A Customization Method for On-Site Investigation Simulation Training

Abstract

The present invention relates to the technical field of on-site investigation simulation training, and discloses a customization method for on-site investigation simulation training, including: collecting information of historical investigation sites, and generating a historical information data set for each historical investigation site; generating node information of the historical investigation site based on the historical information data set; establishing a matrix; updating matrix information based on the node information; setting the number of investigation sites to be simulated, and obtaining matrix information of the simulated sites based on the matrix information; setting investigation objects in the simulated sites based on the attribute values of the elements of the matrix of the simulated sites; The present invention synthesizes based on historical investigation site data to obtain the attribute information of the investigation objects of the simulated sites, and simulates a large number of historical investigation phenomena through a limited number of simulated sites, and the set simulated sites are more representative.

Description

Technical Field

[0001] The present invention relates to the field of on-site investigation simulation training, and more specifically, it relates to a customization method for on-site investigation simulation training. Background Art

[0002] Virtual simulation investigation training solves to a certain extent the problems of the current traditional on-site investigation teaching training, such as few training sites, long training cycles, high single-scene setting costs for case scenes, few equipment, and insufficient training flexibility. However, there is the same problem as traditional on-site investigation teaching. How to set the investigation objects in the simulated investigation scene to be more representative is a difficult problem. Summary of the Invention

[0003] The present invention provides a customization method for on-site investigation simulation training, which solves the technical problem in related technologies of how to set investigation objects to represent more on-site investigation scenes.

[0004] According to one aspect of the present invention, a customization method for on-site investigation simulation training is provided, including the following steps:

[0005] Step 101, collect information of historical on-site investigations, and generate a historical information data set for each historical on-site investigation;

[0006] Step 102, generate node information of the historical on-site investigation based on the historical information data set; the node information is associated with the investigation object, and the attribute information of the node information includes the size of the data volume of the investigation object, the number of evidences associated with the investigation object, the number of perpetrators associated with the investigation object, and the number of investigators associated with the investigation object;

[0007] Step 103, establish a matrix containing 15 or 16 elements, and the number of rows and columns of the matrix is greater than 3;

[0008] Step 104, randomly select node information with the same number as the number of elements of the matrix from the node information of a historical on-site investigation and randomly map it to the elements of the matrix, and assign the same attribute information as the mapped node information to each element;

[0009] Step 105, extract a node information from the node information of the historical on-site investigation, calculate the first distance parameter between the node information and the elements of the matrix, select the element with the smallest value of the first distance parameter, and mark the element and the elements in the adjacent grids of the element, and update the attribute values of the marked elements;

[0010] The update method includes:

[0011] Sequentially update the attribute values of the marked elements, and the update formula is as follows:

[0012] ui (t + 1)= u i (t)+ e 1-t (m i (t)- u i (t))

[0013] Where t represents the number of times step 105 is iteratively executed, and u i (t + 1) represents the value of the i-th attribute of the element after update, and u i (t) represents the value of the i-th attribute of the element before update, and m i (t) represents the value of the i-th attribute of the extracted node information;

[0014] Step 106, iteratively execute step 105 until all node information is selected;

[0015] Step 107, set the number k of the exploration sites to be simulated, and randomly select k historical exploration sites as the simulation sites;

[0016] Step 108, calculate the distance between the historical exploration site and the simulation site, match each simulation site with the historical exploration sites whose distance is less than the first distance threshold, and update the element matrix of the simulation site;

[0017] The method for updating the element matrix of the simulation site includes:

[0018]

[0019] Where M bv represents the value of the v-th attribute of the b-th element of the element matrix of the simulation site, s represents the number of historical exploration sites matched with the simulation site, and y bvx represents the value of the v-th attribute of the b-th element of the element matrix of the x-th historical exploration site matched with the simulation site;

[0020] Step 109, iteratively execute step 108, and the number of executions is f(k);

[0021] Step 110, set the exploration object in the simulation site based on the attribute values of the elements of the matrices of the k simulation sites obtained after step 109 ends.

[0022] Furthermore, the node information selected when executing step 105 is the node information that has not been selected in the previous iterative process.

[0023] Furthermore, the formula for calculating the first distance parameter between the node information and the elements of the matrix is as follows:

[0024]

[0025] Where x jThe value of the j-th attribute representing node information, y j Represents the value of the j-th attribute of an element of the matrix.

[0026] Furthermore, calculating the distance between the historical investigation site and the simulated site specifically involves calculating the distance between the element matrix of the historical investigation site and the element matrix of the simulated site.

[0027] The formula for calculating the distance between the element matrix of the historical investigation site and the element matrix of the simulated site is as follows:

[0028]

[0029] Where d c Represents the distance between the c-th element of the element matrix of the historical investigation site and the c-th element of the element matrix of the simulated site;

[0030]

[0031] Where h z Represents the value of the z-th attribute of the c-th element of the element matrix of the historical investigation site, a z Represents the value of the z-th attribute of the c-th element of the element matrix of the simulated site.

[0032] Furthermore, f(k) = k.

[0033] Furthermore, f(k) = k 2 -1.

[0034] Furthermore, the attribute value of the investigation object set in the simulated site is the same as or has a functional relationship with the attribute value of an element of the corresponding matrix of the simulated site.

[0035] The number of investigation objects set in the simulated site is greater than the number of elements of the corresponding matrix of the simulated site, and more than one investigation object corresponds to the same element.

[0036] The beneficial effects of the present invention are as follows:

[0037] The present invention obtains the attribute information of the investigation objects in the simulated site by synthesizing the historical investigation site data, and simulates a large number of historical investigation phenomena through a limited number of simulated sites, and the set simulated sites are more representative. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] Figure 1 Is the flowchart of a method for customizing on-site investigation simulation training of the present invention Figure 1 ;

[0039] Figure 2 Is the flowchart of a method for customizing on-site investigation simulation training of the present inventionFigure 2 。 Detailed implementation manners

[0040] The subject matter described herein will now be discussed with reference to exemplary embodiments. It should be understood that discussing these embodiments is only to enable those skilled in the art to better understand and thus implement the subject matter described herein, and is not a limitation on the scope of protection, applicability, or examples set forth in the claims. The functions and arrangements of the elements discussed can be changed without departing from the scope of protection of the content of this specification. Each example can omit, substitute, or add various processes or components as needed. Additionally, the features described for some examples can also be combined in other examples.

[0041] Embodiment 1

[0042] As Figure 1 - Figure 2 shown, a customization method for on-site investigation simulation training includes the following steps:

[0043] Step 101: Collect information on historical investigation sites and generate a historical information dataset for each historical investigation site;

[0044] Step 102: Generate node information of the historical investigation site based on the historical information dataset; the node information is associated with the investigation object, and the attribute information of the node information includes the size of the data volume of the investigation object, the number of evidences associated with the investigation object, the number of perpetrators associated with the investigation object, and the number of investigators associated with the investigation object;

[0045] Step 103: Establish a matrix containing 15 or 16 elements, where the number of rows and columns of the matrix is greater than 3;

[0046] Step 104: Randomly select node information with the same number as the number of elements of the matrix from the node information of a historical investigation site and randomly map it to the elements of the matrix, and assign the same attribute information as the mapped node information to each element;

[0047] Step 105: Extract a piece of node information from the node information of the historical investigation site, calculate the first distance parameter between this node information and the elements of the matrix, select the element with the smallest value of the first distance parameter, and mark this element and the elements in the adjacent grids, and update the attribute values of the marked elements;

[0048] The update method includes:

[0049] Update the attribute values of the marked elements in sequence, and the update formula is as follows:

[0050] u i (t + 1) = u i (t) + e 1-t (mi (t)-u i (t))

[0051] Where t represents the number of times step 105 is iteratively executed, and u i (t + 1) represents the value of the i-th attribute of the element after update, and u i (t) represents the value of the i-th attribute of the element before update, and m i (t) represents the value of the i-th attribute of the extracted node information;

[0052] The formula for calculating the first distance parameter between the node information and the elements of the matrix is as follows:

[0053]

[0054] Where x j represents the value of the j-th attribute of the node information, and y j represents the value of the j-th attribute of the element of the matrix, and n is the number of attributes of the element.

[0055] Step 106, iteratively execute step 105 until all node information is selected;

[0056] In an embodiment of the present invention, the node information selected when executing step 105 is the node information that was not selected in the previous iteration process;

[0057] Step 107, set the number k of the exploration sites to be simulated, and randomly select k historical exploration sites as the simulated sites;

[0058] Step 108, calculate the distance between the historical exploration site and the simulated site, match a historical exploration site with a distance less than the first distance threshold for each simulated site, and update the element matrix of the simulated site;

[0059] Specifically, calculating the distance between the historical exploration site and the simulated site is to calculate the distance between the element matrix of the historical exploration site and the element matrix of the simulated site;

[0060] The calculation formula is as follows:

[0061]

[0062] Where d c represents the distance between the element matrix of the historical exploration site and the c-th element of the element matrix of the simulated site, and c is the number of elements of the matrix;

[0063]

[0064] Where h z represents the value of the z-th attribute of the c-th element of the element matrix of the historical exploration site, and az represents the value of the z-th attribute of the c-th element of the element matrix of the simulated scene, and n is the number of attributes of the element;

[0065] A method for updating the element matrix of the simulated scene includes:

[0066]

[0067] where M bv represents the value of the v-th attribute of the b-th element of the element matrix of the simulated scene, s represents the number of historical exploration scenes matching the simulated scene, and y bvx represents the value of the v-th attribute of the b-th element of the element matrix of the x-th historical exploration scene matching the simulated scene;

[0068] Step 109, iteratively execute Step 108, and the number of executions is f(k).

[0069] In an embodiment of the present invention, f(k) = k.

[0070] In an embodiment of the present invention, f(k) = k 2 -1.

[0071] Step 110, set the exploration object in the simulated scene based on the attribute values of the elements of the matrices of the k simulated scenes obtained after the end of Step 109.

[0072] In an embodiment of the present invention, the attribute value of the exploration object set in the simulated scene is the same as or has a functional relationship with the attribute value of an element of the corresponding matrix of the simulated scene;

[0073] In an embodiment of the present invention, the number of exploration objects set in the simulated scene is greater than the number of elements of the matrix of the corresponding simulated scene, and more than one exploration object corresponds to the same element.

[0074] The above describes the embodiments of this embodiment, but this embodiment is not limited to the above specific implementation manners. The above specific implementation manners are merely illustrative and not restrictive. Under the inspiration of this embodiment, those of ordinary skill in the art can also make many forms without departing from the purpose of this embodiment and the scope protected by the claims, and all belong to the protection scope of this embodiment.

Claims

1. A customization method for on-site investigation simulation training, characterized in that, It includes the following steps: Step 101, collect the information of historical investigation scenes, and generate a historical information dataset for each historical investigation scene; Step 102, generate the node information of the historical investigation scene based on the historical information dataset; the node information is associated with the investigation object, and the attribute information of the node information includes the size of the data volume of the investigation object, the number of evidences associated with the investigation object, the number of perpetrators associated with the investigation object, and the number of investigators associated with the investigation object; Step 103, establish a matrix containing 15 or 16 elements, and the number of rows and columns of the matrix is greater than 3; Step 104, randomly select the same number of node information as the number of elements of the matrix from the node information of a historical investigation scene and randomly map it to the elements of the matrix, and assign the same attribute information as the mapped node information to each element; Step 105, extract a node information from the node information of the historical investigation scene, calculate the first distance parameter between the node information and the elements of the matrix, select the element with the smallest value of the first distance parameter, and mark the element and the elements in the adjacent grids of the element, and update the attribute value of the marked element; The update method includes: Update the attribute values of the marked elements in sequence, and the update formula is as follows: u i (t + 1)= u i (t)+ e 1-t (m i (t)- u i (t)) where t represents the number of times the step 105 is iteratively executed, and u i (t + 1) represents the value of the i-th attribute of the element after update, and u i (t) represents the value of the i-th attribute of the element before update, and m i (t) represents the value of the i-th attribute of the extracted node information; Step 106, iteratively execute Step 105 until all node information is selected; Step 107, set the number k of investigation scenes to be simulated, and randomly select k historical investigation scenes as simulated scenes; Step 108, calculate the distance between the historical investigation scene and the simulated scene, match each simulated scene with the historical investigation scene whose distance is less than the first distance threshold, and update the element matrix of the simulated scene; The method for updating the element matrix of the simulated scene includes: where M bv represents the value of the v-th attribute of the b-th element of the element matrix of the simulated scene, s represents the number of historical investigation scenes matching the simulated scene, y bvx represents the value of the v-th attribute of the b-th element of the element matrix of the x-th historical investigation scene matching the simulated scene; Step 109, iteratively execute Step 108, and the number of executions is f(k); Step 110, set the investigation object in the simulated scene based on the attribute values of the elements of the matrix of the k simulated scenes obtained after Step 109 ends.

2. The customization method of on-site investigation simulation training according to claim 1, characterized in that The node information selected when executing Step 105 is the node information that has not been selected in the previous iteration process.

3. A customization method for on-site investigation simulation training according to claim 1, characterized in that, The formula for calculating the first distance parameter between the node information and the elements of the matrix is as follows: where x j represents the value of the j-th attribute of the node information, and y j represents the value of the j-th attribute of the element of the matrix.

4. A customization method for on-site investigation simulation training according to claim 1, characterized in that, Specifically, calculating the distance between the historical investigation scene and the simulated scene is to calculate the distance between the element matrix of the historical investigation scene and the element matrix of the simulated scene.

5. A customization method for on-site investigation simulation training according to claim 4, characterized in that The formula for calculating the distance between the element matrix of the historical investigation scene and the element matrix of the simulated scene is as follows: where d c represents the distance between the c-th element of the element matrix of the historical exploration site and the element matrix of the simulated site; where h z represents the value of the z-th attribute of the c-th element of the element matrix of the historical survey site, and a z represents the value of the z-th attribute of the c-th element of the element matrix of the simulated site.

6. A customization method for on-site investigation simulation training according to claim 1, characterized in that, f(k)=k.

7. A customization method for on-site investigation simulation training according to claim 1, characterized in that f(k) = k 2 -1.

8. A customization method for on-site investigation simulation training according to claim 1, characterized in that The attribute value of the investigation object set in the simulated scene is the same as or has a functional relationship with the attribute value of an element of the corresponding matrix of the simulated scene.

9. A customization method for on-site investigation simulation training according to claim 1, characterized in that, The number of investigation objects set in the simulated scene is greater than the number of elements of the matrix of the corresponding simulated scene, and more than one investigation object corresponds to the same element.

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