Target Personnel Determination Method, Device, Equipment and Storage Medium

Abstract

An embodiment of the present application discloses a method, apparatus, device, and storage medium for determining a target person. The method includes: determining an involved area according to historical case data, and determining a first set of suspicious persons who appear in the involved area; scoring the first set of suspicious persons according to the historical behavior data of the first set of suspicious persons to obtain a first scoring result; determining a second set of suspicious persons associated with the involved network information according to the obtained involved network information; scoring the second set of suspicious persons according to the involved network information to obtain a second scoring result; and determining a target person from the first set of suspicious persons and the second set of suspicious persons according to the first scoring result and the second scoring result. The above solution can more comprehensively and accurately lock the target set of suspicious persons by analyzing two different data sources, and achieve quantitative analysis through the scoring results to accurately screen out the target person.

Description

Technical Field

[0001] The embodiments of the present application relate to the field of ecology and public safety technology, and in particular, to a method, device, equipment and storage medium for determining a target person. Background Art

[0002] At present, in order to prevent the hunting and trading of wild animals, on the one hand, anti-hunting monitoring is carried out in wild animal habitats, and at the same time, wild animal trading is cracked down. Wild animal habitat monitoring mainly uses drones or cameras to capture and monitor over wild animal habitats, and once poachers are found, an alarm is issued. However, the visual range of this solution is limited, and suspects can only be locked in a small range, and it is impossible to carry out comprehensive deployment of poaching and trading behaviors. In addition, this method is mostly applied to precious wild animals, while for other wild animals in ordinary mountains and forests, this method requires the deployment of a large number of monitoring equipment and takes a lot of time to play back and view, consuming a lot of manpower and material resources, and the monitoring effect is also very limited, with a certain delay, and the prevention effect is not obvious. Summary of the invention

[0003] The embodiments of the present invention provide a method, apparatus, device and storage medium for determining a target person, so as to comprehensively and accurately determine the target person without completely relying on monitoring equipment.

[0004] In one embodiment, the present application provides a method for determining a target person, the method comprising:

[0005] According to historical case data, determine the area involved in the case, and determine the first set of suspicious persons appearing in the area involved in the case;

[0006] Scoring the first suspicious person according to the historical behavior data of the first suspicious person in the first suspicious person set to obtain a first scoring result;

[0007] Determine, based on the acquired network information involved in the case, a second set of suspicious persons associated with the network information involved in the case;

[0008] Scoring the second suspicious person in the second suspicious person set according to the content of the network information involved in the case to obtain a second scoring result;

[0009] According to the first scoring result and the second scoring result, a target person is determined from the first suspicious person set and the second suspicious person set.

[0010] In another embodiment, the present application also provides a target person determination device, the device comprising:

[0011] The first set of suspicious person determination module is used to determine the involved area according to historical case data and determine the first set of suspicious persons who appear in the involved area;

[0012] The first scoring module is used to score the first suspicious persons in the first set of suspicious persons according to the historical behavior data of the first suspicious persons in the first set of suspicious persons, and obtain the first scoring result;

[0013] The second set of suspicious person determination module is used to determine the second set of suspicious persons associated with the involved network information according to the obtained involved network information;

[0014] The second scoring module is used to score the second suspicious persons in the second set of suspicious persons according to the content of the involved network information, and obtain the second scoring result;

[0015] The target person determination module is used to determine the target person from the first set of suspicious persons and the second set of suspicious persons according to the first scoring result and the second scoring result.

[0016] In another embodiment, the embodiment of the present application further provides a target person determination device, including: one or more processors;

[0017] A memory for storing one or more programs;

[0018] When the one or more programs are executed by the one or more processors, the one or more processors implement the target person determination method according to any one of the embodiments of the present application.

[0019] In still another embodiment, the embodiment of the present application further provides a computer-readable storage medium, on which a computer program is stored, and when the program is executed by a processor, it implements the target person determination method according to any one of the embodiments of the present application.

[0020] In the embodiments of the present application, the involved area is determined based on historical case data, and the first set of suspicious persons appearing in the involved area is determined; according to the historical behavior data of the first suspicious persons in the first set of suspicious persons, the first suspicious persons are scored to obtain a first scoring result. The relevant first set of suspicious persons is locked in a grid-like manner through the relevance of the involved areas in the historical case data, and the first suspicious persons are scored. The second set of suspicious persons associated with the involved network information is determined by obtaining the involved network information; the second suspicious persons in the second set of suspicious persons are scored according to the involved network information to obtain a second scoring result. The second set of suspicious persons is more comprehensively screened in combination with the involved network information and scored to obtain a scoring result. According to the first scoring result and the second scoring result, the target persons are determined from the first set of suspicious persons and the second set of suspicious persons, realizing comprehensive and accurate quantitative analysis and determining the target persons without completely relying on a large number of monitoring devices. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] Figure 1 It is a flowchart of a method for determining target persons provided by an embodiment of the present invention;

[0022] Figure 2 It is a flowchart of a method for determining target persons provided by another embodiment of the present invention;

[0023] Figure 3 It is a schematic diagram for determining the first scoring result provided by another embodiment of the present invention;

[0024] Figure 4 It is a flowchart of a method for determining target persons provided by still another embodiment of the present invention;

[0025] Figure 5 It is a schematic diagram for determining the second scoring result provided by still another embodiment of the present invention;

[0026] Figure 6 It is a schematic structural diagram of a device for determining target persons provided by an embodiment of the present invention;

[0027] Figure 7 It is a schematic structural diagram of a device for determining target persons provided by an embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0028] The present invention will be further described in detail below with reference to the drawings and embodiments. It can be understood that the specific embodiments described herein are only used to explain the present invention, rather than limiting the present invention. In addition, it should be noted that for the sake of description, only parts related to the present invention are shown in the drawings, rather than all the structures.

[0029] Figure 1The flowchart of the target person determination method provided by an embodiment of the present invention. The target person determination method provided by this embodiment is applicable to the situation of determining the target person. Typically, this method can be applicable to the situation of determining the set of target suspects through two different data sources, and then determining the target person according to the scores of the suspects. Specifically, this method can be executed by a target person determination device, which can be implemented in the form of software and / or hardware, or integrated in a target person determination device. Refer to Figure 1 , the method of the embodiment of the present application specifically includes:

[0030] S110. According to the historical case data, determine the involved area, and determine the first set of suspicious persons who appear in the involved area.

[0031] The embodiment of the present application can be used to determine the suspects of hunting or trading wild animals, and then investigate the suspects. The historical case data can be the relevant data of the wild animal hunting or trading cases that have been solved, including but not limited to case types, event types, time and location, and animal types, etc. The case type can include animal-related cases, the event type can include animal hunting or animal trading, the event location can include the location name and longitude and latitude, and the animal type can be the specific name of the animal. The involved area is the area where animal hunting or trading behaviors may exist determined according to the historical case data. The first set of suspicious persons who appear in the involved area can be obtained from the monitoring data of the monitoring devices set in the involved area.

[0032] Specifically, according to the historical case data, by using data statistics or other methods, determine the areas where animal hunting or trading behaviors are frequent, and can infer the areas where the hunted or traded animals often appear through the habitats of the relevant animals recorded in the animal list library. Determine the personnel who appear in the above areas according to the monitoring devices, and form the first set of suspicious persons.

[0033] In the embodiment of the present application, an animal list library can be established in advance. For example, according to the input or imported information, establish a list library including information such as animal names and habitats to record the relevant information of animals. The animal list library can be updated regularly according to the historical case data. For example, the hunting locations in the historical case data can be added as the habitats corresponding to the animals in the animal list library.

[0034] S120. According to the historical behavior data of the first suspicious person in the first set of suspicious persons, score the first suspicious person to obtain a first scoring result.

[0035] Among them, the historical behavior data can be the relevant data of the behaviors done by the first suspicious person in the past, such as the specific content, time and location of the behavior, etc. The historical behavior data is used to infer the possibility that the first suspicious person has animal hunting or trading behaviors, etc.

[0036] In the embodiment of the present application, there may be multiple historical behavior data. Based on the specific content of the first suspicious person's historical behavior data and the amount of historical behavior data available, the possibility that the first suspicious person has engaged in animal hunting or animal trading can be scored to obtain a first scoring result. The higher the score of the first scoring result, the greater the possibility that the first suspicious person has engaged in animal hunting or animal trading. For example, if the first suspicious person has appeared in the area involved in the case m times, the corresponding score is f1; if the first suspicious person has appeared in the area involved in the case n times, the corresponding score is f2. If m <n,则f1<f2。若第一可疑人涉案区域中出现过m次,且m次出现的时间间隔都小于预设时间间隔,例如小于1天,则累加对应分值f3,即第一可疑人的第一评分结果的评分值为f1+f3。

[0037] S130. Determine, based on the acquired network information involved in the case, a second set of suspicious persons associated with the network information involved in the case.

[0038] Among them, the network information involved in the case can be information crawled from the Internet, such as articles, posts and pictures crawled from Weibo, Tieba, and e-commerce platforms. When crawling the network information involved in the case, filtering rules can be set in advance, such as crawling network information containing common animal hunting and trading terms and keywords such as "buy and sell", "hunt", and "trade" as the network information involved in the case. Filter out network information related to animal hunting or animal trading cases, and then lock in the associated second suspicious person set based on the network information involved in the case. The second suspicious person set associated with the network information involved in the case can be determined by the publisher, author, merchant or personnel information of the network information involved in the case.

[0039] S140: Score the second suspicious person in the second suspicious person set according to the content of the network information involved in the case to obtain a second scoring result.

[0040] Exemplarily, for the second suspect in the initially determined second set of suspects, the possibility of the second suspect having animal hunting or animal trading behavior can be scored based on the corresponding involved network information content to obtain a second scoring result. Analyze the involved network information content corresponding to the second suspect to determine whether the text and pictures therein contain content related to animal hunting and trading, and determine the second scoring result of the second suspect according to the analysis result. The higher the score value of the second scoring result, the greater the possibility that the second suspect has animal hunting or trading behavior. For example, if the involved network information content corresponding to the second suspect includes wild animal organs or wild animal containers, and the text contains words such as buying, selling, trading, price, etc., it is determined that the score value of the second scoring result of the second suspect is relatively high. For example, if the involved network information content corresponding to the second suspect includes wild animal organs, the corresponding score f4 is accumulated. If the involved network information content corresponding to the second suspect includes wild animal containers, the corresponding score f5 is accumulated. If the text of the involved network information content corresponding to the second suspect includes words such as buying, selling, trading, price, etc., the corresponding score f6 is accumulated. For example, if the involved network information content corresponding to the locked second suspect includes wild animal organs and the text includes words such as buying, selling, trading, price, etc., the score value of the second scoring result of the second suspect is f4 + f6.

[0041] S150. Determine target personnel from the first set of suspects and the second set of suspects according to the first scoring result and the second scoring result.

[0042] Exemplarily, the personnel in the first set of suspects and the second set of suspects may all be personnel with animal hunting and animal trading behavior. The first set of suspects and the second set of suspects can be merged to obtain a target set of suspects, and the suspects in the target set of suspects are analyzed according to the first scoring result and the second scoring result to determine the target personnel. In the embodiment of the present application, the merging is to take the union of the first set of suspects and the second set of suspects.

[0043] Specifically, the first scoring result of the first suspect and the second scoring result of the second suspect can be processed. For example, the scoring results are both converted to a full score of 100 points system, so that the first scoring result of the first suspect and the second scoring result of the second suspect have the same scoring standard. The first suspect and the second suspect in the target set of suspects can be sorted in descending order according to the scoring results, and the suspects ranked in the front are selected as the target personnel. It is also possible to use the suspects with scoring results higher than the preset scoring threshold as the target personnel.

[0044] It should be noted that the execution order of S110, S120 and S130, S140 in the embodiments of the present application is not specifically limited. S110 and S120 can be executed first, and then S130 and S140 can be executed. Or S130 and S140 can be executed first, and then S110 and S120 can be executed.

[0045] In the embodiments of the present application, through historical case data, the involved area is determined, and the first set of suspicious persons appearing in the involved area is determined; according to the historical behavior data of the first suspicious persons in the first set of suspicious persons, the first suspicious persons are scored to obtain a first scoring result. Through the relevance of the involved areas in the historical case data, the relevant first set of suspicious persons is locked in a grid-like manner, and the first suspicious persons are scored. By determining the second set of suspicious persons associated with the involved network information according to the obtained involved network information; scoring the second suspicious persons in the second set of suspicious persons according to the content of the involved network information to obtain a second scoring result, combining the involved network information, screening the second set of suspicious persons more comprehensively, and scoring to obtain a second scoring result. According to the first scoring result and the second scoring result, the target persons are determined from the first set of suspicious persons and the second set of suspicious persons, realizing comprehensive and accurate quantitative analysis and determining the target persons without relying entirely on a large number of monitoring devices. In addition, compared with the method of obtaining clues through passive public reports and surprise inspections, the solution of the embodiments of the present application can actively obtain the target persons and provide them to the police for investigation, giving the police the initiative in cracking down on the illegal capture and trafficking of wild animals.

[0046] Figure 2 It is a flowchart of a method for determining target persons provided in another embodiment of the present invention. The embodiments of the present application optimize S110 and S120 on the basis of the above embodiments. Details not described in detail in this embodiment can be found in the above embodiments. Refer to Figure 2 , the method for determining target persons provided in this embodiment may include:

[0047] S201. Determine the target animal hunting locations where the number of occurrences is greater than a first preset number in the animal hunting locations of the historical case data.

[0048] Among them, the first preset number can be set according to the actual situation. If the number of occurrences of a certain animal hunting location in the historical case data is large, it indicates that the possibility of illegal animal hunting and animal trading behaviors occurring at this animal hunting location is relatively high. Therefore, this animal hunting location is used as the target animal hunting location to focus on the situation at this location and its vicinity.

[0049] S202. Determine the target animals where the number of occurrences is greater than a second preset number in the animals of the historical case data, and determine the habitats of the target animals.

[0050] Among them, the second preset number of times can be set according to the actual situation. If the number of times a certain type of animal appears in the historical case data is relatively large, it indicates that this animal is more likely to be used as an object of hunting and trading. There is a high probability that hunting and trading of this animal will occur in its habitat, and it is necessary to focus on the habitat location of this animal and the surrounding situation.

[0051] S203. Determine the animal hunting suspect area according to the hunting location of the target animal and the habitat location of the target animal.

[0052] Exemplarily, the hunting location of the target animal and the surrounding area, as well as the habitat location and the surrounding area, can be used as the animal hunting suspect area, and key attention can be paid to the animal hunting suspect area to monitor the situation in the animal hunting suspect area.

[0053] S204. Determine the target trading locations in the animal trading locations of the historical case data where the number of occurrences is greater than the third preset number of times.

[0054] Among them, the third preset number of times can be set according to the actual situation. If there is any animal trading location where the number of times it appears in the historical case data is relatively large, it indicates that multiple animal trading behaviors have occurred at this animal trading location. Then, it is very likely that animal trading behaviors will occur again at this animal trading location, and the situation of this animal trading location and the surrounding area should be focused on.

[0055] S205. Determine the label data of the target trading location on the map.

[0056] Exemplarily, the label data is a general description of the location, used to reflect the type of the location. For example, for the target trading location "XX Villa", the label data determined from the map is a farmhouse.

[0057] S206. Determine the animal trading suspect area according to the target trading location and the suspicious trading locations with the label data within the first preset range of the target trading location.

[0058] Among them, the first preset range can be set according to the actual situation. For locations of the same type, such as locations with the same label data of a farmhouse nearby, it is very likely that there are also trading behaviors. Therefore, the locations with the same label data within the first preset range near the target trading location are used as suspicious trading locations, and the animal trading suspect area is determined based on the areas of the target trading location and the suspicious trading locations, so as to focus on the animal trading suspect area.

[0059] S207. Determine the first set of suspicious persons who appear in the animal hunting suspect area and / or the animal trading suspect area.

[0060] S208. Determine whether the first suspect has historical case-related data. If so, execute S209; if not, execute S210.

[0061] Among them, the historical case-related data includes those who appear in the white list, have been arrested, or have been identified as target persons. If the first suspect appears in the white list, it can be directly determined that the first suspect will not be involved in animal hunting and trading. If the first suspect has been arrested or has been identified as a target person, there is no need to analyze them as target persons anymore.

[0062] S209. Take the score corresponding to the historical case-related data as the first scoring result of the first suspect.

[0063] When the first suspect has historical case-related data, it can be directly determined whether to identify them as target persons. Therefore, the score corresponding to the historical case-related data can be directly assigned to the first suspect as their final score, indicating whether the first suspect is a target person. Among them, the corresponding relationship between historical case-related data and scores can be determined in advance. For example, as Figure 3 shown, the score corresponding to appearing in the white list is determined to be 0, and the score corresponding to having been arrested or having been identified as a target person is determined to be 100. When the historical case-related data of the first suspect is appearing in the white list, it can be determined that their score value is 0 and they are not regarded as target persons. When the historical case-related data of the first suspect is having been arrested or having been identified as a target person, it can be determined that their score value is 100 and they are directly identified as target persons.

[0064] S210. If the first suspect has at least one piece of historical suspicious behavior data, accumulate the scores corresponding to the at least one piece of historical suspicious behavior data as the first scoring result of the first suspect.

[0065] Among them, the historical suspicious behavior data includes: having historical case-related behaviors, the number of appearances in the animal hunting suspect area or the animal trading suspect area being greater than the fourth preset number, appearing in at least two sub-areas in the animal hunting suspect area or the animal trading suspect area, the number of appearances in both the animal hunting suspect area and the animal trading suspect area being greater than the fifth preset number, and the behavior track matching that of the already identified target person.

[0066] Among them, the fourth preset number and the fifth preset number can be set according to the actual situation. Specifically, the historical suspicious behavior data can correspond to corresponding scores. The corresponding relationship between historical suspicious behavior data and scores can be set according to the actual situation. The scores corresponding to each piece of historical suspicious behavior data can be the same or different. If the first suspect has multiple pieces of historical suspicious data, accumulate the scores corresponding to the multiple pieces of suspicious data. For example, asFigure 3 As shown, the score corresponding to the historical involved behavior is S1, the score corresponding to the number of appearances in the animal hunting suspected area or the animal trading suspected area being greater than the fourth preset number is S2, the score corresponding to the appearance in at least two sub-areas in the animal hunting suspected area or the animal trading suspected area is S3, the score corresponding to the number of appearances in both the animal hunting suspected area and the animal trading suspected area being greater than the fifth preset number and the behavior track is S4, and the score corresponding to the behavior track matching the behavior track of the determined target person is S5. If the first suspect has a historical involved behavior, and the number of appearances in the animal hunting suspected area or the animal trading suspected area is greater than the fourth preset number, and the behavior track matches the behavior track of the determined target person, then the score value of the first scoring result of the first suspect is S1 + S2 + S5. If the number of appearances of the first suspect in the animal hunting suspected area or the animal trading suspected area is greater than the fourth preset number, and the first suspect appears in at least two sub-areas in the animal hunting suspected area or the animal trading suspected area, and the number of appearances in both the animal hunting suspected area and the animal trading suspected area is greater than the fifth preset number, then the score value of the first scoring result of the first suspect is S2 + S3 + S4.

[0067] Through the technical solution of the embodiment of the present application, by determining the animal hunting suspected area and / or the animal trading suspected area according to the historical case data, the area where the behavior of the animal being hunted and traded is likely to occur is focused on, and the first suspect appearing in the suspected area is obtained comprehensively and accurately.

[0068] Figure 4 Figure 4 This is a flowchart of the target person determination method provided by another embodiment of the present invention. The embodiment of the present application optimizes S130 and S140 on the basis of the above embodiment. For details not described in detail in this embodiment, please refer to the above embodiment. Refer to Figure 4 , the target person determination method provided by this embodiment may include:

[0069] S310. Determine a second suspect set associated with the involved network information according to the obtained involved network information.

[0070] S320. Analyze at least one of the location information, text information, and picture information in the involved network information content of the second suspect to obtain at least one of a location score, a text score, and a picture score.

[0071] ​Exemplarily, the location information, text information, or picture information in the involved network information can intuitively and truly reflect the possibility that the second suspect may be involved in animal hunting or animal trading behaviors. Therefore, analyzing at least one of the location information, text information, and picture information in the involved network content can assist in analyzing whether the second suspect is involved in animal hunting or animal trading behaviors. By analyzing at least one of the location information, text information, and picture information, at least one of a location score, a text score, and a picture score is obtained, so as to quantitatively represent the analysis result obtained according to the content of the involved network information, and then intuitively and accurately determine whether the second suspect is involved in animal hunting or trading behaviors.

[0072] In an embodiment of the present application, analyzing the location information in the content of the involved network information of the second suspect to obtain a location score includes: if the content of the involved network information of the second suspect includes location information, determining whether there is an animal's habitat within a second preset range according to the location information, and determining a first location score La according to the judgment result, including L1 and L1'; if it is determined according to the location information that there is an animal's habitat within the second preset range, determining whether the animal type corresponding to the habitat is consistent with the animal type appearing in the content of the involved network information, and determining a second location score Lb according to the judgment result, including L2 and L2'; adding the first location score and the second location score to obtain the location score.

[0073] Exemplarily, if the involved network information obtained by crawling includes location information, the location information is analyzed. The location information may be the location where the involved network information is published, or the positioning shown by the involved network information, etc. The second preset range can be set according to the actual situation. If it is determined according to the location information whether there is an animal's habitat within the second preset range of the positioning, if there is an animal's habitat, it indicates that there may be a possibility that the second suspect hunts and trades animals, and the first location score is determined as L1. If there is no animal's habitat, the first location score is determined as L1', where L1' is less than L1, and L1' can be 0. If there is an animal's habitat within the second preset range, it is further determined whether the animal type corresponding to the habitat is consistent with the animal type appearing in the content of the involved network information. If they are consistent, it indicates that the second suspect has a greater possibility of hunting or trading the animal at this habitat. Therefore, the second location score is determined as L2. If they are inconsistent, the second location score is determined as L2', where L2' is less than L2, and L2' can be 0. According to the location information corresponding to the involved network information of the second suspect, the first location score and the second location score are determined and accumulated to obtain the location score. The beneficial effect of the above solution is that by combining the location information in the involved network information, the habitat and animal information associated with the location information can be accurately determined. Furthermore, when the location is close to the habitat and the animals in the involved network information match the animals in the habitat, the suspicion degree of the second suspect can be quantitatively analyzed to facilitate subsequent investigations.

[0074] In an embodiment of the present application, the text information in the content of the involved network information of the second suspect is analyzed, and the obtained text score includes: determining the similarity between the text information in the content of the involved network information and the preset speech text; according to the similarity, determining the text score Ta, including T1 and T1'.

[0075] Exemplarily, if the summary of the text information in the content of the involved network information of the second suspect contains common speech terms and keywords for animal hunting and trading, such as "buy and sell", "hunt", "trade", etc., it indicates that there is a greater possibility that the second suspect has animal hunting or trading behavior. The text information in the content of the involved network information is matched with the preset speech text for similarity. If the similarity is greater than the preset similarity threshold, the text score is set as T1. If the similarity is less than or equal to the preset similarity threshold, the text score is set as T1', where T1' is less than T1, and T1' can be 0. The beneficial effect of the above solution is that through the text, the content of the involved network information can be accurately and directly analyzed, and it can be effectively identified whether it is network information involving animal hunting or trading, which helps to determine the suspicion degree of the second suspect.

[0076] In an embodiment of the present application, analyzing the picture information in the involved network information content of the second suspect to obtain a picture score includes: determining whether the picture information includes wild animals, and determining a first picture score Pa according to the judgment result, including P1 and P1'; determining whether the picture information includes wild animal organs, and determining a second picture score Pb according to the judgment result, including P2 and P2'; determining whether the picture information includes wild animal containers, and determining a third picture score Pc according to the judgment result, including P3 and P3'; adding up the first picture score, the second picture score, and the third picture score to obtain the picture score. The wild animal containers mentioned here include cages, boxes, etc. that can be used to hold wild animals.

[0077] Exemplarily, as Figure 5 shown, by analyzing the picture information in the involved network information content, the possibility of the second suspect having animal hunting and trading behaviors is determined. If the picture information includes wild animals, it indicates that the second suspect has a greater possibility of having animal hunting or trading behaviors, and then the first picture score is determined as P1; if it does not include wild animals, the first picture score is determined as P1'. P1' is less than P1, and P1' can be 0. If the picture information includes wild animal organs, it indicates that the second suspect has a greater possibility of having animal hunting or trading behaviors, and then the second picture score is determined as P2; if it does not include wild animal organs, the second picture score is determined as P2'. P2' is less than P2, and P2' can be 0. If the picture information includes wild animal containers, it indicates that the second suspect has a greater possibility of having animal hunting or trading behaviors, and then the third picture score is determined as P3; if it does not include wild animal containers, the third picture score is determined as P3'. P3' is less than P3, and P3' can be 0. The beneficial effect of the above solution is that through the picture information, it can be directly inferred whether the involved network information contains content related to animal hunting and trading, which is convenient for determining the suspicion degree of the second suspect.

[0078] In an embodiment of the present application, it is also possible to analyze whether the second suspect has posted the involved network information multiple times, whether the picture information includes hunting tools, bloodstains, etc., so as to further determine the suspicion degree of the second suspect. Details are not listed one by one here. Any solution that can be analyzed based on the involved network information and quantitatively represented by scores is within the protection scope of the present application.

[0079] S330. Determine the second scoring result of the second suspect according to at least one of the location score, the text score, and the picture score.

[0080] Exemplarily, if at least two of the relevant scores of the second suspect include a location score, a text score, and a picture score, then at least two scores are added together to obtain the final second score result of the second suspect.

[0081] The technical solution of the embodiment of the present application analyzes at least one of the location information, text information, and picture information in the involved network information, and quantitatively represents the analysis result to determine the second score result of the second suspect, so as to comprehensively and accurately analyze the suspicion degree of the second suspect's behavior of animal hunting and trading, and based on the network data source for analysis, saving manpower and material resources.

[0082] Figure 6 It is a schematic structural diagram of a target person determination device provided by an embodiment of the present invention. This device is applicable to the situation of determining the target person. Typically, this method can be applicable to the situation of determining the set of target suspects through two different data sources, and then determining the target person according to the scores of the suspects. This device can be implemented in the form of software and / or hardware, or integrated in the target person determination device. See Figure 6 This device specifically includes:

[0083] The first suspect set determination module 410 is used to determine the involved area according to the historical case data, and determine the first suspect set that appears in the involved area;

[0084] The first scoring module 420 is used to score the first suspect according to the historical behavior data of the first suspect in the first suspect set to obtain the first score result;

[0085] The second suspect set determination module 430 is used to determine the second suspect set associated with the involved network information according to the obtained involved network information;

[0086] The second scoring module 440 is used to score the second suspect in the second suspect set according to the content of the involved network information to obtain the second score result;

[0087] The target person determination module 450 is used to determine the target person from the first suspect set and the second suspect set according to the first score result and the second score result.

[0088] In the embodiment of the present application, the involved area includes an animal hunting suspect area and / or an animal trading suspect area.

[0089] In the embodiment of the present application, the first suspect set determination module 410 includes:

[0090] A hunting location determination unit, configured to determine, among the animal hunting locations of the historical case data, a target animal hunting location where the occurrence times are greater than a first preset number of times;

[0091] A habitat location determination unit, configured to determine a target animal among the animals in the historical case data where the occurrence times are greater than a second preset number of times, and determine the habitat location of the target animal;

[0092] A hunting suspicion area determination unit, configured to determine the animal hunting suspicion area according to the target animal hunting location and the habitat location of the target animal.

[0093] In an embodiment of the present application, the first suspect set determination module 410 includes:

[0094] A trading location determination unit, configured to determine, among the animal trading locations of the historical case data, a target trading location where the occurrence times are greater than a third preset number of times;

[0095] A label data determination unit, configured to determine the label data of the target trading location on the map;

[0096] A trading suspicion area determination unit, configured to determine the animal trading suspicion area according to the target trading location and the suspicious trading locations with the label data within a first preset range of the target trading location.

[0097] In an embodiment of the present application, the first scoring module 420 includes:

[0098] A first scoring result determination unit, configured to, if the first suspect has historical case-related data, use the score corresponding to the historical case-related data as the first scoring result of the first suspect;

[0099] A second scoring result determination unit, configured to, if the first suspect does not have historical case-related data and the first suspect has at least one piece of historical suspicious behavior data, accumulate the scores corresponding to the at least one piece of historical suspicious behavior data as the first scoring result of the first suspect.

[0100] In an embodiment of the present application, the historical suspicious behavior data includes: having a historical case-related behavior, the occurrence times in the animal hunting suspicion area or the animal trading suspicion area being greater than a fourth preset number of times, appearing in at least two sub-areas in the animal hunting suspicion area or the animal trading suspicion area, the occurrence times in both the animal hunting suspicion area and the animal trading suspicion area being greater than a fifth preset number of times, and the behavior trajectory matching the behavior trajectory of a determined target person;

[0101] The historical case-related data includes: appearing in the white list, having been arrested, or having been determined as a target person.

[0102] The second scoring module 440 includes:

[0103] An analysis unit, configured to analyze at least one of location information, text information, and picture information in the involved network information content of the second suspect to obtain at least one of a location score, a text score, and a picture score;

[0104] A scoring unit, configured to determine a second scoring result of the second suspect according to at least one of the location score, the text score, and the picture score.

[0105] In an embodiment of the present application, the analysis unit includes:

[0106] A first location score determination subunit, configured to, if the involved network information content of the second suspect includes location information, determine whether there is an animal's habitat within a second preset range according to the location information, and determine a first location score according to the judgment result;

[0107] A second location score determination subunit, configured to, if it is determined that there is an animal's habitat within the second preset range according to the location information, determine whether the animal type corresponding to the habitat is consistent with the animal type appearing in the involved network information content, and determine a second location score according to the judgment result;

[0108] A location score sub-unit, configured to accumulate the first location score and the second location score to obtain the location score.

[0109] In an embodiment of the present application, the analysis unit includes:

[0110] A similarity determination sub-unit, configured to determine the similarity between the text information in the involved network information content and a preset speech text;

[0111] A text score determination sub-unit, configured to determine the text score according to the similarity.

[0112] In an embodiment of the present application, the analysis unit includes:

[0113] A first picture score determination sub-unit, configured to determine whether the picture information includes wild animals, and determine a first picture score according to the judgment result;

[0114] A second picture score determination sub-unit, configured to determine whether the picture information includes wild animal organs, and determine a second picture score according to the judgment result;

[0115] A third picture score determination sub-unit, configured to determine whether the picture information includes wild animal containers, and determine a third picture score according to the judgment result;

[0116] The picture scoring determination subunit is configured to accumulate the first picture score, the second picture score, and the third picture score to obtain the picture score.

[0117] The target person determination device provided in the embodiments of the present application can execute the target person determination method provided in any embodiment of the present application, and has corresponding functional modules and beneficial effects for executing the method.

[0118] Figure 7 It is a schematic structural diagram of a target person determination device provided in an embodiment of the present invention. Figure 7 A block diagram of an exemplary target person determination device 512 suitable for use in implementing the embodiments of the present application is shown. Figure 7 The shown target person determination device 512 is only an example and should not impose any limitation on the functions and usage scope of the embodiments of the present application.

[0119] As Figure 7 shown, the target person determination device 512 may include: one or more processors 516; a memory 528 for storing one or more programs, and when the one or more programs are executed by the one or more processors 516, the one or more processors 516 implement the target person determination method provided in the embodiments of the present application, including:

[0120] Determine the involved area according to historical case data, and determine a first set of suspicious persons appearing in the involved area;

[0121] Score the first suspicious person according to the historical behavior data of the first suspicious person in the first set of suspicious persons to obtain a first scoring result;

[0122] Determine a second set of suspicious persons associated with the involved network information according to the obtained involved network information;

[0123] Score the second suspicious person in the second set of suspicious persons according to the content of the involved network information to obtain a second scoring result;

[0124] Determine the target person from the first set of suspicious persons and the second set of suspicious persons according to the first scoring result and the second scoring result.

[0125] The components of the target person determination device 512 may include, but are not limited to: one or more processors or processors 516, a memory 528, and a bus 518 connecting different device components (including the memory 528 and the processors 516).

[0126] The bus 518 represents one or more of several types of bus architectures, including a memory bus or memory controller, a peripheral bus, an Accelerated Graphics Port, a processor bus, or a local bus using any of the several bus architectures. By way of example, and without limitation, these architectures include the Industry Standard Architecture (ISA) bus, Micro Channel Architecture (MAC) bus, Enhanced ISA bus, Video Electronics Standards Association (VESA) local bus, and Peripheral Component Interconnect (PCI) bus.

[0127] The target person determination device 512 typically includes a variety of computer device-readable storage media. These storage media can be any available storage media accessible by the target person determination device 512, including volatile and nonvolatile storage media, removable and non-removable storage media.

[0128] The memory 528 may include computer device-readable storage media in the form of volatile memory, such as random access memory (RAM) 530 and / or cache memory 532. The target person determination device 512 may further include other removable / non-removable, volatile / nonvolatile computer device storage media. By way of example only, a storage system 534 may be used for reading and writing on non-removable, nonvolatile magnetic storage media ( Figure 7 not shown, typically called a "hard disk drive"). Although Figure 7 not shown in the figure, a disk drive for reading and writing on removable nonvolatile disks (such as a "floppy disk"), and an optical disk drive for reading and writing on removable nonvolatile optical disks (such as a CD-ROM, DVD-ROM, or other optical storage media) may be provided. In these cases, each drive may be connected to the bus 518 via one or more data storage media interfaces. The memory 528 may include at least one program product having a set (e.g., at least one) of program modules that are configured to perform the functions of the various embodiments of the present invention.

[0129] A program / utility 540 having a set (at least one) of program modules 542 may be stored, for example, in the memory 528. Such program modules 542 include, but are not limited to, an operating device, one or more application programs, other program modules, and program data, each of these examples or some combination thereof may include an implementation of a network environment. The program modules 542 generally perform the functions and / or methods described in the embodiments of the present invention.

[0130] The target person determination device 512 can also communicate with one or more external devices 514 (such as a keyboard, a pointing device, a display 526, etc.), and can also communicate with one or more devices that enable a user to interact with the target person determination device 512, and / or communicate with any device that enables the target person determination device 512 to communicate with one or more other computing devices (such as a network card, a modem, etc.). Such communication can be carried out through an input / output (I / O) interface 522. Moreover, the target person determination device 512 can also communicate with one or more networks (such as a local area network (LAN), a wide area network (WAN), and / or a public network, such as the Internet) through a network adapter 520. As Figure 7 shown, the network adapter 520 communicates with other modules of the target person determination device 512 through a bus 518. It should be understood that although Figure 7 not shown in the figure, other hardware and / or software modules can be incorporated with the target person determination device 512, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID devices, tape drives, and data backup storage devices, etc.

[0131] The processor 516 executes various functional applications and data processing by running at least one of other programs stored in the memory 528, such as implementing a target person determination method provided by an embodiment of the present application.

[0132] An embodiment of the present invention provides a storage medium containing computer-executable instructions, and the computer-executable instructions are used to execute a target person determination method when executed by a computer processor, including:

[0133] Determine the involved area according to historical case data, and determine a first set of suspicious persons who appear in the involved area;

[0134] Score the first suspicious persons in the first set of suspicious persons according to the historical behavior data of the first suspicious persons in the first set of suspicious persons, and obtain a first scoring result;

[0135] Determine a second set of suspicious persons associated with the involved network information according to the obtained involved network information;

[0136] Score the second suspicious persons in the second set of suspicious persons according to the content of the involved network information, and obtain a second scoring result;

[0137] Determine the target person from the first set of suspicious persons and the second set of suspicious persons according to the first scoring result and the second scoring result.

[0138] The computer storage medium of the embodiments of the present application may adopt any combination of one or more computer-readable storage media. The computer-readable storage medium may be a computer-readable signal storage medium or a computer-readable storage medium. The computer-readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor device, apparatus, or component, or any combination of the above. More specific examples (non-exhaustive list) of the computer-readable storage medium include: an electrical connection having one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In the embodiments of the present application, the computer-readable storage medium may be any tangible storage medium that contains or stores a program, and the program may be used by or in conjunction with an instruction execution device, apparatus, or component.

[0139] The computer-readable signal storage medium may include a data signal propagated in a baseband or as part of a carrier wave, which carries the computer-readable program code. Such a propagated data signal may take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. The computer-readable signal storage medium may also be any computer-readable storage medium other than the computer-readable storage medium, which can send, propagate, or transmit a program for use by or in conjunction with an instruction execution device, apparatus, or component.

[0140] The program code contained on the computer-readable storage medium may be transmitted using any appropriate storage medium, including but not limited to wireless, wire, optical fiber cable, RF, etc., or any suitable combination of the above.

[0141] The computer program code for performing the operations of the present invention may be written in one or more programming languages or combinations thereof. The programming languages include object-oriented programming languages such as Java, Smalltalk, C++, and also include conventional procedural programming languages such as the "C" language or similar programming languages. The program code may be executed entirely on the user's computer, partially on the user's computer, executed as an independent software package, partially on the user's computer and partially on a remote computer, or entirely on the remote computer or device. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., by using an Internet service provider to connect through the Internet).

[0142] Note that the above is only a preferred embodiment of the present invention and the technical principles applied. Those skilled in the art will understand that the present invention is not limited to the specific embodiments described herein, and various obvious changes, re-adjustments and substitutions can be made by those skilled in the art without departing from the protection scope of the present invention. Therefore, although the present invention has been described in more detail through the above embodiments, the present invention is not limited to the above embodiments only. Without departing from the concept of the present invention, more other equivalent embodiments can be included, and the scope of the present invention is determined by the scope of the appended claims.

Claims

1. A method for determining a target person, characterized in that, The method includes: Determining the involved area according to historical case data, and determining a first set of suspicious persons who appear in the involved area; Scoring the first suspicious persons in the first set of suspicious persons according to the historical behavior data of the first suspicious persons in the first set of suspicious persons, to obtain a first scoring result; Determining a second set of suspicious persons associated with the involved network information according to the obtained involved network information; wherein, the involved network information includes at least one of location information, text information, and picture information; Scoring the second suspicious persons in the second set of suspicious persons according to the content of the involved network information, to obtain a second scoring result; Determining target persons from the first set of suspicious persons and the second set of suspicious persons according to the first scoring result and the second scoring result; Scoring the first suspicious persons in the first set of suspicious persons according to the historical behavior data of the first suspicious persons in the first set of suspicious persons, to obtain a first scoring result, including: If the first suspicious person has historical involved case data, then taking the score corresponding to the historical involved case data as the first scoring result of the first suspicious person; If the first suspicious person does not have historical involved case data, and the first suspicious person has at least one piece of historical suspicious behavior data, then accumulating the scores corresponding to the at least one piece of historical suspicious behavior data as the first scoring result of the first suspicious person.

2. The method according to claim 1, wherein The involved area includes an animal hunting suspicion area and / or an animal trading suspicion area.

3. The method according to claim 2, characterized in that, Determining the involved area according to historical case data, including: Determining a target animal hunting location in the animal hunting locations of the historical case data where the number of occurrences is greater than a first preset number of times; Determining a target animal in the animals of the historical case data where the number of occurrences is greater than a second preset number of times, and determining the habitat location of the target animal; Determining the animal hunting suspicion area according to the target animal hunting location and the habitat location of the target animal.

4. The method according to claim 2, characterized in that Determining the involved area according to historical case data, including: Determining a target trading location in the animal trading locations of the historical case data where the number of occurrences is greater than a third preset number of times; Determining the label data of the target trading location in the map; Determining the animal trading suspicion area according to the target trading location and the suspicious trading locations with the label data within a first preset range of the target trading location.

5. The method according to claim 1, wherein The historical suspicious behavior data includes: having historical involved case behavior, the number of occurrences in the animal hunting suspicion area or the animal trading suspicion area being greater than a fourth preset number of times, appearing in at least two sub-areas in the animal hunting suspicion area or the animal trading suspicion area, the number of occurrences in both the animal hunting suspicion area and the animal trading suspicion area being greater than a fifth preset number of times, and the behavior trajectory matching the behavior trajectory of the already determined target persons; The historical involved case data includes: appearing in the white list, having been arrested, or having been determined as a target person.

6. The method according to any one of claims 1-5, characterized in that, Scoring the second suspicious persons in the second set of suspicious persons according to the content of the involved network information, to obtain a second scoring result, including: Analyze at least one of the location information, text information, and picture information in the content of the network information involved in the case of the second suspicious person to obtain at least one of the location score, text score, and picture score; A second scoring result of the second suspicious person is determined according to at least one of the location score, the text score, and the picture score.

7. The method according to claim 6, characterized in that Analyze the location information in the content of the network information involved in the case of the second suspect to obtain a location score including: If the network information of the second suspect includes location information, determining whether there is an animal habitat within the second preset range according to the location information, and determining the first location score according to the determination result; If it is determined according to the location information that there is an animal habitat within the second preset range, then determining whether the animal type corresponding to the habitat is consistent with the animal type appearing in the content of the network information involved in the case, and determining the second location score according to the judgment result; The first position score and the second position score are accumulated to obtain the position score.

8. The method according to claim 6, characterized in that, The text information in the content of the network information involved in the case of the second suspect is analyzed to obtain a text score including: Determine the similarity between the text information in the content of the network information involved in the case and the preset speech text; The text score is determined according to the similarity.

9. The method according to claim 6, wherein Analyze the image information in the content of the network information involved in the case of the second suspicious person, and obtain the image scores including: Determine whether the image information includes wild animals, and determine the first image score according to the determination result; Determining whether the image information includes a wild animal organ, and determining a second image score according to the determination result; Determining whether the image information includes a wild animal container, and determining a third image score according to the determination result; The first picture score, the second picture score and the third picture score are accumulated to obtain the picture score.

10. A target person determination device, characterized in that, The device comprises: A first suspicious person set determination module, used to determine the area involved in the case based on historical case data, and determine the first suspicious person set appearing in the area involved in the case; A first scoring module, configured to score the first suspicious person in the first suspicious person set according to the historical behavior data of the first suspicious person, to obtain a first scoring result; A second suspicious person set determination module, used to determine a second suspicious person set associated with the network information involved in the case based on the acquired network information involved in the case; wherein the network information involved in the case includes at least one of location information, text information and picture information; A second scoring module, configured to score the second suspicious person in the second suspicious person set according to the content of the network information involved in the case, and obtain a second scoring result; A target person determination module, configured to determine a target person from the first suspicious person set and the second suspicious person set according to the first scoring result and the second scoring result; The first scoring module includes: A first scoring result determining unit, configured to, if the first suspicious person has historical case-related data, use the score corresponding to the historical case-related data as a first scoring result of the first suspicious person; The second scoring result determination unit is configured to, if there is no historical case-related data for the first suspect and there is at least one piece of historical suspicious behavior data for the first suspect, accumulate the scores corresponding to the at least one piece of historical suspicious behavior data as the first scoring result of the first suspect.

11. A target person determination device, characterized in that, The target person determination device includes: One or more processors; A memory for storing one or more programs; When the one or more programs are executed by the one or more processors, the one or more processors implement the target person determination method according to any one of claims 1-9.

12. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the program is executed by the processor, it implements the target person determination method according to any one of claims 1-9.

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