SYSTEM AND METHOD FOR DETECTING RATS AND MICE INFESTATIONS

Abstract

The present invention relates to a system for detecting and predicting rats and mice infestations. The system comprises a plurality of rechargeable traps, and a receiver unit configured for receiving a monitoring data signal from the rechargeable traps. The receiver unit comprises a processor and is operatively connected to a database. The database is adapted to store multiple database entries representing a value of a parameter related to rats and mice infestations at various points in time. The processor is programmed to perform a univariate and/or multivariate analysis of the database entries to detect and predict rats and mice infestations in a predefined geographical area.

Claims

1. A system for detecting and predicting rats and mice infestations, comprising: a plurality of rechargeable traps, comprising: a) a spring driven and/or gas pressure driven killing means; b) species monitoring means configured for determining the species by monitoring the degree of advancement of the killing means during an individual triggering; c) kill monitoring means configured for determining the number of kills by monitoring the number of triggering of the killing means; and d) a transmitter unit configured for receiving monitoring data from the species monitoring means and from the kill monitoring means, and configured for transmitting the received monitoring data; and a receiver unit configured for receiving a monitoring data signal from the transmitter unit; the receiver unit comprising a processor and being operatively connected to a database; wherein the database is adapted to store multiple database entries representing a value of a parameter at various points in time, the parameter being selected from the first group of i) individual number of rats killed by said killing means ii) combined number of rats killed by said killing means, iii) average number of rats killed by said killing means, iv) individual number of mice killed by said killing means v) combined number of mice killed by said killing means, vi) average number of mice killed by said killing means, viii) combined number of rodents killed by said killing means, ix) average number of rodents killed by said killing means, and optionally from the second group of I) location of said plurality of rechargeable traps, II) individual location of said plurality of rechargeable traps III) average temperature in the vicinity of said individual rechargeable traps, IV) individual temperature in the vicinity of said individual rechargeable traps, V) average humidity in the vicinity of said plurality of rechargeable traps, VI) individual humidity in the vicinity of said plurality of rechargeable traps, VII) individual type of bait used in said plurality rechargeable traps, and VIII) bait type composition used in said plurality rechargeable traps; and wherein the processor is programmed to: 1a) perform a univariate analysis of the database entries of said first group to obtain a first set of data representing expected values of at least one of said parameters at future points in time; and 1b) store the first set of data in said database; and/or 2a) perform a multivariate analysis of the database entries of said first group and said second group to produce a second set of data, derived from combined analysis of values of at least one of the said parameters from said first group, and at least one of the said parameters from said second group; the second set of data being representative of a) the current and/or future degree of rats and/or mice infestation in the area where the plurality of rechargeable traps are positioned, and/or b) the optimal timing for providing service to the plurality of rechargeable traps, and/or c) the type of bait to be used in the plurality of rechargeable traps, and/or d) the need for more or less rechargeable traps in a given area, and/or e) specific locations suitable for positioning rechargeable traps, and/or f) problem zones; and 2b) store the second set of data in said database.

2. A system according to claim 1, wherein the processor is programmed to: 1a) perform a univariate analysis of the database entries of said first group to obtain a first set of data representing expected values of at least one of said parameters at future points in time; and 1b) store the first set of data in said database.

3. A system according to claim 2, wherein the processor is further programmed to 1c) compare said first set of data with a pattern of previously measured values of the same parameter, to predict: a) the current and future degree of rats and mice infestation in the area where the plurality of rechargeable traps are positioned, and/or b) the optimal timing for providing service to the plurality of rechargeable traps, and/or c) the type of bait to be used in the plurality of rechargeable traps, and/or d) the need for more or less rechargeable traps in a given area, and/or e) specific locations suitable for positioning rechargeable traps, and/or f) problem zones.

4. A system according to claim 1, wherein the processor is programmed to: 2a) perform a multivariate analysis of the database entries of said first group and said second group to produce a second set of data, derived from combined analysis of values of at least one of the said parameters from said first group, and at least one of the said parameters from said second group; the second set of data being representative of a) the current and/or future degree of rats and/or mice infestation in the area where the plurality of rechargeable traps are positioned, and/or b) the optimal timing for providing service to the plurality of rechargeable traps, and/or c) the type of bait to be used in the plurality of rechargeable traps, and/or d) the need for more or less rechargeable traps in a given area, and/or e) specific locations suitable for positioning rechargeable traps, and/or f) problem zones; and 2b) store the second set of data in said database.

5. A system according to claim 4, wherein the processor is further programmed to 2c) compare said second set of data with a pattern of previously measured values of the same parameters, to predict: a) the current and future degree of rats and mice infestation in the area where the plurality of rechargeable traps are positioned, and/or b) the optimal timing for providing service to the plurality of rechargeable traps, and/or c) the type of bait to be used in the plurality of rechargeable traps, and/or d) the need for more or less rechargeable traps in a given area, and/or e) specific locations suitable for positioning rechargeable traps, and/or f) problem zones.

6. A system according to claim 5, wherein the processor is programmed to: 3a) combine the first and second sets of data to obtain a third set of data representative of a) the current and future degree of rats and/or mice infestation in the area where the plurality of rechargeable traps are positioned, and/or b) the optimal timing for providing service to the plurality of rechargeable traps, and/or c) the type of bait to be used in the plurality of rechargeable traps, and/or d) the need for more or less rechargeable traps in a given area, and/or e) specific locations suitable for positioning rechargeable traps, and/or f) problem zones; 3b) store the third set of data in said database.

7. A system according to claim 1, wherein the value parameters are selected from both the first group and the second group.

8. A system according to claim 1, wherein the plurality of rechargeable traps further comprise a housing with a rat and mouse entry opening positioned in the side wall and/or in the bottom wall; and wherein the spring driven and/or gas pressure driven killing means is positioned at a level above the level of the rat and/or mouse entry opening, such that a rat or mouse can reach the killing means when standing within the housing on their hind legs.

9. A system according to claim 1, wherein the wherein the spring driven and/or gas pressure driven killing means comprises a piston, and wherein the piston, after a triggered release, is configured to hold its position for a predefined period of time.

10. A system according to claim 9, wherein the species monitoring means is configured for monitoring the degree of advancement of the piston during the predefined period of time that the piston holds its position during a triggered release.

11. A system according to claim 1, wherein the spring driven and/or gas pressure driven killing means comprises a piston, and wherein the piston, after a triggered release, is returned to a charged position within a piston bore by a motor unit, and wherein a) the motor operating time needed to return the piston to a charged position and/or b) the force needed by the motor to return the piston to a charged position and/or c) the number of motor shaft revolutions needed to return the piston to a charged position and/or d) measuring the power consumption needed to return the piston to a charged position, are used by the species monitoring means for monitoring the degree of advancement of the piston.

Description

DETAILED DESCRIPTION OF THE INVENTION

[0032] One tool for multivariate data analysis is Principal Component Analysis (PCA), in literature also referred to as factor analysis. In short, manifest variables are substituted by latent variables in multivariate data analysis. Manifest variables are direct and measurable, i.e. manifested variables, in the present context also referred to as parameter values, such as the individual number of rats killed by the killing means. Latent variables are weighted sums of the manifest variables. As an example, latent variables t1 and t2 are determined as t1=0.45*individual number of rats killed by the killing means+0.12*humidity, and t2=0.05*individual number of rats killed by the killing means+0.72*humidity. Here, t1 and t2 are projections of the manifest variables, individual number of rats killed by the killing means; and humidity, on vectors [0.45; 0.12] and [0.05; 0.72]. By appropriate selection of weightings, e.g. as eigenvectors of a matrix of manifest variables, the thus determined latent variables include information from all of the manifest variables independently from the number of manifest variables. Accordingly, information in an aggregation of data may be distinguished or separated from random noise. Moreover, the weightings may be visualised, so as to enable extraction of information related to the manifest variables, and the latent variables may be visualised, so as to enable extraction of information concerning objects, for example the optimal timing for providing service to the plurality of rechargeable traps.

[0033] The aggregation of data may conveniently be arranged or stored in a table in the database. For example, measured variables may be arranged in columns of the table, and the objects, may be arranged in rows. This table is referred to as X. In PCA, the above-mentioned weightings can be the elements in the eigenvectors to the correlation matrix of X. The number of relevant eigenvectors, which governs the number of relevant latent variables, is dependent from the content of information in X.

[0034] The current and/or future degree of rat infestation in the area where the plurality of rechargeable traps are positioned may be determined from a comparison of a pattern in measured parameters, and a reference pattern (or a reference parameter value) (which is typical for a situation with relatively low degree of rat infestation), and a pattern, which is typical for a relatively high degree of rat infestation.

[0035] In multivariate data analysis, so-called patterns of parameters (i.e. manifest variables) may be provided in order to take into account mutual influences between parameters. If a selective parameter is at hand, univariate data analysis may be appropriate.