threshold range is denoted as... The lower limit of the deterioration threshold interval is denoted as The threshold can be determined by the optimal cutoff value of the ROC curve of historical samples or by empirical quantiles; If the overall collaborative deterioration index > This indicates that multiple disease vectors, including rodents, mosquitoes, and flies, coexist frequently and couple at a high frequency, possessing conditions for chain transmission and cross-host spread, posing a risk of local or regional infectious disease outbreaks. The outbreak risk level is high. Response measures include activating the regional vector emergency control response, adding temporary and intensified monitoring points throughout the region, conducting centralized disinfection twice a week, carrying out daily rolling inspections and cleanups of breeding grounds throughout the region, designating special emergency control zones, and conducting joint special campaigns against multiple disease vectors weekly. ≤Global Collaborative Deterioration Index ≤ This indicates that a synergistic relationship between disease vectors has formed across the entire region, but it is not fully coupled. Public health risks continue to accumulate, and the risk level for infectious disease outbreaks is at a medium level. Response measures include monthly simultaneous sampling and monitoring of multiple disease vectors across the entire region, a comprehensive investigation of breeding grounds across the entire region every two months, and an additional monthly special disinfection campaign in old residential areas, underground pipe networks, and restaurant kitchens. If the overall synergistic deterioration index... < This indicates that the vector co-occurrence structure in the entire area is loose, lacking the conditions for systematic and coordinated transmission, and the risk of infectious disease outbreak is low. The infectious disease outbreak risk level is low outbreak risk. Response measures include maintaining the basic routine inspection rhythm throughout the entire area, conducting a unified basic sampling and monitoring of multiple vectors throughout the entire area once a year, not initiating special emergency control, not increasing the frequency of disinfection, and only simultaneously investigating various basic breeding risks during routine inspections.

[0020] In this embodiment, a multi-source sensing module acquires vector-borne disease monitoring data, propagation space environment sensing data, and human activity management data, and classifies them into vector datasets, environmental data, and human activity datasets. This improves the completeness and accuracy of risk assessment across the entire system from the source. The blind spot identification module sets differentiated dynamic sliding windows for the three types of vectors. It can quickly locate hidden risk blind spots without monitoring points and quantify the population concealment index of the monitoring blind spots. This allows for the early identification of areas prone to disease vector breeding despite a lack of data support, enhancing overall spatial risk coverage. The ecological diffusion link module constructs a directed graph of vector diffusion based on spatial distance and population overflow thresholds, enabling the monitoring of population dynamics and quantifying the output of a diffusion intensity index. This system reconstructs the cross-regional transmission routes, activity levels, and spillover risks of disease vectors. The vector breeding analysis module distinguishes between single-vector risks and multi-vector collaborative transmission risks, and quantifies the collaborative deterioration index. The vector breeding analysis module counts the co-occurrence frequency of three types of vectors: rats, mosquitoes, rat flies, and mosquitoes and flies, providing early warning of potential chain outbreaks of infectious diseases induced by combined vectors. The intelligent prevention and control module has a fixed range of threshold intervals to determine the risk level of blind spots, the level of population spread trend, and the level of potential infectious disease outbreaks. It outputs the judgment results and response measures, realizing automatic risk assessment, saving manpower and resources for prevention and control, and improving the targeting and efficiency of vector control.

[0021] The threshold is set to facilitate comparison. The size of the threshold depends on the amount of sample data and the number of bases set by those skilled in the art for each set of sample data; as long as it does not affect the ratio between the parameter and the quantized value, it is acceptable.

[0022] The above formulas are all derived from software simulation using a large amount of data and are selected to be close to the actual values. The coefficients in the formulas are set by those skilled in the art according to the actual situation. The above description is only a preferred embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any equivalent substitutions or changes made by those skilled in the art within the technical scope disclosed in the present invention, based on the technical solution and inventive concept of the present invention, should be covered within the protection scope of the present invention.

Claims

1. A vector-borne organism population density monitoring system, characterized in that: It includes a multi-source sensing module, a blind spot identification module, an ecological diffusion link module, a vector breeding analysis module, and an intelligent prevention and control module; The multi-source sensing module is used to acquire vector-borne organism monitoring data, propagation space environment sensing data, and human activity management data, and classify them into vector datasets, environmental datasets, and human activity datasets. The blind spot identification module constructs dynamic sliding windows based on different disease vectors. Identify monitoring blind spots and quantify the population camouflage index of these blind spots. ; The ecological diffusion link module is used to analyze the migration trend of diffusion links, understand population dynamics, and quantify the output of diffusion intensity index. ; The vector breeding analysis module is used to assess the synergistic contribution of multiple vectors in a total breeding area and quantify the synergistic deterioration index. ; The intelligent prevention and control module is set with a fixed range of threshold intervals to determine the risk level of blind spots, the level of population spread trends, and the level of potential infectious disease outbreaks, and outputs the judgment results and response measures.

2. The vector-borne organism population density monitoring system according to claim 1, characterized in that: The vector dataset includes rodent reproductive cycles, rodent food source migration cycles, coordinates of deployed monitoring points, monitoring point density, monitoring frequency, measured vector density, vector activity migration radius, breeding site detection records, and sampling timestamps. The vector species include rodents, mosquitoes, and flies.

3. The vector-borne organism population density monitoring system according to claim 2, characterized in that: The environmental dataset includes effective rainfall intervals, water accumulation cycles, temperature activity cycles, water depth, food density, average temperature, and average relative humidity.

4. The vector-borne organism population density monitoring system according to claim 3, characterized in that: The human activity dataset includes propagation spatial scenarios and garbage collection cycles. The propagation spatial scenarios include waterlogged areas, farmers' markets, stairwells in old residential areas, green belts, underground pipe networks, restaurant kitchens, public toilets, and garbage collection points.

5. The vector-borne organism population density monitoring system according to claim 4, characterized in that: The population hiding index The calculation process is as follows: S11. Adaptive scale grid partitioning is adopted for different spatial scenarios. Enclosed spatial scenarios use 5m×5m encrypted cells, while non-enclosed scenarios use 20m×20m coarse-scale cells to generate a global cell set. All cells are marked simultaneously. Cells containing valid monitoring points are monitored cells, and cells without any monitoring records are cells without monitoring blind spots. Combine vector datasets, environmental data, and human activity datasets to integrate global vector biological monitoring data, spatial environmental perception data, and human activity management data. S12. Construct dynamic sliding windows respectively. All windows slide in daily increments, and each window is bound to a unique current timestamp. ; S13, Based on the current timestamp Dynamic sliding window Quantify output blind zone cells Ecological similarity The range of values ​​is ; S14, Based on the current timestamp Dynamic sliding window Quantize and output a single blind zone cell Information gap The range of values ​​is ; S15, Based on the current timestamp Dynamic sliding window Quantify output blind zone cells abnormal proximity The range of values ​​is ; S16. Based on S11-S15, perform weighted calculation of individual blind zone cells. population hiding index .

6. The vector-borne organism population density monitoring system according to claim 5, characterized in that: The diffusion intensity index The calculation process is as follows: S21, Based on the current timestamp Dynamic sliding window Construct a directed graph of vector spread in the global cell set, including blind zone cells. With any cell A cell is considered to exist when both of the following conditions are met. To cell Effective diffusion links in the direction; (1) Blind spot cells With any cell The spatial distance between the coordinates is less than or equal to the migration radius of the current disease vector organism. (2) Blind spot cells abnormal proximity ≥ Anomaly Proximity Threshold ; S22, Define blind zone cells With any cell diffusion adjacency matrix between ; S23. Based on S21-S22, calculate the blind zone cells. With any cell diffusion intensity index between .

7. The vector-borne organism population density monitoring system according to claim 6, characterized in that: The Co-deterioration Index The calculation process is as follows: S31, Based on the current timestamp Dynamic sliding window Calculate the co-occurrence frequency of rats and mosquitoes in the entire breeding ground. ; S32, Based on the current timestamp Dynamic sliding window Calculate the co-occurrence frequency of rats and flies in the entire breeding ground. ; S33, Based on the current timestamp Dynamic sliding window Calculate the mosquito-fly co-occurrence frequency in the entire breeding ground. ; S34. Based on S31-S33, calculate the overall collaborative deterioration index using a weighted method. .

8. The vector-borne organism population density monitoring system according to claim 5, characterized in that: The blind spot risk level assessment process is as follows: Let the upper limit of the hidden threshold range be denoted as Let the lower limit of the hidden threshold interval be denoted as ; If cell population hiding index > , indicating a cell The monitoring gaps are significant, with blind spots classified as high-risk. Response measures include establishing more routine monitoring points across the entire area, doubling the density of monitoring points compared to regular areas; conducting preventative disinfection twice a month; adding weekly targeted disinfection of hidden breeding sites; designating specific key control areas; and carrying out a comprehensive investigation and cleanup of vector breeding grounds every two weeks. ≤cell population hiding index ≤ , indicating a cell The monitoring gap is moderate, and the blind spot risk level is medium risk. Response measures include regular monthly on-site inspections covering all areas, quarterly supplementary sampling of blind spot locations, and monthly special inspections of corridors, green belts, and underground pipe networks. Any breeding grounds discovered must be cleaned up and disposed of within 3 days. If a cell... population hiding index < , indicating a cell The monitoring gap is low, the blind spot risk level is low, and the response measures include maintaining the basic routine inspection rhythm, conducting a routine inspection of the entire area once a quarter, and carrying out a unified basic sampling and monitoring of the entire area once a year. No additional monitoring points will be added, and the frequency of special disinfection will not be increased. Basic breeding hazards will only be investigated in the routine inspection.

9. A vector-borne organism population density monitoring system according to claim 6, characterized in that: The process for assessing the level of population dispersal trends is as follows: Let the upper limit of the intensity threshold range be denoted as The lower limit of the intensity threshold range is denoted as ; If the diffusion intensity index of a single diffusion link > This indicates that the cell corresponds to a significant vector spillover transmission capability, with a population spread trend level of strong spread. Response measures include activating a cross-regional vector control mechanism, adding mobile monitoring points along the upstream and downstream of the spread path, conducting weekly comprehensive preventative disinfection along the spread channel, carrying out daily patrols and cleanups of breeding grounds covered by the path, and conducting a cross-regional joint breeding ground investigation every ten days. ≤Diffusion intensity index of a single diffusion link ≤ This indicates that the diffusion link corresponding to the cell is in an intermittently active state, and the population diffusion trend level is medium diffusion trend. Response measures include densely deploying routine monitoring points in the diffusion buffer zone, conducting monthly special inspections covering the entire buffer zone, carrying out breeding ground cleanup every half month at farmers' markets, waterlogged areas, and garbage collection points along the link, and dynamically increasing the frequency of basic disinfection in the buffer zone. If the diffusion intensity index of a single diffusion link... < This indicates that the population migration driver of the corresponding diffusion link in the cell is insufficient, and the population diffusion trend level is a weak diffusion trend. The response measures include maintaining the rhythm of routine inspections, monitoring and disinfection in the region, checking the diffusion link only in the quarterly routine inspection of the entire region, not adding any new monitoring points, not increasing the frequency of special disinfection, and only dealing with the scattered breeding points found in the inspection as needed.

10. A vector-borne organism population density monitoring system according to claim 7, characterized in that: The process for assessing the risk level of an infectious disease outbreak is as follows: The upper limit of the deterioration threshold range is denoted as... The lower limit of the deterioration threshold interval is denoted as ; If the overall collaborative deterioration index > This indicates that multiple disease vectors, including rodents, mosquitoes, and flies, coexist frequently and in close proximity, posing a high risk of infectious disease outbreaks. Response measures include activating a regional vector control emergency response, establishing additional temporary monitoring points throughout the region, conducting centralized disinfection twice a week, carrying out daily rolling inspections and cleanups of breeding grounds, designating specific emergency control zones, and conducting joint multi-vector control campaigns weekly. ≤Global Collaborative Deterioration Index ≤ This indicates that a synergistic relationship between disease vectors has formed across the entire region, but is not fully coupled. The risk level for an infectious disease outbreak is considered medium. Response measures include monthly simultaneous sampling and monitoring of multiple disease vectors across the entire region, a comprehensive investigation of breeding grounds across the entire region every two months, and additional monthly special disinfection of old residential areas, underground pipe networks, and restaurant kitchens. If the overall synergistic deterioration index... < This indicates that the vector co-occurrence structure in the entire area is loose, the risk of infectious disease outbreak is low, and the infectious disease outbreak risk level is low. The response measures include maintaining the basic routine inspection rhythm in the entire area, conducting a unified basic sampling and monitoring of multiple vectors in the entire area once a year, not initiating special emergency control, not increasing the frequency of disinfection, and only checking various basic breeding risks in the routine inspection.

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