Adaptive behavior identification method and device, equipment, medium and product

Abstract

The invention discloses an adaptive behavior identification method and device, equipment, a medium and a product. The method comprises the steps of obtaining behavior feature data corresponding to at least two to-be-identified individuals; wherein the behavior characteristic data represents statistical behavior information represented by the to-be-identified individual in response to repeated threat stimulation signals in repeated threat stimulation experiments; according to the at least two pieces of behavior feature data, clustering the at least two to-be-identified individuals to obtain at least two clustering sets; and according to the clustering index values corresponding to the at least two clustering sets, determining an adaptive behavior result corresponding to each to-be-identified individual in each clustering set, thereby improving identification accuracy and identification efficiency of adaptive behaviors of biological individuals in a repeated threat stimulation scene.

Description

Technical Field

[0001] The present invention relates to the field of data processing technology, and in particular to an adaptive behavior recognition method, device, equipment, medium and product. Background Art

[0002] Fear is defined as a basic emotion that occurs quickly when an organism perceives a threat. In nature, organisms can respond quickly to threats such as natural enemies, harmful stimuli, and danger signals, and take instinctive reactions such as fighting, escaping, and playing dead according to environmental clues. Neuroscientists call the above series of instinctive reactions taken by organisms when facing threats instinctive defense behaviors.

[0003] Among them, escape can reduce the harm to the individual organism, but it will also lead to the cessation of other survival activities such as foraging and finding habitats, and the loss of important resources. In order to balance escape and other survival behaviors, individuals usually use sensory information and past experience to estimate threats and decide whether to escape. In different species, individuals repeatedly exposed to threats may show different coping strategies.

[0004] The adaptive response strategies of biological individuals to recurring threats in the environment are of great significance to both individual survival and species reproduction. Accurate classification of the adaptive behaviors of biological individuals will help to deeply explore the generation mechanism, development process and influencing factors of adaptive behaviors, thereby providing data support for the development of effective intervention strategies.

[0005] At present, the existing definition of adaptive behavior of biological individuals relies on the subjective judgment of scientific researchers, and it is particularly difficult to accurately distinguish biological individuals at the edge of group boundaries. Summary of the invention

[0006] The embodiments of the present invention provide a method, device, equipment, medium and product for identifying adaptive behavior, so as to solve the problem that adaptive behavior depends on subjective judgment and improve the recognition accuracy and efficiency of adaptive behavior.

[0007] According to an embodiment of the present invention, a method for identifying adaptive behavior is provided, the method comprising:

[0008] Obtaining behavioral characteristic data corresponding to at least two individuals to be identified, wherein the behavioral characteristic data represent statistical behavioral information exhibited by the individuals to be identified in response to repetitive threat stimulus signals in a repeated threat stimulus experiment;

[0009] Clustering the at least two individuals to be identified to obtain at least two cluster sets according to the at least two behavioral characteristic data;

[0010] According to the clustering index values ​​respectively corresponding to at least two clustering sets, the adaptive behavior results respectively corresponding to each to-be-identified individual in each clustering set are determined.

[0011] According to another embodiment of the present invention, a device for identifying adaptive behavior is provided, the device comprising:

[0012] A behavior characteristic data acquisition module, used to acquire behavior characteristic data corresponding to at least two individuals to be identified; wherein the behavior characteristic data represents statistical behavior information exhibited by the individuals to be identified in response to repetitive threat stimulus signals in a repeated threat stimulus experiment;

[0013] A cluster set determination module, used for clustering the at least two individuals to be identified to obtain at least two cluster sets according to at least two behavioral characteristic data;

[0014] The adaptive behavior result determination module is used to determine the adaptive behavior result corresponding to each to-be-identified individual in each cluster set according to the cluster index values ​​corresponding to at least two cluster sets.

[0015] According to another embodiment of the present invention, there is provided an electronic device, the electronic device comprising:

[0016] at least one processor; and

[0017] a memory communicatively connected to the at least one processor; wherein,

[0018] The memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can perform the method for identifying adaptive behavior described in any embodiment of the present invention.

[0019] According to another embodiment of the present invention, a computer-readable storage medium is provided, wherein the computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a processor to implement the adaptive behavior recognition method described in any embodiment of the present invention when executed.

[0020] According to another embodiment of the present invention, a computer program product is provided, including a computer program. When the computer program is executed by a processor, the method for identifying adaptive behavior described in any embodiment of the present invention is implemented.

[0021] An embodiment of the above invention has at least the following advantages or beneficial effects:

[0022] At least two cluster sets are obtained by clustering at least two individuals to be identified according to the behavioral characteristic data corresponding to the at least two individuals to be identified, and the adaptive behavior results corresponding to each individual to be identified in each cluster set are determined according to the cluster index values ​​corresponding to the at least two cluster sets, wherein the behavioral characteristic data represent the statistical behavioral information exhibited by the individual to be identified in response to repetitive threat stimulus signals in a repeated threat stimulus experiment, thereby solving the problem of adaptive behavior relying on subjective judgment and improving the recognition accuracy and efficiency of the adaptive behavior of biological individuals in repetitive threat stimulus scenarios.

[0023] It should be understood that the contents described in this section are not intended to identify the key or important features of the embodiments of the present invention, nor are they intended to limit the scope of the present invention. Other features of the present invention will become easily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0025] Figure 1 A flowchart of a method for identifying adaptive behavior provided by an embodiment of the present invention;

[0026] Figure 2 A flowchart of another adaptive behavior recognition method provided by an embodiment of the present invention;

[0027] Figure 3 A schematic diagram of a specific example of a method for identifying adaptive behavior provided by an embodiment of the present invention;

[0028] Figure 4 A schematic diagram of visualization of adaptive behavior results of a cluster set provided by an embodiment of the present invention;

[0029] Figure 5 A schematic diagram of the recognition results of adaptive behaviors corresponding to repeated threat stimulation experiments from the first day to the third day in a continuous repeated threat stimulation experiment provided by an embodiment of the present invention;

[0030] Figure 6 A schematic diagram of the recognition results of adaptive behaviors corresponding to the repeated threat stimulation experiment on the 4th day to the 5th day in a continuous repeated threat stimulation experiment provided by an embodiment of the present invention;

[0031] Figure 7A schematic diagram of the structure of an adaptive behavior recognition device provided by an embodiment of the present invention;

[0032] Figure 8 The present invention is a schematic structural diagram of an electronic device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0033] In order to enable those skilled in the art to better understand the scheme of the present invention, the technical scheme in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. 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 creative work should fall within the scope of protection of the present invention.

[0034] It should be noted that the terms "first", "second", "target", "to be identified", etc. in the specification and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0035] Figure 1 This is a flow chart of a method for identifying adaptive behavior provided by an embodiment of the present invention. This embodiment is applicable to the case of identifying adaptive behavior in a threat stimulus environment, especially in a repetitive threat stimulus environment. The method can be executed by an adaptive behavior identification device, which can be implemented in the form of hardware and / or software, and can be configured in a terminal device. Figure 1 As shown, the method includes:

[0036] S110, obtaining behavioral characteristic data corresponding to at least two individuals to be identified.

[0037] Specifically, the individual to be identified refers to a biological individual for adaptive behavior identification. For example, the individual species of the individual to be identified include, but are not limited to, insects, rodents, or primates. When the individual species of the individual to be identified is a rodent, the individual to be identified may be a mouse. The individual to be identified is not limited here, and can be customized according to actual needs.

[0038] In this embodiment, the behavioral characteristic data represents the statistical behavioral information exhibited by the individual to be identified in response to repetitive threat stimulus signals in a repeated threat stimulus experiment.

[0039] Specifically, a repeated threat stimulus experiment refers to an experiment in which the same threat stimulus signal is given to the individual to be identified multiple times within the experimental duration to collect behavioral response data of the individual to be identified, which is used to simulate the dangerous stimuli that biological individuals face in the natural environment for a long time.

[0040] In an optional embodiment, the experimental paradigm of the repeated threat stimulation experiment includes: in the preparation period before the start of the repeated threat stimulation experiment, a timing device is used to perform a timing operation to allow the individual to be identified to adapt to the experimental box; during the repeated threat stimulation experiment, a timing device is used to perform a timing operation to allow the individual to be identified to adapt to the experimental box, and after the timing ends, in response to detecting that the individual to be identified is located in a preset stimulation area in the experimental box, the stimulation device is controlled to send a threat stimulation signal.

[0041] In another optional embodiment, the repeated threat stimulation experiment is included in the continuous repeated threat stimulation experiment, and the experimental paradigm of the continuous repeated threat stimulation experiment includes: in the preparation period before the start of the continuous repeated threat stimulation experiment, a timing device is used to perform a timing operation to allow the individual to be identified to adapt to the experimental box; during the process of the continuous repeated threat stimulation experiment, for each repeated threat stimulation experiment, a timing device is used to perform a timing operation to allow the individual to be identified to adapt to the experimental box, and after the timing ends, in response to detecting that the individual to be identified is located in a preset stimulation area in the experimental box, the stimulation device is controlled to send a threat stimulation signal.

[0042] In this embodiment, the number of repeated threat stimulation experiments in the continuous repeated threat stimulation experiment is 5, and the experimental cycle of the repeated threat stimulation experiment is 1 day.

[0043] In the above embodiment, the number of times the threat stimulus signal is sent is greater than 1 and less than the number threshold, the time interval between two adjacent threat stimulus signals is greater than the time interval threshold, and the experimental duration of the individual to be identified is less than the experimental duration threshold. Exemplarily, the preparation period is 1 day, the timing duration corresponding to the timing operation can be 10 minutes or 15 minutes, the number threshold can be 10 times or 15 times, the time interval threshold can be 2 minutes or 3 minutes, and the experimental duration threshold can be 1 hour or 2 hours, but it is not limited to the above example situations.

[0044] Exemplarily, the signal types of threat stimulus signals include but are not limited to auditory stimulus signals, visual stimulus signals, tactile stimulus signals or olfactory stimulus signals, etc. For example, the auditory stimulus signal may be a sudden high-decibel sound signal or a long-term sharp sound signal, etc., the visual stimulus signal may be a gradually increasing image signal, an approaching object, a flashing strong light signal or a terrifying image signal, etc., the tactile stimulus signal may be an electrical stimulation signal or an airflow impact signal, etc., the olfactory stimulus signal may be the smell of the predator of the individual to be identified or the smell containing harmful chemicals, etc., but is not limited to the above-mentioned example situations.

[0045] In an optional embodiment, the threat stimulus signal is a visual looming danger signal, which indicates that a black disk with an initial diameter expands to a black disk with a target diameter at a preset expansion rate and continues to display a signal for a preset time. For example, the initial diameter may be 5 cm, the preset expansion rate may be 5 cm / s, the target diameter may be 30 cm, and the preset time may be 30 s or 1 min, but is not limited to the above example situations.

[0046] In this embodiment, the continuous repeated threat stimulation experiment using visual imminent danger signals as threat stimulus signals is defined as the continuous repeated negative visual experience experiment (Repeated Aff ective Visual Experiences, RAVE). The RAVE experiment has strong stability and is applicable to biological groups of different genders.

[0047] In this embodiment, the behavior characteristic data includes at least two behavior characteristic parameters. In an optional embodiment, the behavior characteristic data includes at least two of the escape times, escape ratio, average escape reaction latency, average escape speed, and average escape return time.

[0048] Specifically, the number of escapes represents the cumulative frequency of the individual to be identified making escape actions in response to the threatening stimulus signal within the experimental duration, the escape ratio represents the ratio between the number of escapes and the number of times the threatening stimulus signal is issued within the experimental duration, the average escape reaction latency represents the ratio between the sum of the reaction latency times corresponding to at least one escape action and the number of escapes, the reaction latency represents the time interval between the start of the stimulation of the threatening stimulus signal and the moment when the individual to be identified makes an escape behavior, the average escape speed represents the ratio between the sum of the escape speeds corresponding to at least one escape action and the number of escapes, the average escape return to the nest time represents the ratio between the sum of the return to the nest time corresponding to at least one escape action and the number of escapes, and the return to the nest time represents the time interval between the start of the stimulation of the threatening stimulus signal and the moment when the individual to be identified returns to the nest.

[0049] On the basis of the above embodiment, optionally, when the individual to be identified does not make an escape action in the repeated threat stimulation experiment, each behavior characteristic parameter in the behavior characteristic data is set to 0. Among them, the escape action represents the behavior action when the current moving speed of the individual to be identified exceeds the speed threshold. Exemplarily, the speed threshold may be 40 mm / s, but is not limited to the example case, and the speed threshold may be customized according to the individual type of the individual to be identified.

[0050] S120. Cluster at least two individuals to be identified according to at least two behavioral characteristic data to obtain at least two cluster sets.

[0051] Exemplarily, clustering algorithms include but are not limited to K-Means clustering algorithm, hierarchical clustering algorithm, DBSCAN (Density-Based Spatial Clustering of Applications with Noise) algorithm and Gaussian mixture model clustering algorithm, etc. The clustering algorithm used is not limited here, and the specific settings can be customized according to actual needs.

[0052] In an optional embodiment, at least two individuals to be identified are clustered according to at least two behavioral characteristic data to obtain at least two cluster sets, including: for each individual to be identified, the behavioral characteristic data corresponding to the individual to be identified is subjected to dimensionality reduction processing to obtain the behavioral characteristic data after dimensionality reduction processing; based on at least two behavioral characteristic data after dimensionality reduction processing, at least two individuals to be identified are clustered to obtain at least two cluster sets.

[0053] Exemplarily, the behavioral feature data after dimensionality reduction processing can be expressed as [umap1, umap2, umap3]. The dimensionality reduction algorithm used in the dimensionality reduction processing includes but is not limited to UMAP (Uniform Manifold Approximation and Projection) dimensionality reduction algorithm, PCA (Principal Component Analysis) dimensionality reduction algorithm, T-SNE (t-Distributed Stochastic Neighbor Embedding) dimensionality reduction algorithm, SVD (Singular Value Decomposition) dimensionality reduction algorithm and LLE dimensionality reduction algorithm (Locally Linear Embedding), etc. The dimensionality reduction algorithm used in the dimensionality reduction processing is not limited here.

[0054] The benefit of setting up dimensionality reduction processing is that it removes noise and redundant information in the behavioral feature data, reduces the computational complexity of the clustering step, and makes the distribution structure of the behavioral feature data in the low-dimensional space clearer and more separable, thereby improving the quality and interpretability of subsequent clustering results.

[0055] Specifically, a cluster set represents a set of individuals to be identified that belong to the same adaptive behavior. Exemplarily, when the number of cluster sets is two, the adaptive behavior result is adaptive or maladaptive, when the number of cluster sets is three, the adaptive behavior result is overadaptive, adaptive, and maladaptive, and when the number of cluster sets is four, the adaptive behavior result is overadaptive, adaptive, maladaptive, and maladaptive. The number of cluster sets and their corresponding adaptive behaviors are not limited here.

[0056] S130. Determine, according to the clustering index values ​​corresponding to at least two clustering sets, the adaptive behavior results corresponding to each to-be-identified individual in each clustering set.

[0057] In an optional embodiment, the clustering index value indicates the similarity between the clustering feature corresponding to the cluster set and the real features corresponding to at least two adaptive behaviors, wherein the clustering feature can be the statistical feature of the behavior feature parameters in the behavior feature data under the cluster set, or can be the statistical feature of other behavior feature parameters under the cluster set.

[0058] For example, the clustering index value may be a statistical value related to the escape ratio or a statistical value related to the escape reaction latency, such as an average value or a median value, etc. The selection of the clustering index value is not limited here, and can be customized according to actual needs.

[0059] In this embodiment, the adaptive behavior result corresponding to each individual to be identified in each cluster set is determined according to the cluster index values ​​corresponding to at least two cluster sets, including: for each cluster set, the cluster index value of the cluster set is determined according to the cluster characteristics of the cluster set and the real characteristics corresponding to at least two adaptive behavior results; the adaptive behavior result corresponding to the smallest index value among the cluster index values ​​is used as the adaptive behavior result corresponding to each individual to be identified in the cluster set.

[0060] Among them, the clustering index value includes at least two index values, and the index value represents the characteristic distance between the clustering characteristics corresponding to the clustering set and the real characteristics corresponding to the adaptive behavior results. Exemplarily, the algorithm for determining the clustering index value includes but is not limited to the distance algorithm or the neural network algorithm, but is not limited to the example scenario.

[0061] The technical solution of this embodiment is to cluster at least two individuals to be identified according to the behavioral characteristic data respectively corresponding to the at least two individuals to be identified to obtain at least two cluster sets, and determine the adaptive behavior results corresponding to each individual to be identified in each cluster set according to the cluster index values ​​respectively corresponding to the at least two cluster sets, wherein the behavioral characteristic data represents the statistical behavioral information exhibited by the individual to be identified in response to repetitive threat stimulus signals in the repeated threat stimulus experiment, thereby solving the problem of adaptive behavior relying on subjective judgment and improving the recognition accuracy and efficiency of the adaptive behavior of biological individuals in repetitive threat stimulus scenarios.

[0062] Figure 2 A flowchart of another adaptive behavior identification method provided by an embodiment of the present invention, this embodiment further refines the above-mentioned embodiment of "determining the adaptive behavior result corresponding to each individual to be identified in each cluster set according to the cluster index values ​​corresponding to at least two cluster sets". In this embodiment, the adaptive behavior result corresponding to each individual to be identified in each cluster set is determined according to the cluster index values ​​corresponding to at least two cluster sets, including: comparing the cluster index values ​​corresponding to at least two cluster sets to obtain a comparison result; and determining the adaptive behavior result corresponding to each individual to be identified in each cluster set according to the comparison result. Figure 2 As shown, the method includes:

[0063] S210: Obtain behavioral characteristic data corresponding to at least two individuals to be identified.

[0064] In an optional embodiment, S210 is the same as in the above embodiment. Figure 1 The S110 shown corresponds to the same or similar steps, and will not be described in detail in this embodiment.

[0065] In another optional embodiment, when the individual to be identified is a rodent, the behavioral characteristic data includes an action conversion frequency and an action proportion data set, the action proportion data set represents the ratio information between the behavioral action duration and the escape duration of the individual to be identified, the escape duration represents the length of time from the start of the stimulation of the threatening stimulus signal to the end of the escape of the individual to be identified within a single stimulation cycle, and the action proportion data set includes at least one of the flying action proportion, the running action proportion, the trotting action proportion, the walking action proportion, the left turn action proportion, the right turn action proportion, the stepping action proportion, the climbing action proportion, the sniffing action proportion, the grooming action proportion, the pause action proportion, the freezing behavior proportion and the jumping behavior proportion.

[0066] Specifically, the action conversion frequency indicates the number of times the individual to be identified converts from one behavioral action to another in the repeated threat stimulation experiment. The single stimulation cycle indicates the time interval between the stimulation start time of the current threat stimulation signal and the stimulation start time of the next threat stimulation signal. The single stimulation cycles corresponding to each two adjacent threat stimulation signals may be the same or different. The escape end time indicates the time when the escape speed of the individual to be identified changes from greater than or equal to the speed threshold to less than the speed threshold within the single stimulation cycle.

[0067] Specifically, the duration of the behavior action represents a statistical value of the duration of the behavior action. For example, the statistical value may be a summation value, a maximum value, an average value, or the like.

[0068] Among them, the running action corresponding to the running action proportion satisfies the behavioral action that the moving speed reaches the first speed threshold and / or the stride reaches the first stride threshold, the trotting action corresponding to the trotting action proportion satisfies the behavioral action that the moving speed is greater than the second speed threshold and less than the first speed threshold and / or the stride is greater than the second stride threshold and less than the first stride threshold, the walking action corresponding to the walking action proportion satisfies the behavioral action that the moving speed is greater than the third speed threshold and less than the second speed threshold and / or the stride is greater than the third stride threshold and less than the second stride threshold, and the stepping action corresponding to the stepping action proportion includes at least two behavioral actions of running action, trotting action and walking action.

[0069] S220: Cluster at least two individuals to be identified according to at least two behavioral characteristic data to obtain at least two cluster sets.

[0070] S220 in this embodiment is similar to that in the above embodiment. Figure 1 The S120 shown corresponds to the same or similar steps, and will not be described in detail in this embodiment.

[0071] S230: Compare the clustering index values ​​corresponding to at least two clustering sets to obtain a comparison result.

[0072] Specifically, the clustering index value represents the statistical value of the clustering feature corresponding to the clustering set under the clustering set. The clustering feature can be the statistical feature of the behavior feature parameters in the behavior feature data under the clustering set, or it can be the statistical feature of other behavior feature parameters under the clustering set.

[0073] In an optional embodiment, the clustering index value is a statistical value corresponding to the escape ratio. Exemplarily, the statistical value may be an average value or a median value.

[0074] S240: Determine the adaptive behavior results corresponding to each to-be-identified individual in each cluster according to the comparison result.

[0075] In an optional embodiment, based on the comparison result, the adaptive behavior results corresponding to each individual to be identified in each cluster set are determined, including: when the cluster index value is a statistical value corresponding to the escape ratio, and the number of cluster sets is two, the adaptive behavior results of at least one individual to be identified in the cluster set with a larger cluster index value shown in the comparison result are all regarded as adaptive; the adaptive behavior results of at least one individual to be identified in the cluster set with a smaller cluster index value shown in the comparison result are all regarded as non-adaptive.

[0076] In another optional embodiment, based on the comparison result, the adaptive behavior result corresponding to each individual to be identified in each cluster set is determined, including: when the cluster index value is a statistical value corresponding to the escape ratio, and the number of cluster sets is three, the adaptive behavior result of at least one individual to be identified in the cluster set with the largest cluster index value shown in the comparison result is regarded as over-adaptive; the adaptive behavior result of at least one individual to be identified in the cluster set with the second largest cluster index value shown in the comparison result is regarded as adaptive; the adaptive behavior result of at least one individual to be identified in the cluster set with the smallest cluster index value shown in the comparison result is regarded as non-adaptive.

[0077] The technical solution of this embodiment obtains a comparison result by comparing the clustering index values ​​corresponding to at least two cluster sets, and determines the adaptive behavior result corresponding to each individual to be identified in each cluster set according to the comparison result, thereby achieving the purpose of unsupervised recognition of the adaptive behavior of biological individuals, saving manpower, material resources and time costs, improving the versatility of the adaptive behavior recognition method, and facilitating the subsequent exploration of the previously unknown distribution patterns of cluster sets.

[0078] Based on the above embodiment, optionally, the method also includes: when the number of cluster sets is two, determining an escape ratio threshold value based on the escape ratios and adaptive behavior results of at least two individuals to be identified; if the escape ratio of the target individual is greater than or equal to the escape ratio threshold value, setting the adaptive behavior result of the target individual to adaptive type; if the escape ratio of the target individual is less than the escape ratio threshold value, setting the adaptive behavior result of the target individual to maladaptive type; wherein the target individual is a biological individual participating in the repeated threat stimulation experiment other than the at least two individuals to be identified.

[0079] Specifically, the escape ratio threshold is determined based on the escape ratios and adaptive behavior results of at least two individuals to be identified, including: averaging the escape ratio of at least one individual to be identified corresponding to the adaptive type to obtain a first average escape ratio, averaging the escape ratio of at least one individual to be identified corresponding to the unadaptive type to obtain a second average escape ratio, and determining the escape ratio threshold based on the first average escape ratio and the second average escape ratio. Specifically, the escape ratio threshold satisfies [p2, p1], where p1 represents the first average escape ratio and p2 represents the second average escape ratio.

[0080] In an optional embodiment, the escape ratio threshold is an average of the first average escape ratio and the second average escape ratio.

[0081] Exemplarily, the adaptive recognition condition is expressed as: M / N≥P, where M represents the number of times the biological individual escapes in a repeated threat stimulation experiment, N represents the number of times the threat stimulation signal is triggered by the biological individual in the repeated threat stimulation experiment, and P represents the escape ratio threshold.

[0082] The advantage of this setting is that, under the premise of ensuring the accuracy of the adaptive behavior results, it overcomes the dependence of the adaptive behavior identification method on the biological population and achieves the purpose of quickly identifying the adaptive behavior of a single biological individual.

[0083] Figure 3 A schematic diagram of a specific example of an adaptive behavior recognition method provided by an embodiment of the present invention. Specifically, a plurality of individuals to be identified are subjected to continuous repeated threat stimulation experiments, and the behavioral characteristic data of each individual to be identified in each repeated threat stimulation experiment are counted. For each repeated threat stimulation experiment, the behavioral characteristic data corresponding to each individual to be identified and the repeated threat stimulation experiment are subjected to dimensionality reduction processing to obtain 3-dimensional behavioral characteristic data. Hierarchical clustering is performed on the 3-dimensional behavioral characteristic data of all individuals to be identified to obtain 2 cluster sets, namely cluster set A and cluster set B. It is judged whether the average escape ratio corresponding to cluster set A is greater than the average escape ratio corresponding to cluster set B. If so, the adaptive behavior results of the individuals to be identified in cluster set A in the repeated threat stimulation experiment are set to adaptive type, and correspondingly, the adaptive behavior results of the individuals to be identified in cluster set B in the repeated threat stimulation experiment are set to non-adaptive type; if not, the adaptive behavior results of the individuals to be identified in cluster set B in the repeated threat stimulation experiment are set to adaptive type, and correspondingly, the adaptive behavior results of the individuals to be identified in cluster set A in the repeated threat stimulation experiment are set to non-adaptive type.

[0084] Figure 4 A schematic diagram of the visualization of adaptive behavior results of a cluster set provided by an embodiment of the present invention. Figure 4For example, the number of cluster sets is two, and the behavior feature data after dimensionality reduction is three-dimensional data. Specifically, Figure 4 The three coordinate axes in the figure represent the three dimensions of the behavioral feature data after dimensionality reduction processing. The cluster composed of red circles represents the cluster set composed of adaptive individuals to be identified, and the cluster composed of blue circles represents the cluster set composed of unadaptive individuals to be identified.

[0085] Figure 5 and Figure 6 The figure shows the recognition results of adaptive behaviors corresponding to the repeated threat stimulation experiment from day 1 to day 5 in the continuous repeated threat stimulation experiment. Specifically, day 1 to day 5 represent 5 consecutive days. Figure 5 and Figure 6 The red circle in the figure indicates that the adaptive behavior result of the individual to be identified is adaptive, the blue circle indicates that the adaptive behavior result of the individual to be identified is non-adaptive, "F" indicates that the gender of the individual to be identified is female, "M" indicates that the gender of the individual to be identified is male, the number after "F" or "M" indicates the number of the individual to be identified, "A" indicates the recognition result of the adaptive behavior determined based on five behavioral characteristic parameters, including the number of escapes, escape ratio, average escape reaction latency, average escape speed, and average escape return time, "B" indicates the recognition result of the adaptive behavior determined based on 14 behavioral characteristic parameters, including action conversion frequency and action proportion dataset, among which the action proportion dataset contains 13 types of behavioral action proportions.

[0086] exist Figure 5 In the repeated threat stimulus experiment on day 1, Figure 5 The results of adaptive behavior recognition of 26 individuals to be recognized are shown, including 14 female individuals to be recognized and 12 male individuals to be recognized. Figure 5 The results of adaptive behavior recognition of 30 individuals to be recognized are shown, including 14 female individuals to be recognized and 16 male individuals to be recognized. Figure 5 The identification results of the adaptive behaviors of 31 individuals to be identified are shown, including 15 female individuals to be identified and 16 male individuals to be identified.

[0087] exist Figure 6 In the repeated threat stimulus experiment on the 4th day, Figure 6 The results of adaptive behavior recognition of 32 individuals to be recognized are shown, including 16 female individuals to be recognized and 16 male individuals to be recognized. Figure 6 The identification results of the adaptive behaviors of 28 individuals to be identified are shown, including 14 female individuals to be identified and 14 male individuals to be identified.

[0088] pass Figure 5 and Figure 6 It can be seen from the recognition results that in the continuous repeated threat stimulation experiment, with the continuous execution of the repeated threat stimulation experiment, the proportion of adaptive individuals to be identified in the biological population becomes higher and higher, and the consistency of the recognition results of adaptive behavior obtained based on two different behavioral characteristic data reaches more than 95%, which proves the feasibility of the two behavioral characteristic data.

[0089] Furthermore, through Figure 5 and Figure 6 The recognition results of the adaptive behavior shown in are obtained by regular statistical analysis, and the escape ratio threshold for distinguishing the adaptive type from the non-adaptive type is 50%.

[0090] The following is an embodiment of an adaptive behavior recognition device provided in an embodiment of the present invention. The device and the adaptive behavior recognition method of the above-mentioned embodiment belong to the same inventive concept. For details not described in detail in the embodiment of the adaptive behavior recognition device, reference can be made to the contents of the adaptive behavior recognition method in the above-mentioned embodiment.

[0091] Figure 7 FIG. 1 is a schematic diagram of a structure of an adaptive behavior recognition device provided by an embodiment of the present invention. Figure 7 As shown, the device includes: a behavior feature data acquisition module 310, a cluster set determination module 320 and an adaptive behavior result determination module 330.

[0092] The behavior characteristic data acquisition module 310 is used to acquire behavior characteristic data corresponding to at least two individuals to be identified; wherein the behavior characteristic data represents statistical behavior information exhibited by the individuals to be identified in response to repetitive threat stimulus signals in a repeated threat stimulus experiment;

[0093] A cluster set determination module 320, configured to cluster at least two individuals to be identified according to at least two behavioral characteristic data to obtain at least two cluster sets;

[0094] The adaptive behavior result determination module 330 is used to determine the adaptive behavior result corresponding to each to-be-identified individual in each cluster set according to the cluster index values ​​corresponding to at least two cluster sets.

[0095] The technical solution of this embodiment is to cluster at least two individuals to be identified according to the behavioral characteristic data respectively corresponding to the at least two individuals to be identified to obtain at least two cluster sets, and determine the adaptive behavior results corresponding to each individual to be identified in each cluster set according to the cluster index values ​​respectively corresponding to the at least two cluster sets, wherein the behavioral characteristic data represents the statistical behavioral information exhibited by the individual to be identified in response to repetitive threat stimulus signals in the repeated threat stimulus experiment, thereby solving the problem of adaptive behavior relying on subjective judgment and improving the recognition accuracy and efficiency of the adaptive behavior of biological individuals in repetitive threat stimulus scenarios.

[0096] In an optional embodiment, the adaptive behavior result determination module 330 includes:

[0097] A comparison result determination unit, used to compare the clustering index values ​​corresponding to at least two clustering sets to obtain a comparison result;

[0098] The adaptive behavior result determination unit is used to determine the adaptive behavior result corresponding to each to-be-identified individual in each cluster set according to the comparison result.

[0099] In an optional embodiment, the adaptive behavior result determination unit is specifically configured to:

[0100] When the clustering index value is a statistical value corresponding to the escape ratio, and the number of cluster sets is two, the adaptive behavior results of at least one individual to be identified in the cluster set with a larger clustering index value in the comparison result are all taken as the adaptive type;

[0101] The adaptive behavior results of at least one individual to be identified in the cluster set with a smaller cluster index value in the comparison result are all regarded as the maladaptive type.

[0102] In an optional embodiment, the cluster set determination module 320 is specifically configured to:

[0103] For each individual to be identified, the behavior characteristic data corresponding to the individual to be identified is subjected to dimensionality reduction processing to obtain the behavior characteristic data after dimensionality reduction processing;

[0104] At least two individuals to be identified are clustered according to at least two behavioral characteristic data after dimensionality reduction processing to obtain at least two cluster sets.

[0105] In an optional embodiment, the behavioral characteristic data includes at least two of the following: number of escapes, escape ratio, average escape reaction latency, average escape speed, and average escape return time.

[0106] In an optional embodiment, when the individual to be identified is a rodent, the behavioral characteristic data includes an action conversion frequency and an action proportion data set. The action proportion data set represents the ratio information between the behavioral action duration and the escape duration of the individual to be identified. The escape duration represents the length of time from the start of the stimulation of the threatening stimulus signal to the end of the escape of the individual to be identified within a single stimulation cycle. The action proportion data set includes at least one of the flying action proportion, the running action proportion, the trotting action proportion, the walking action proportion, the left turn action proportion, the right turn action proportion, the stepping action proportion, the climbing action proportion, the sniffing action proportion, the grooming action proportion, the pause action proportion, the freezing behavior proportion and the jumping behavior proportion.

[0107] In an optional embodiment, the repeated threat stimulation experiment is included in the continuous repeated threat stimulation experiment, and the device further includes:

[0108] The continuous repetitive threat stimulation experimental paradigm module is used to use a timing device to perform timing operations in the preparation period before the continuous repetitive threat stimulation experiment begins, so as to make the individual to be identified adapt to the experimental box;

[0109] During the process of continuously repeating the threat stimulation experiment, for each repeated threat stimulation experiment, a timing device is used to perform a timing operation so that the individual to be identified can adapt to the experimental box, and after the timing ends, in response to detecting that the individual to be identified is located in a preset stimulation area in the experimental box, the stimulation device is controlled to emit a threat stimulation signal;

[0110] Among them, the number of repeated threat stimulation experiments in the continuous repeated threat stimulation experiment is 5, the experimental cycle of the repeated threat stimulation experiment is 1 day, the number of times the threat stimulation signal is issued is greater than 1 time and less than the number threshold, the time interval between two adjacent threat stimulation signals is greater than the time interval threshold, and the experimental duration of the repeated threat stimulation experiment is less than the experimental duration threshold.

[0111] In an optional embodiment, the device further comprises:

[0112] An escape ratio threshold determination module, used for determining an escape ratio threshold according to the escape ratios and adaptive behavior results of at least two individuals to be identified when the number of cluster sets is two;

[0113] If the escape ratio of the target individual is greater than or equal to the escape ratio threshold, the adaptive behavior result of the target individual is set to adaptive;

[0114] If the escape ratio of the target individual is less than the escape ratio threshold, the adaptive behavior result of the target individual is set to maladaptive;

[0115] The target individual is a biological individual other than at least two individuals to be identified that participates in the repeated threat stimulation experiment.

[0116] The adaptive behavior recognition device provided in the embodiment of the present invention can execute the adaptive behavior recognition method provided in any embodiment of the present invention, and has the corresponding functional modules and beneficial effects of the execution method.

[0117] Figure 8 A schematic diagram of the structure of an electronic device provided for one embodiment of the present invention. The electronic device 10 is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, servers, blade servers, mainframe computers, and other suitable computers. The electronic device may also represent various forms of mobile devices, such as personal digital assistants, cellular phones, smart phones, wearable devices (such as helmets, glasses, watches, etc.) and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present invention described and / or required herein.

[0118] like Figure 8 As shown, the electronic device 10 includes at least one processor 11, and a memory connected to the at least one processor 11, such as a read-only memory (ROM) 12, a random access memory (RAM) 13, etc., wherein the memory stores a computer program that can be executed by at least one processor 11, and the processor 11 can perform various appropriate actions and processes according to the computer program stored in the ROM 12 or the computer program loaded from the storage unit 18 to the RAM 13. In the RAM 13, various programs and data required for the operation of the electronic device 10 can also be stored. The processor 11, the ROM 12, and the RAM 13 are connected to each other through a bus 14. The input / output (I / O) interface 15 is also connected to the bus 14.

[0119] A number of components in the electronic device 10 are connected to the I / O interface 15, including: an input unit 16, such as a keyboard, a mouse, etc.; an output unit 17, such as various types of displays, speakers, etc.; a storage unit 18, such as a disk, an optical disk, etc.; and a communication unit 19, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 19 allows the electronic device 10 to exchange information or data with other devices through a computer network such as the Internet and / or various telecommunication networks.

[0120] The processor 11 may be a variety of general and / or special processing components with processing and computing capabilities. Some examples of the processor 11 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various dedicated artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, digital signal processors (DSP), and any appropriate processors, controllers, microcontrollers, etc. The processor 11 executes the various methods and processes described above, such as the adaptive behavior recognition method provided in the above embodiments.

[0121] In some embodiments, the method for identifying adaptive behavior provided in the above embodiments may be implemented as a computer program, which is tangibly contained in a computer-readable storage medium, such as a storage unit 18. In some embodiments, part or all of the computer program may be loaded and / or installed on the electronic device 10 via the ROM 12 and / or the communication unit 19. When the computer program is loaded into the RAM 13 and executed by the processor 11, one or more steps in the method for identifying adaptive behavior described above may be performed. Alternatively, in other embodiments, the processor 11 may be configured to perform the method for identifying adaptive behavior in any other appropriate manner (e.g., by means of firmware).

[0122] Various embodiments of the systems and techniques described above herein may be implemented in the following systems or combinations thereof: digital electronic circuit systems, integrated circuit systems, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard parts (ASSPs), systems on chips (SOCs), complex programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments may include: being implemented in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which may be a special purpose or general purpose programmable processor that can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.

[0123] The computer program for implementing the adaptive behavior recognition method of the present invention can be written in any combination of one or more programming languages. These computer programs can be provided to a processor of a general-purpose computer, a special-purpose computer or other programmable data processing device, so that when the computer program is executed by the processor, the functions / operations specified in the flow chart and / or block diagram are implemented. The computer program can be executed entirely on the machine, partially on the machine, partially on the machine as a stand-alone software package and partially on a remote machine, or entirely on a remote machine or server.

[0124] In the context of the present application, a computer readable storage medium may be a tangible medium that may contain or store a computer program for use by or in conjunction with an instruction execution system, device or equipment. A computer readable storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices or equipment, or any suitable combination of the foregoing. Alternatively, a computer readable storage medium may be a machine readable storage medium. Examples of machine readable storage media may include electrical connections based on at least one line, portable computer disks, hard disks, random access memories (RAM), read-only memories (ROM), erasable programmable read-only memories (EPROM), flash memories, optical fibers, portable compact disc read-only memories (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the foregoing.

[0125] To provide interaction with a user, the systems and techniques described herein may be implemented on a terminal device having: a display device (e.g., a cathode ray tube (CRT) or a liquid crystal display (LCD) monitor) for displaying information to the user; and a keyboard and a pointing device (e.g., a mouse or a trackball), through which the user can provide input to the terminal device. Other types of devices may also provide interaction with the user; for example, the feedback provided to the user may be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form (including acoustic input, voice input, or tactile input).

[0126] The systems and techniques described herein may be implemented in a computing system that includes backend components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes frontend components (e.g., a user computer with a graphical user interface or a web browser through which a user can interact with implementations of the systems and techniques described herein), or a computing system that includes any combination of such backend components, middleware components, or frontend components. The components of the system may be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: a Local Area Network (LAN), a Wide Area Network (WAN), a blockchain network, and the Internet.

[0127] A computing system may include a client and a server. The client and the server are generally remote from each other and usually interact through a communication network. The relationship between the client and the server is generated by computer programs running on the corresponding computers and having a client-server relationship with each other. The server may be a cloud server, also known as a cloud computing server or a cloud host, which is a host product in the cloud computing service system to solve the defects of difficult management and weak business scalability in traditional physical hosts and virtual private servers (VPS) services.

[0128] It should be understood that the various forms of processes shown above can be used to reorder, add or delete steps. For example, the steps described in the present invention can be executed in parallel, sequentially or in different orders, as long as the desired results of the technical solution of the present invention can be achieved, and this document does not limit this.

[0129] The above specific implementations do not constitute a limitation on the protection scope of the present invention. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions can be made according to design requirements and other factors. Any modification, equivalent substitution and improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A method for identifying adaptive behavior, characterized in that: include: Obtaining behavioral characteristic data corresponding to at least two individuals to be identified, wherein the behavioral characteristic data represent statistical behavioral information exhibited by the individuals to be identified in response to repetitive threat stimulus signals in a repeated threat stimulus experiment; Clustering the at least two individuals to be identified to obtain at least two cluster sets according to the at least two behavioral characteristic data; According to the clustering index values ​​respectively corresponding to at least two clustering sets, the adaptive behavior results respectively corresponding to each to-be-identified individual in each clustering set are determined.

2. The method according to claim 1, characterized in that: The step of determining the adaptive behavior result corresponding to each to-be-identified individual in each cluster set according to the cluster index values ​​corresponding to the at least two cluster sets respectively includes: Comparing the clustering index values ​​corresponding to at least two clustering sets to obtain a comparison result; According to the comparison result, the adaptive behavior result corresponding to each to-be-identified individual in each cluster set is determined.

3. The method according to claim 2, characterized in that Determining the adaptive behavior results corresponding to each to-be-identified individual in each cluster according to the comparison result includes: When the clustering index value is a statistical value corresponding to the escape ratio, and the number of the clustering sets is two, the adaptive behavior results of at least one individual to be identified in the clustering set showing a larger clustering index value in the comparison result are all taken as the adaptive type; The adaptive behavior result of at least one individual to be identified in the cluster set showing a smaller cluster index value in the comparison result is regarded as the non-adaptive type.

4. The method according to any one of claims 1 to 3, characterized in that: The step of clustering the at least two individuals to be identified according to the at least two behavioral characteristic data to obtain at least two cluster sets includes: For each individual to be identified, performing dimensionality reduction processing on the behavior characteristic data corresponding to the individual to be identified to obtain the behavior characteristic data after dimensionality reduction processing; According to at least two behavioral characteristic data after dimensionality reduction processing, the at least two individuals to be identified are clustered to obtain at least two cluster sets.

5. The method according to claim 1, characterized in that The behavioral characteristic data include at least two of the number of escapes, the escape ratio, the average escape reaction latency, the average escape speed, and the average escape return time.

6. The method according to claim 1, characterized in that When the individual to be identified is a rodent, the behavioral characteristic data includes an action conversion frequency and an action proportion data set, the action proportion data set represents the ratio information between the behavioral action duration and the escape duration of the individual to be identified, the escape duration represents the time length from the stimulation start time of the threat stimulus signal to the escape end time of the individual to be identified within a single stimulation cycle, and the action proportion data set includes at least one of the flying action proportion, the running action proportion, the trotting action proportion, the walking action proportion, the left turn action proportion, the right turn action proportion, the stepping action proportion, the climbing action proportion, the sniffing action proportion, the grooming action proportion, the pause action proportion, the freezing behavior proportion and the jumping behavior proportion.

7. The method according to claim 1, characterized in that The repeated threat stimulation experiment is included in the continuous repeated threat stimulation experiment, and the experimental paradigm of the continuous repeated threat stimulation experiment includes: In the preparation period before the continuous repeated threat stimulation experiment begins, a timing device is used to perform a timing operation so that the individual to be identified can adapt to the experimental box; During the continuous repeated threat stimulation experiment, for each repeated threat stimulation experiment, a timing device is used to perform a timing operation so that the individual to be identified adapts to the experimental box, and after the timing ends, in response to detecting that the individual to be identified is located in a preset stimulation area in the experimental box, the stimulation device is controlled to emit a threat stimulation signal; Among them, the number of repeated threat stimulation experiments in the continuous repeated threat stimulation experiment is 5, the experimental period of the repeated threat stimulation experiment is 1 day, the number of times the threat stimulation signal is issued is greater than 1 time and less than the number threshold, the time interval between the issuance of two adjacent threat stimulation signals is greater than the time interval threshold, and the experimental duration of the repeated threat stimulation experiment is less than the experimental duration threshold.

8. The method according to claim 1, characterized in that The method further comprises: When the number of the cluster sets is two, determining an escape ratio threshold value according to the escape ratios and adaptive behavior results of at least two individuals to be identified; If the escape ratio of the target individual is greater than or equal to the escape ratio threshold, the adaptive behavior result of the target individual is set to adaptive; If the escape ratio of the target individual is less than the escape ratio threshold, the adaptive behavior result of the target individual is set to be maladaptive; Wherein, the target individual is a biological individual participating in the repeated threat stimulation experiment other than the at least two individuals to be identified.

9. An adaptive behavior recognition device, characterized in that: include: A behavior characteristic data acquisition module, used to acquire behavior characteristic data corresponding to at least two individuals to be identified; wherein the behavior characteristic data represents statistical behavior information exhibited by the individuals to be identified in response to repetitive threat stimulus signals in a repeated threat stimulus experiment; A cluster set determination module, used for clustering the at least two individuals to be identified to obtain at least two cluster sets according to at least two behavioral characteristic data; The adaptive behavior result determination module is used to determine the adaptive behavior result corresponding to each to-be-identified individual in each cluster set according to the cluster index values ​​corresponding to at least two cluster sets.

10. An electronic device, characterized in that: The electronic device comprises: at least one processor; and a memory communicatively connected to the at least one processor; wherein, The memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can perform the method for identifying adaptive behaviors according to any one of claims 1 to 8.

11. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a processor to implement the adaptive behavior recognition method according to any one of claims 1 to 8 when executed.

12. A computer program product, comprising a computer program, wherein when the computer program is executed by a processor, the computer program implements the method for identifying adaptive behaviors according to any one of claims 1 to 8.

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