A termite monitoring and intelligent identification and killing system

Abstract

The present invention discloses a termite monitoring intelligent identification and killing system, which belongs to the technical field of termite monitoring and processing; it is used to solve the technical problems of poor multi-dimensional evaluation and analysis effect of termite monitoring and poor targeted killing and management effect in existing solutions; different environmental impact parameters of different location areas are monitored, counted and processed, and the processing results corresponding to the different environmental impact parameters are sorted, combined and analyzed; data analysis is performed on termite activity analysis data corresponding to different location areas, the termite activity risks corresponding to the different location areas are determined, and the termite activity data corresponding to the different location areas are verified; and the termite activity risks corresponding to the different location areas are corrected and termite killing management is performed according to the verification results, and the analysis scheme of the termite activity analysis data is dynamically optimized.

Description

Technical Field

[0001] The present invention relates to the technical field of termite monitoring and treatment, and in particular to a termite monitoring intelligent identification and killing system. Background Art

[0002] Termite monitoring, identification and extermination refers to the process of monitoring, identifying and controlling termites using a series of technical means.

[0003] When implementing existing termite monitoring, identification and extermination programs, it is impossible to conduct data analysis and evaluation of possible termite activities in different locations from different dimensions, verify and conduct anomaly analysis of possible termite activities in different locations, and adaptively implement targeted termite extermination management in different locations based on the analysis results. As a result, the multi-dimensional evaluation and analysis of termite monitoring is not effective, and the targeted extermination management is not effective. Summary of the Invention

[0004] The purpose of the present invention is to provide a termite monitoring intelligent identification and killing system to solve the technical problems of poor multi-dimensional evaluation and analysis effect of termite monitoring and poor targeted killing management effect in existing solutions.

[0005] The purpose of the present invention can be achieved through the following technical solutions:

[0006] A termite monitoring and intelligent identification and killing system includes a location area information collection and processing module for monitoring, counting, and processing different environmental impact parameters in different location areas, and sorting, combining, and analyzing the processing results corresponding to different environmental impact parameters to obtain termite activity analysis data corresponding to different location areas;

[0007] The location area risk verification and processing module is used to analyze the termite activity analysis data corresponding to different location areas, determine the termite activity risks corresponding to different location areas, and verify the termite activity data corresponding to different location areas. Based on the verification results, the termite activity risks corresponding to different location areas are corrected and termite extermination management is carried out, and the analysis plan of the termite activity analysis data is dynamically optimized.

[0008] Preferably, data monitoring and statistics are performed on different location areas according to different preset environmental impact parameters, and when the monitoring and statistical data corresponding to different environmental impact parameters are processed, the values ​​of all monitoring and statistical data corresponding to the link impact parameters are extracted and displayed and connected through the corresponding monitoring coordinate system to obtain the monitoring processing curve of the corresponding environmental impact parameters;

[0009] When analyzing the monitoring and processing curves corresponding to different environmental impact parameters, the monitoring and processing curves whose environmental impact parameters fall within the monitoring and processing range are marked as valid curves, and the total duration of appearance corresponding to all valid curves is obtained, and the total duration of appearance corresponding to the environmental impact parameters is calculated using the formula The corresponding occurrence duration coefficient CCi is calculated; where i is a different environmental impact parameter, i = 1, 2, 3, ..., n; n is a positive integer; TCi is the total occurrence duration corresponding to the environmental impact parameter; TJi is the total monitoring duration corresponding to the environmental impact parameter; αi is the parameter influence factor corresponding to the environmental impact parameter;

[0010] The occurrence and persistence coefficients obtained from the corresponding treatments of different environmental impact parameters are sorted and combined to obtain a first regulatory treatment sequence.

[0011] Preferably, all item materials corresponding to the location area are obtained, and different item materials are traversed and matched in turn through the material influence table to obtain material influence values ​​corresponding to different item materials;

[0012] The material influence values ​​corresponding to the materials of all items in the location area are calculated by the formula Calculate the corresponding item influence coefficient WYj; where j is the material of the item, j = 1, 2, 3, ..., m; m is a positive integer; ZTj is the volume corresponding to the material of the item; ZT0 is the spatial volume corresponding to the location area; βj is the material influence value corresponding to the material of the item;

[0013] The item influence coefficients obtained from the corresponding processing of different item materials are sorted and combined to obtain a second supervision processing sequence.

[0014] Preferably, the first supervision processing sequence and the second supervision processing sequence obtained by the location area corresponding processing are calculated by the formula The corresponding regional risk integration coefficient QF is calculated; where η is the error correction factor and is a real number greater than one; M is the total number of materials of all items whose material impact value is not 0;

[0015] According to the value of the regional risk integration coefficient, the corresponding different location areas are arranged in descending order and numbered to obtain the termite activity analysis data corresponding to the different location areas.

[0016] Preferably, when performing data analysis on termite activity risks corresponding to different location areas, the regional risk integration coefficients corresponding to the different location areas are compared with preset regional risk integration standard values;

[0017] If QF≤QF0, it indicates that the location area corresponds to a mild termite activity risk, and the location area is marked as the first risk area; QF0 is the regional risk integration standard value;

[0018] Otherwise, it is prompted that the location area corresponds to a high risk of termite activity, and the location area is marked as a second risk area.

[0019] Preferably, termite trace data corresponding to different location areas are obtained, and the termite trace data are analyzed using a termite activity verification model, and a verification coefficient HX of the corresponding location area is output;

[0020] Among them, the expression of the termite activity verification model is: Where, a is the termite trace data;

[0021] The verification coefficient contains a value of 0 or 1.

[0022] Preferably, a verification coefficient of 0 indicates that there is no risk of termite activity traces in the corresponding location area;

[0023] A verification coefficient of 1 indicates that there is a risk of termite activity in the area.

[0024] Furthermore, the location area is marked as a target location area according to the verification coefficient having a value of 1, and a termite extermination plan is implemented in the target location area.

[0025] Preferably, when dynamically optimizing the analysis scheme for termite activity analysis data, risk area markers corresponding to all target location areas are obtained and analyzed;

[0026] If the risk area marks corresponding to all target location areas are the second risk area, then the analysis plan for termite activity risk data is determined to be effective and the subsequent implementation is maintained;

[0027] If the risk area marks corresponding to all target location areas have the first risk area, then the formula Calculate the risk identification validity SX corresponding to the analysis scheme; where N0 and N1 are the total number of all target location areas and the total number of first risk areas with corresponding risk area marks in all target location areas, respectively; A is the risk identification standard value.

[0028] Preferably, data analysis is performed on the risk identification validity to determine the risk identification abnormality level corresponding to the analysis scheme of termite activity risk data;

[0029] If SX>0, it indicates that the risk identification corresponding to the analysis scheme of termite activity risk data is slightly abnormal, and prompts to implement the first optimization scheme;

[0030] Otherwise, it will be prompted that the risk identification corresponding to the analysis plan of termite activity risk data is severely abnormal, and it will be prompted to implement the second optimization plan.

[0031] Compared with the existing solutions, the present invention achieves the following beneficial effects:

[0032] The present invention monitors, counts and processes different environmental impact parameters in different location areas, and sorts, combines and analyzes the processing results corresponding to different environmental impact parameters. It can not only realize multi-dimensional supervision and digital processing of regional risks corresponding to different location areas, but also provide reliable multi-dimensional supervision data support for the subsequent implementation of termite extermination management corresponding to different location areas and the optimization of risk plans for termite activity analysis data, thereby improving the multi-dimensional evaluation and analysis effect of termite monitoring.

[0033] The present invention determines the termite activity risks corresponding to different location areas by performing data analysis on the termite activity analysis data corresponding to different location areas, verifies the termite activity data corresponding to different location areas, and corrects the termite activity risks corresponding to different location areas and manages termite extermination based on the verification results. At the same time, the analysis scheme of the termite activity analysis data is dynamically optimized, thereby realizing autonomous dynamic extermination of termites in different location areas. At the same time, it can also realize autonomous optimization management of existing supervision and analysis schemes, thereby improving the targeted extermination management effect of termites and the identification and implementation optimization management effect. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] The present invention will be further described below with reference to the accompanying drawings.

[0035] Figure 1 This is a block diagram of the steps of operating a termite monitoring, intelligent identification and killing system of the present invention.

[0036] Figure 2 This is a block diagram of the steps for analyzing and obtaining the first risk area and the second risk area in the present invention. DETAILED DESCRIPTION

[0037] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0038] like Figure 1 As shown, the present invention is a termite monitoring intelligent identification and killing system, which includes a location area information collection and processing module and a location area risk verification and processing module;

[0039] The location area information collection and processing module is used to monitor, count, and process different environmental impact parameters in different location areas, sort, combine, and analyze the processing results corresponding to different environmental impact parameters to obtain termite activity analysis data corresponding to different location areas; it includes:

[0040] Data monitoring and statistics are performed on different location areas based on different preset environmental impact parameters, which include but are not limited to temperature, humidity and light intensity. Termites prefer warm climates and are most active when the temperature is between 17°C and 35°C (approximately 62.6°F to 95°F). Humidity is essential for termite survival. Termite colonies require a specific humidity level to maintain their body water content and keep the humidity in the nest appropriate. Termites prefer dark places, so those dark corners that are not easily discovered by humans are ideal habitats for termites. Other environmental impact parameters can be added, deleted, and modified according to the application requirements of actual application scenarios. When processing the monitoring and statistical data corresponding to different environmental impact parameters, the values ​​of all monitoring and statistical data corresponding to the link impact parameters are extracted and displayed and connected through the corresponding monitoring coordinate system to obtain the monitoring and processing curves of the corresponding environmental impact parameters.

[0041] The horizontal axis of the monitoring coordinate system corresponding to different environmental impact parameters is the real-time changing Beijing time, and the vertical axis is the corresponding numerical value with arithmetical changes. The specific numerical values ​​of the arithmetical changes can be determined based on the historical monitoring big data of the corresponding environmental impact parameters, and can also be customized according to the application requirements of the actual application scenario;

[0042] When analyzing the monitoring and treatment curves corresponding to different environmental impact parameters, the monitoring and treatment curves whose environmental impact parameters fall within the monitoring and treatment range are marked as valid curves. The monitoring and treatment range is determined based on the environmental test data of historical termite activities, and the total duration of all valid curves is obtained. The unit of the total duration is minutes, and the total duration of the environmental impact parameters is calculated by the formula Calculate and obtain the corresponding occurrence persistence coefficient CCi; where i is a different environmental impact parameter, i = 1, 2, 3, ..., n; n is a positive integer, representing the total number of all environmental impact parameters; TCi is the total occurrence duration corresponding to the environmental impact parameter; TJi is the total monitoring duration corresponding to the environmental impact parameter; αi is the parameter impact factor corresponding to the environmental impact parameter, and the parameter impact factor is used to digitally represent the calculated impact degree of the corresponding environmental impact parameter. The specific values ​​of the parameter impact factors corresponding to different environmental impact parameters can be determined based on the preliminary test data of termite activity, or based on the existing environmental impact assessment data of termite activity. The specific values ​​are not limited and can be customized according to the application requirements of the actual application scenario.

[0043] The occurrence and persistence coefficient is used to process and calculate the monitoring data corresponding to different aspects of environmental impact parameters to digitally represent the corresponding occurrence and persistence of negative impacts;

[0044] Sort and combine the occurrence and persistence coefficients obtained from the corresponding treatments of different environmental impact parameters to obtain a first regulatory treatment sequence;

[0045] Also, obtain all item materials corresponding to the location area, and traverse and match different item materials through the material influence table in turn to obtain the material influence values ​​corresponding to different item materials;

[0046] The material of the object includes but is not limited to metal, plastic, and wood. The material impact table pre-sets a number of sample materials, and each sample material is associated with a corresponding material impact value. The sample materials and their associated material impact values ​​can be obtained based on historical termite activity monitoring data and test data. The specific value of the material impact value can be determined based on the area range of historical termite activity. If the material of the object is not in the material impact table, the corresponding material impact value is set to 0.

[0047] The material influence values ​​corresponding to the materials of all items in the location area are calculated by the formula Calculate the corresponding item influence coefficient WYj; where j is the material of the item, j = 1, 2, 3, ..., m; m is a positive integer representing the total number of all item materials; ZTj is the volume corresponding to the material of the item; ZT0 is the spatial volume corresponding to the location area; βj is the material influence value corresponding to the material of the item;

[0048] The item impact coefficient is used to process and calculate the monitoring data corresponding to different aspects of the item material, and to digitally represent the negative impact of the corresponding item;

[0049] Sort and combine the item influence coefficients obtained from the corresponding processing of different item materials to obtain a second supervision processing sequence;

[0050] In the embodiment of the present invention, supervision, data processing and combination are carried out from the aspects of environmental impact and material of objects respectively, which can not only realize the supervision and digital processing representation of data of different factors affecting termite activities, but also provide reliable multi-dimensional supervision and processing data support for subsequent regional risk integration analysis corresponding to different location areas.

[0051] The first supervision processing sequence and the second supervision processing sequence obtained by the location area corresponding processing are calculated by the formula The corresponding regional risk integration coefficient QF is calculated; where η is the error correction factor and is a real number greater than one, specifically 1.72; M is the total number of materials of all items whose material impact value is not 0;

[0052] According to the value of the regional risk integration coefficient, the corresponding different location areas are arranged in descending order and numbered to obtain the termite activity analysis data corresponding to the different location areas;

[0053] In an embodiment of the present invention, by monitoring, counting and processing different environmental impact parameters in different location areas, and sorting, combining and analyzing the processing results corresponding to different environmental impact parameters, it is possible to achieve multi-dimensional supervision and digital processing of regional risks corresponding to different location areas, and provide reliable multi-dimensional supervision data support for the subsequent implementation of termite extermination management corresponding to different location areas and the optimization of risk plans for termite activity analysis data, thereby improving the multi-dimensional evaluation and analysis effect of termite monitoring.

[0054] The location area risk verification processing module is used to analyze the termite activity analysis data corresponding to different location areas, determine the termite activity risks corresponding to different location areas, verify the termite activity data corresponding to different location areas, and modify the termite activity risks corresponding to different location areas and manage termite extermination based on the verification results. At the same time, the analysis plan of the termite activity analysis data is dynamically optimized; it includes:

[0055] like Figure 2 As shown, when analyzing the data of termite activity risks corresponding to different location areas, the regional risk integration coefficients corresponding to different location areas are compared with the preset regional risk integration standard values. The regional risk integration coefficients can be determined based on the median value of all regional risk integration coefficients obtained from historical processing of different location areas, or based on the actual application requirements of the actual application scenario and the early test data of termite activity.

[0056] If QF≤QF0, it indicates that the location area corresponds to a mild termite activity risk, and the location area is marked as the first risk area; QF0 is the regional risk integration standard value;

[0057] Otherwise, it indicates that the location area corresponds to a high risk of termite activity, and marks the location area as a second risk area;

[0058] Obtain termite trace data corresponding to different locations. Termite trace data includes but is not limited to mud tunnels, honeycombed or layered wood, excrement, and shed wings. Professionals in this field will inspect and determine the termite trace data, analyze it using a termite activity verification model, and output a verification coefficient HX for the corresponding location area.

[0059] Among them, the expression of the termite activity verification model is: Where, a is the termite trace data;

[0060] The verification coefficient contains a value of 0 or 1;

[0061] A verification coefficient of 0 indicates that there is no risk of termite activity in the area.

[0062] A verification coefficient of 1 indicates that there is a risk of termite activity in the area.

[0063] and, marking the location area as a target location area according to the verification coefficient having a value of 1, and implementing a termite extermination plan in the target location area;

[0064] When dynamically optimizing the analysis scheme for termite activity analysis data, obtain and analyze the risk area labels corresponding to all target location areas;

[0065] If the risk area marks corresponding to all target location areas are the second risk area, then the analysis plan for termite activity risk data is determined to be effective and the subsequent implementation is maintained;

[0066] It can be understood that if all the target location areas obtained by verification are the second risk areas identified and marked by the analysis scheme of the existing termite activity risk data, it means that the identification implementation of the analysis scheme of the existing termite activity risk data is effective;

[0067] On the contrary, it means that the identification and implementation of the existing termite activity risk data analysis program is partially effective or ineffective, and targeted optimization and upgrading are needed;

[0068] If the risk area marks corresponding to all target location areas have the first risk area, then the formula Calculate the risk identification validity SX corresponding to the analysis scheme; where N0 and N1 are the total number of all target location areas and the total number of first risk areas with corresponding risk area markers in all target location areas, respectively; A is the risk identification standard value, which can be determined based on preliminary test data on termite activity or customized according to the application requirements of the actual application scenario;

[0069] Risk identification validity is used to integrate and process the analysis data and verification data corresponding to the analysis plan to digitally represent the risk identification effect of the analysis plan;

[0070] Conduct data analysis on risk identification validity to determine the degree of risk identification anomaly corresponding to the analysis scheme for termite activity risk data;

[0071] If SX>0, it indicates that the risk identification corresponding to the analysis scheme of termite activity risk data is slightly abnormal, and prompts to implement the first optimization scheme;

[0072] Otherwise, it will prompt that the risk identification corresponding to the analysis plan of termite activity risk data is severely abnormal, and prompt the implementation of the second optimization plan;

[0073] Among them, the first optimization scheme can specifically add, delete, or modify the identification and analysis rules of the existing analysis scheme locally;

[0074] The second optimization solution can specifically make overall additions, deletions, and modifications to the identification and analysis rules of the existing analysis solution.

[0075] In an embodiment of the present invention, by performing data analysis on termite activity analysis data corresponding to different location areas, the termite activity risks corresponding to different location areas are determined, and the termite activity data corresponding to different location areas are verified. Based on the verification results, the termite activity risks corresponding to different location areas are corrected and termite extermination management is performed. At the same time, the analysis scheme of the termite activity analysis data is dynamically optimized, thereby realizing autonomous dynamic extermination of termites in different location areas. At the same time, it is also possible to realize autonomous optimization management of existing supervision and analysis schemes, thereby improving the targeted extermination management effect of termites and the identification and implementation of optimized management effects.

[0076] In addition, the formulas involved in the above are all calculated by removing dimensions and taking their numerical values. They are a formula that is closest to the actual situation obtained by collecting a large amount of data and simulating it through simulation software.

[0077] In the several embodiments provided by the present invention, it should be understood that the disclosed system can be implemented in other ways. For example, the embodiments of the invention described above are merely illustrative. For example, the division of modules is only a logical function division, and other division methods may be used in actual implementation.

[0078] Modules described as separate components may or may not be physically separate, and components shown as modules may or may not be physical modules, and may be located in one place or distributed across multiple network modules. Some or all of these modules may be selected to achieve the objectives of this embodiment based on actual needs.

[0079] In addition, the functional modules in various embodiments of the present invention may be integrated into a single processing module, each module may exist physically separately, or two or more modules may be integrated into a single module. The integrated modules may be implemented in the form of hardware or hardware plus software functional modules.

[0080] It is obvious to a person skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above, but that the present invention can be implemented in other specific forms without departing from the essential characteristics of the present invention.

[0081] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not limiting. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention.

Claims

1. A termite monitoring and intelligent identification and killing system, characterized in that: It includes a location area information collection and processing module, which is used to monitor, count and process different environmental impact parameters in different location areas, and sort, combine and analyze the processing results corresponding to different environmental impact parameters to obtain termite activity analysis data corresponding to different location areas; Among them, when monitoring and statistics are performed on different environmental impact parameters in different location areas and processing them, data monitoring and statistics are performed on different location areas according to preset different environmental impact parameters, and when the monitoring and statistical data corresponding to different environmental impact parameters are processed, the values ​​of all monitoring and statistical data corresponding to the link impact parameters are extracted and displayed and connected through the corresponding monitoring coordinate system to obtain the monitoring and processing curve of the corresponding environmental impact parameters; When analyzing the monitoring and processing curves corresponding to different environmental impact parameters, the monitoring and processing curves whose environmental impact parameters fall within the monitoring and processing range are marked as valid curves, and the total duration of appearance corresponding to all valid curves is obtained, and the total duration of appearance corresponding to the environmental impact parameters is calculated using the formula The corresponding occurrence duration coefficient CCi is calculated; where i is a different environmental impact parameter, i=1, 2, 3, ..., n; n is a positive integer; TCi is the total occurrence duration corresponding to the environmental impact parameter; TJi is the total monitoring duration corresponding to the environmental impact parameter; αi is the parameter influence factor corresponding to the environmental impact parameter; Sort and combine the occurrence and persistence coefficients obtained from the corresponding treatments of different environmental impact parameters to obtain a first regulatory treatment sequence; Get all the item materials corresponding to the location area, and traverse and match different item materials through the material influence table in turn to obtain the material influence values ​​corresponding to different item materials; The material influence values ​​corresponding to the materials of all items in the location area are calculated by the formula Calculate the corresponding item influence coefficient WYj; where j is the material of the item, j=1, 2, 3, ..., m; m is a positive integer; ZTj is the volume corresponding to the material of the item; ZT0 is the spatial volume corresponding to the location area; βj is the material influence value corresponding to the material of the item; Sort and combine the item influence coefficients obtained from the corresponding processing of different item materials to obtain a second supervision processing sequence; The location area risk verification processing module is used to analyze the termite activity analysis data corresponding to different location areas, determine the termite activity risks corresponding to different location areas, verify the termite activity data corresponding to different location areas, and modify the termite activity risks corresponding to different location areas and carry out termite extermination management based on the verification results. At the same time, the analysis plan of the termite activity analysis data is dynamically optimized; When verifying the termite activity data corresponding to different location areas, the termite trace data corresponding to different location areas are obtained, and the termite trace data are analyzed through the termite activity verification model, and the verification coefficient HX of the corresponding location area is output; wherein, the expression of the termite activity verification model is ; Where a is the termite trace data; the verification coefficient contains a value of 0 or 1; A verification coefficient of 0 indicates that there is no risk of termite activity in the area. A verification coefficient of 1 indicates that there is a risk of termite activity in the area. Furthermore, the location area is marked as a target location area according to the verification coefficient having a value of 1, and a termite extermination plan is implemented in the target location area.

2. The intelligent termite monitoring and killing system according to claim 1, characterized in that: The first supervision processing sequence and the second supervision processing sequence obtained by the location area corresponding processing are calculated by the formula The corresponding regional risk integration coefficient QF is calculated; Where η is the error correction factor and is a real number greater than one; M is the total number of materials of all items whose material influence value is not 0; According to the value of the regional risk integration coefficient, the corresponding different location areas are arranged in descending order and numbered to obtain the termite activity analysis data corresponding to the different location areas.

3. The intelligent termite monitoring and killing system according to claim 2, characterized in that: When analyzing the data on termite activity risks corresponding to different locations, the regional risk integration coefficients corresponding to different locations are compared with the preset regional risk integration standard values; If QF≤QF0, it indicates that the location area corresponds to a mild termite activity risk, and the location area is marked as the first risk area; QF0 is the regional risk integration standard value; Otherwise, it is prompted that the location area corresponds to a high risk of termite activity, and the location area is marked as a second risk area.

4. The intelligent termite monitoring and killing system according to claim 1, characterized in that: When dynamically optimizing the analysis scheme for termite activity analysis data, obtain and analyze the risk area labels corresponding to all target location areas; If the risk area marks corresponding to all target location areas are the second risk area, then the analysis plan for termite activity risk data is determined to be effective and the subsequent implementation is maintained; If the risk area marks corresponding to all target location areas have the first risk area, then the formula Calculate the risk identification validity SX corresponding to the analysis scheme; where N0 and N1 are the total number of all target location areas and the total number of first risk areas with corresponding risk area marks in all target location areas, respectively; A is the risk identification standard value.

5. The intelligent termite monitoring and killing system according to claim 4, characterized in that: Conduct data analysis on risk identification validity to determine the degree of risk identification anomaly corresponding to the analysis scheme for termite activity risk data; If SX>0, it indicates that the risk identification corresponding to the analysis scheme of termite activity risk data is slightly abnormal, and prompts to implement the first optimization scheme; Otherwise, it will be prompted that the risk identification corresponding to the analysis plan of termite activity risk data is severely abnormal, and it will be prompted to implement the second optimization plan.

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