CN115116097A - Drug rehabilitation person relapse risk prediction method and device and readable storage medium - Google Patents

Abstract

The invention discloses a relapse risk prediction method, device and readable storage medium for drug addicts, and relates to the technical field of information processing. Wherein, the method includes: identifying the knuckle edge of the drug addict's knuckle in a preset area, and acquiring first information of the knuckle edge sliding in the preset area; wherein, the first information is that the knuckle edge is in the preset area When moving inside, the first space information collected at the first moment of movement and the second space information collected at the second moment; according to the first space information collected at the first moment and the second space collected at the second moment The comparison of the information confirms the movement state data of the phalangeal edge in the preset area; the comparison result is generated by the comparison between the movement state data and the preset state data, and the relapse risk is predicted for the drug addicts according to the comparison result. It solves the problems in the prior art that the process of taking a urine test for drug addicts registered in the system is complicated, the coverage of supervisors is small, and the problems of privacy are involved.

Description

Method, device and readable storage medium for predicting relapse risk of drug addicts

technical field

The invention relates to the technical field of information processing, and in particular, to a method, device and readable storage medium for predicting relapse risk of drug addicts.

Background technique

After drug addicts get rid of drug addiction, they need to accept the dynamic control of the "Drug Addicts Online Dynamic Control Early Warning System" to predict the risk of relapse of drug addicts based on the detection data of dynamic control. When drug addicts registered in the dynamic control and early warning system travel to other places to check in for accommodation, take flights or take high-speed trains, the local dynamic control and early warning system will immediately call the police based on the ID card information, prompting the local anti-drug police to dynamically supervise these people; at present, dynamic supervision usually Urine tests are performed on these individuals. For the detection of drug addicts' relapse status, the method of urine test is mainly used at present. Although the urine test method is accurate, the detection process is complicated, the coverage of supervisors is small, and issues such as personal privacy are involved. Therefore, it is necessary to improve the existing technology and propose a more reasonable technical solution.

SUMMARY OF THE INVENTION

In order to solve the problems in the prior art, the process of taking a urine test for drug addicts registered in the system is complicated, the coverage of supervisors is small, and privacy is involved.

In the first aspect, an embodiment of the present invention proposes a method for predicting relapse risk for drug addicts, including:

Identifying the knuckle edge of the drug addict's knuckle in the preset area, and acquiring first information about the knuckle edge sliding in the preset area; wherein, the first information is that when the knuckle edge moves in the preset area, the movement the first spatial information collected at the first moment and the second spatial information collected at the second moment;

According to the comparison between the first spatial information collected at the first moment and the second spatial information collected at the second moment, confirm the movement state data of the phalangeal edge within the preset area;

The comparison result is generated by comparing the mobile state data with the preset state data, and the relapse risk is predicted for the drug addicts according to the comparison result.

In a possible design, the step of confirming the movement state data of the phalangeal edge in the preset area includes: confirming the horizontal displacement and vertical displacement of the phalangeal edge in the preset area, and determining the horizontal displacement and vertical displacement according to the horizontal displacement and vertical displacement. Generate the movement trajectory and velocity trajectory of the knuckle edge.

In a possible design, the preset state data includes a preset motion track and a preset speed track, and the step generates a comparison result by comparing the movement state data with the preset state data, and the preset state data includes:

According to the comparison of the movement trajectory and movement data of the knuckle edge with the preset motion trajectory and the preset speed trajectory, respectively, the degree of movement correlation between the movement trajectory and the preset motion trajectory and the correlation between the movement data and the preset speed trajectory are obtained. Speed correlation.

In one possible design, the steps to predict relapse risk for drug addicts based on the comparison results include:

Determine whether the mobility relatedness and speed relatedness are within the range of the mobile relatedness threshold and the speed relatedness threshold, respectively. When the mobile relatedness is within the mobile relatedness threshold and the speed relatedness is within the speed related threshold, judge the drug addicts. not at risk of relapse;

When the mobility correlation degree is not within the mobility correlation degree threshold range and/or the speed correlation degree is not within the speed correlation degree threshold value range, it is determined that the drug addict is in a state of relapse risk.

In one possible design, prior to the step of identifying the knuckle margins of the drug addict's knuckles within the preset area, it also includes:

Obtain historical test data of drug addicts, obtain preset state data according to the historical test data, and the preset state data includes the dynamic running trajectory of the knuckle reference point.

In one possible design, prior to the step of identifying the knuckle margins of the drug addict's knuckles within the preset area, it also includes:

When it is detected that the knuckles of the drug addicts are in the preset area, the dynamic running track of the reference point of the knuckle is displayed, which is used for the drug addict to adjust the motion state of the edge of the knuckle according to the dynamic running track of the reference point of the knuckle.

In the second aspect, the embodiment of the present invention provides a device for predicting the risk of relapse of drug addicts, the device includes:

A knuckle edge data acquisition module, used for identifying the knuckle edge of a drug addict's knuckle in a preset area, and acquiring first information about the knuckle edge sliding in the preset area; wherein the first information is the knuckle edge When moving within the preset area, the first spatial information collected at the first moment of movement and the second spatial information collected at the second moment;

The phalangeal edge state module is used to confirm the movement state data of the phalangeal edge within the preset area according to the comparison between the first spatial information collected at the first moment and the second spatial information collected at the second moment;

The result comparison module is used for generating a comparison result by comparing the mobile state data with the preset state data, and predicting the relapse risk of the drug addicts according to the comparison result.

In one possible design, the device also includes:

The test data calling module is used to obtain historical test data of the relapsed person, and obtain preset state data according to the historical test data, and the preset state data includes the dynamic running track of the reference point of the knuckle.

In one possible design, the device also includes:

The display control module is used to display the dynamic running track of the reference point of the knuckle when the knuckle of the drug addict is detected to be in the preset area, so that the drug addict can adjust the motion state of the edge of the knuckle according to the dynamic running track of the knuckle reference point.

In a third aspect, embodiments of the present invention provide a computer-readable storage medium, where instructions are stored on the computer-readable storage medium, and when the instructions are run on a computer, the relapse risk prediction for drug addicts as proposed in the above embodiments is performed. method.

Beneficial effect: by identifying the knuckle edge of the drug addict's knuckle in the preset area, and obtaining the first information of the knuckle edge sliding in the preset area; wherein, the first information is that the knuckle edge is in the preset area When moving, the first spatial information collected at the first moment of movement and the second spatial information collected at the second moment; according to the first spatial information collected at the first moment and the second spatial information collected at the second moment The comparison of the phalangeal edge in the preset area is used to confirm the movement state data of the phalanx; the comparison result is generated by the comparison between the movement state data and the preset state data, and the relapse risk is predicted for the drug addicts according to the comparison result. It solves the problems in the prior art that the process of taking a urine test for drug addicts registered in the system is complicated, the coverage of supervisors is small, and privacy is involved.

Description of drawings

Various other advantages and benefits will become apparent to those of ordinary skill in the art upon reading the following detailed description of the preferred embodiments. The drawings are for the purpose of illustrating preferred embodiments only and are not to be considered limiting of the invention. Also, the same components are denoted by the same reference numerals throughout the drawings. In the attached image:

Fig. 1 is the flow chart of the method for predicting the risk of relapse of drug addicts proposed by the embodiment of the present invention;

2 is a schematic diagram of functional modules of a device for predicting relapse risk for drug addicts proposed by an embodiment of the present invention;

FIG. 3 is a schematic diagram of functional modules of another device for predicting relapse risk for drug addicts according to an embodiment of the present invention.

Detailed ways

In order to more clearly illustrate the embodiments of the invention or the technical solutions in the prior art, the invention will be briefly introduced below with reference to the accompanying drawings and descriptions of the embodiments or the prior art. Obviously, the following descriptions of the structures of the drawings are only For some embodiments of the invention, for those of ordinary skill in the art, other drawings can also be obtained according to these drawings without any creative effort. It should be noted here that the descriptions of these embodiments are used to help the understanding of the invention, but do not constitute a limitation on the invention.

In the first aspect, please refer to FIG. 1 to FIG. 3 , which are a method for predicting the relapse risk of drug addicts proposed in the embodiment of the present invention, which is applied to the relapse risk prediction device 100 for drug addicts. The device 100 for predicting relapse risk for drug addicts includes: But not limited to being executed by computer equipment with certain computing resources, such as personal computer (Personal Computer, PC, refers to a multi-purpose computer whose size, price and performance are suitable for personal use; desktop computers, notebook computers to small notebook computers and Tablets and ultrabooks belong to personal computers), smart phones, personal digital assistants (PADs), wearable devices or platform servers and other electronic devices to facilitate the detection of drug addicts after recognizing the knuckles of drug addicts. The movement dynamic data of the person's knuckles, and the movement dynamic data is compared with the preset state data. According to the correlation between the movement dynamic data and the preset state data, the knuckle movements of the drug addicts and the preset state data are judged. The correlation between the corresponding knuckle movements can obtain the current mental state of the drug addict, and then predict the relapse risk of the drug addict. This solves the problems in the prior art that the process of taking a urine test for drug addicts registered in the system is complicated, the coverage of supervisors is small, and privacy is involved.

Among them, when the dynamic control and early warning system detects that a registered drug person is checking in for accommodation, boarding a plane or taking a high-speed train in a business trip, the ID card information alarm will prompt the local drug addicts to dynamically supervise the person, and the drug addict will usually carry out Urine test: Although the urine test method is relatively accurate, there are problems such as complicated testing process and limited coverage of supervisors. In this embodiment, based on the characteristics of agitation, panic, and inability to precisely control their own knuckles when drug addiction occurs, a device 100 for predicting relapse risk for drug addicts is proposed, which can perform irregular relapse to drug addicts. Sampling, and then risk prediction for relapse of drug addicts. Wherein, the relapse risk prediction method for drug addicts is applied to the relapse risk prediction device 100, which may be but not limited to steps S1 to S3:

Step S1, identifying the knuckle edge of the drug addict's knuckle in the preset area, and acquiring first information about the knuckle edge sliding in the preset area; wherein, the first information is that the knuckle edge moves in the preset area , the first spatial information collected at the first moment of movement and the second spatial information collected at the second moment.

In this embodiment, the method is applied to the device 100 for predicting relapse risk for drug addicts, and the device 100 for predicting relapse risk is used to identify the phalanx edge in a preset area, and obtain the first position of the phalangeal edge sliding in the preset area. 1 information; the first information is the first spatial information collected at the first moment of movement and the second spatial information collected at the second moment when the phalangeal edge is within the preset area. During the specific implementation, the drug addict can imitate and display the knuckles or hand movements displayed in the prompt video in a preset area. When the relapse risk prediction device 100 detects that the knuckles are in the preset area, it can identify or display it through image processing. The distance sensor detects the knuckle edge of the drug addict's knuckle; wherein, the knuckle edge can be the fingertip or the joint of the knuckle. The drug addicts change their gestures according to the knuckles or hand movements shown in the prompt video, so that the relapse risk prediction device 100 can collect different positions corresponding to the knuckle edges at different times.

Step S2, according to the comparison between the first spatial information collected at the first moment and the second spatial information collected at the second moment, confirm the movement state data of the phalangeal edge within the preset area.

After collecting the spatial information at the first moment and the second spatial information collected at the second moment, the moving direction and speed of the phalanx of the drug addict in the preset area can be obtained through the edge of the drug addict's knuckle, so as to obtain the drug addict's knuckles. The movement state of the person's finger in the preset area, and then determine the movement state data of the knuckle edge in the preset area; the movement state data corresponds to: obtained by the position of the knuckle edge between each adjacent two moments The distance and direction of movement of the knuckle edge at each moment.

In step S3, a comparison result is generated by comparing the mobile state data with the preset state data, and a relapse risk prediction is performed on the drug addicts according to the comparison result.

The preset state data is the matching movement distance and movement direction of the knuckle edge corresponding to the knuckles or hand movements shown in the video display at each moment, and the knuckles or the knuckle edges corresponding to the hand movements of the movement status data are matched at each moment. The moving distance and moving direction of the matched knuckle edge are compared with the moving distance and moving direction of the knuckle edge corresponding to the knuckle or hand motion of the preset state data, and the correlation degree between the preset state data and the moving state data is obtained. , to predict the relapse risk of drug addicts according to the correlation degree. For example, when it is detected that the degree of association between the movement state data and the preset state data is not at the preset threshold, it is determined that the drug addict has a risk of relapse; when it is detected that the degree of association between the movement state data and the preset state data is at the preset threshold , it is judged that there is currently no risk of relapse in drug addicts. The behavior of the drug addicts can be regulated by irregular detection of the drug addicts, and the state of the drug addicts can be quickly detected by the relapse risk prediction device 100, so as to predict the relapse risk of the drug addicts.

In a possible design, the step of confirming the movement state data of the phalangeal edge in the preset area includes: confirming the horizontal displacement and vertical displacement of the phalangeal edge in the preset area, and determining the horizontal displacement and vertical displacement according to the horizontal displacement and vertical displacement. Generate the movement trajectory and velocity trajectory of the knuckle edge.

When detecting the edge of the phalanx of the drug addicts, the vertical plane of the edge of the phalanx of the drug addict can be detected, and the horizontal plane of the edge of the knuckle of the drug addict can also be detected; Accurate, it can detect the horizontal displacement and vertical displacement of the knuckle edge in the preset area to obtain the three-dimensional movement trajectory and speed trajectory of the knuckle edge in the preset area, and then compare the movement state data with the preset state data. During the comparison, it can be more accurately judged whether the movements of the knuckles or hands of the drug addicts are within the threshold corresponding to the preset state data. In this embodiment, by identifying the edges of the knuckles and recording the moving direction and moving speed corresponding to each moment, and comparing with the moving speed and direction corresponding to the preset dynamic data, the corresponding knuckles of drug addicts are more accurately obtained. The movement state data of the knuckle edge improves the accuracy of the relapse risk prediction device 100 for drug addicts.

In a possible design, the preset state data includes a preset motion track and a preset speed track, and the step generates a comparison result by comparing the movement state data with the preset state data, and the preset state data includes:

According to the comparison of the movement trajectory and movement data of the knuckle edge with the preset motion trajectory and the preset speed trajectory, respectively, the degree of movement correlation between the movement trajectory and the preset motion trajectory and the correlation between the movement data and the preset speed trajectory are obtained. Speed correlation.

By comparing the movement trajectory and movement data of the knuckle edge with the preset action and the preset speed trajectory respectively, and obtaining the movement correlation between the movement trajectory and the preset trajectory, the mental state of the drug addicts can be obtained; at the same time, by comparing The speed correlation between the mobile data and the preset speed, combined with the mobile correlation, can predict the accuracy of the drug addicts' finger or hand control, so as to determine whether the drug addicts have drug addiction or drug addiction attacks, and then the drug addicts The risk of relapse is predicted, and when the risk of relapse is detected, supervisors are reminded to take measures against drug addicts.

In one possible design, the steps to predict relapse risk for drug addicts based on the comparison results include:

Determine whether the mobility relatedness and speed relatedness are within the range of the mobile relatedness threshold and the speed relatedness threshold, respectively. When the mobile relatedness is within the mobile relatedness threshold and the speed relatedness is within the speed related threshold, judge the drug addicts. not at risk of relapse;

In this embodiment, the correlation threshold range can be set according to the drug abuse history, age or gender of the drug addict, and the correlation threshold range can be obtained when the drug addict performs test training in a drug rehabilitation center; it can also be obtained through the drug addict history The average correlation threshold range obtained by age or gender, when the mobile correlation degree and the speed correlation degree are within the mobile correlation threshold range and speed correlation threshold range respectively, it is judged that the drug addict is not in a relapse risk state, and there is no need for the drug addict. Take measures.

When the mobility correlation degree is not within the mobility correlation degree threshold range and/or the speed correlation degree is not within the speed correlation degree threshold value range, it is determined that the drug addict is in a state of relapse risk.

When both the mobility correlation degree and the speed correlation degree are not within the threshold range of the mobility correlation degree and the speed correlation degree threshold, it is judged that the drug addict has a drug addiction attack, is in a state of restlessness, panic, and cannot precisely control his knuckles, and needs to be monitored. Personnel re-test or monitor drug addicts to prevent drug addicts from relapse.

In a possible design, before the step of identifying the knuckle edge of the drug addict's knuckles in the preset area, the method further includes: acquiring historical test data of the drug addict, acquiring preset state data according to the historical test data, and preset state data. The data includes dynamic running trajectories of knuckle reference points.

After identifying the drug addict's knuckles entering the preset area, retrieve the drug addict's drug use history and historical test data. According to the historical test data, the difficulty level of the preset state data can be adjusted, so as to gradually adjust the difficulty of the preset state data according to the historical data of different drug addicts. Increase the difficulty or reduce the difficulty; wherein, the preset state data includes the dynamic running trajectory of the reference point of the knuckle, and the reference point can be one or more, which can be the fingertip or the joint; at the same time, it can be based on the historical test data of the drug addicts The motion trajectory of one or more reference points is selected for testing of drug addicts.

In a possible design, before the step of identifying the knuckle edge of the drug addict's knuckles in the preset area, the method further includes: when it is detected that the drug addict's knuckles are in the preset area, displaying the dynamic operation of the knuckle reference point The track is used for drug addicts to adjust the motion state of the knuckle edge according to the dynamic running track of the knuckle reference point.

When collecting the knuckle edges of drug addicts, the dynamic running track of the reference point is displayed on the knuckle or hand motion video displayed by the prompt video, and the reference point of the knuckle or hand currently collected by the drug addict is displayed. To improve the accuracy of drug addicts' control of hand movements; at the same time, collect the hand movements of drug addicts and compare the reference point corresponding to the hand motion with the position and speed of the display reference point at each moment. Yes, it is more accurate to predict the relapse risk of drug addicts. Of course, the collected movement trajectory of the knuckle edge can also be marked on the prompt video, so that the drug addict can know the specific position of the knuckle edge and adjust the movement of the knuckle or hand.

In the second aspect, an embodiment of the present invention provides a relapse risk prediction device 100 for drug addicts, including:

The knuckle edge data acquisition module 110 is used to identify the knuckle edge of the drug addict's knuckle in the preset area, and acquire the first information of the knuckle edge sliding in the preset area; wherein, the first information is the knuckle When the edge moves within the preset area, the first spatial information collected at the first moment of movement and the second spatial information collected at the second moment;

The relapse risk prediction device 100 is used to identify the phalangeal margin in a preset area, and obtain first information of the phalangeal margin sliding within the preset area; the first information is that when the phalangeal margin is within the preset area, The first spatial information collected at the first moment of movement and the second spatial information collected at the second moment. During the specific implementation, the drug addict can imitate and display the knuckles or hand movements displayed in the prompt video in a preset area. When the relapse risk prediction device 100 detects that the knuckles are in the preset area, it can identify or display it through image processing. The distance sensor detects the knuckle edge of the drug addict's knuckle; wherein, the knuckle edge can be the fingertip or the joint of the knuckle. The drug addicts change their gestures according to the knuckles or hand movements shown in the prompt video, so that the relapse risk prediction device 100 can collect different positions corresponding to the knuckle edges at different times.

The phalangeal edge state module 120 is configured to confirm the movement state data of the phalangeal edge within the preset area according to the comparison between the first spatial information collected at the first moment and the second spatial information collected at the second moment.

After collecting the spatial information at the first moment and the second spatial information collected at the second moment, the moving direction and speed of the phalanx of the drug addict in the preset area can be obtained through the edge of the drug addict's knuckle, so as to obtain the drug addict's knuckles. The movement state of the person's finger in the preset area, and then determine the movement state data of the knuckle edge in the preset area; the movement state data corresponds to: obtained by the position of the knuckle edge between each adjacent two moments The distance and direction of movement of the knuckle edge at each moment.

The result comparison module 130 is configured to generate a comparison result by comparing the movement state data with the preset state data, and predict the relapse risk of the drug addicts according to the comparison result.

The preset state data is the matching movement distance and movement direction of the knuckle edge corresponding to the knuckles or hand movements shown in the video display at each moment, and the knuckles or the knuckle edges corresponding to the hand movements of the movement status data are matched at each moment. The moving distance and moving direction of the matched knuckle edge are compared with the moving distance and moving direction of the knuckle edge corresponding to the knuckle or hand motion of the preset state data, and the correlation degree between the preset state data and the moving state data is obtained. , to predict the relapse risk of drug addicts according to the correlation degree. For example, when it is detected that the degree of association between the movement state data and the preset state data is not at the preset threshold, it is determined that the drug addict has a risk of relapse; when it is detected that the degree of association between the movement state data and the preset state data is at the preset threshold , it is judged that there is currently no risk of relapse in drug addicts. The behavior of the drug addicts can be regulated by irregular detection of the drug addicts, and the state of the drug addicts can be quickly detected by the relapse risk prediction device 100, so as to predict the relapse risk of the drug addicts.

In one possible design, the device also includes:

The test data calling module 140 is used for acquiring historical test data of the relapsed person, and acquiring preset state data according to the historical test data, where the preset state data includes the dynamic running track of the reference point of the finger joint.

After identifying the drug addict's knuckles entering the preset area, retrieve the drug addict's drug use history and historical test data. According to the historical test data, the difficulty level of the preset state data can be adjusted, so as to gradually adjust the difficulty of the preset state data according to the historical data of different drug addicts. Increase the difficulty or reduce the difficulty; wherein, the preset state data includes the dynamic running trajectory of the reference point of the knuckle, and the reference point can be one or more, which can be the fingertip or the joint; at the same time, it can be based on the historical test data of the drug addicts The motion trajectory of one or more reference points is selected for testing of drug addicts.

In a possible design, the relapse risk prediction device 100 further includes:

The display control module 150 is configured to display the dynamic running track of the reference point of the knuckle when the knuckle of the drug addict is detected to be in the preset area, so that the drug addict adjusts the motion state of the edge of the knuckle according to the dynamic running track of the knuckle reference point.

When collecting the knuckle edges of drug addicts, the dynamic running track of the reference point is displayed on the knuckle or hand motion video displayed by the prompt video, and the reference point of the knuckle or hand currently collected by the drug addict is displayed. To improve the accuracy of drug addicts' control of hand movements; at the same time, collect the hand movements of drug addicts and compare the reference point corresponding to the hand motion with the position and speed of the display reference point at each moment. Yes, it is more accurate to predict the relapse risk of drug addicts. Of course, the collected movement trajectory of the knuckle edge can also be marked on the prompt video, so that the drug addict can know the specific position of the knuckle edge and adjust the movement of the knuckle or hand.

A third aspect of an embodiment of the present application provides a device for predicting relapse risk for drug addicts, including a memory and a processor that are sequentially connected in communication, wherein the memory is used to store a computer program, and the processor is used to read the computer program A program for executing the relapse risk prediction method for drug addicts as described in the first aspect of the embodiment.

For example, the memory may include, but is not limited to, random access memory (RAM), read only memory (ROM), flash memory (Flash Memory), first-in, first-out (FIFO), and/or first-in, last-out (FILO) etc.; the processor may not be limited to adopting a microprocessor of the STM32F105 series, an ARM (Advanced RISCMachines), X86 and other architecture processors, or a processor integrating an NPU (neural-network processing units).

For the working process, working details, and technical effects of the apparatus provided in the third aspect of this embodiment, reference may be made to the first aspect of the embodiment, and details are not described herein again.

A fourth aspect of this embodiment provides a computer-readable storage medium that stores an instruction that includes the method for predicting relapse risk for drug addicts described in the first aspect of the embodiment, that is, the computer-readable storage medium stores an instruction, When the instructions are executed on the computer, the method for predicting the relapse risk of drug addicts as described in the first aspect is executed. Wherein, the computer-readable storage medium refers to a carrier for storing data, which may include, but is not limited to, a floppy disk, an optical disk, a hard disk, a flash memory, a USB flash drive and/or a Memory Stick, etc. The computer may be a general-purpose computer, a special-purpose computer, or a A computer, computer network, or other programmable device.

For the working process, working details, and technical effects of the computer-readable storage medium provided in the fourth aspect of this embodiment, reference may be made to the first aspect of the embodiment, and details are not repeated here.

A fifth aspect of this embodiment provides a computer program product containing instructions, when the instructions are run on a computer, the computer is made to execute the method for predicting the relapse risk of drug addicts according to the first aspect of the embodiment, wherein , the computer may be a general purpose computer, a special purpose computer, a computer network, or other programmable devices.

The multiple embodiments described above are only illustrative, wherein the units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, Located in one place, or it can be distributed over multiple network elements. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution in this embodiment. Those of ordinary skill in the art can understand and implement it without creative effort.

From the description of the above embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus a necessary general hardware platform, and certainly can also be implemented by hardware. Based on this understanding, the above-mentioned technical solutions can be embodied in the form of software products in essence or the parts that make contributions to the prior art, and the computer software products can be stored in computer-readable storage media, such as ROM/RAM, magnetic A disc, optical disc, etc., including instructions for causing a warehouse code merging apparatus to perform the methods described in various embodiments or portions of embodiments.

Finally, it should be noted that the above descriptions are only preferred embodiments of the invention, and are not intended to limit the protection scope of the invention. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the invention shall be included in the protection scope of the invention.

Claims (10)

1. a drug relapse risk prediction method for drug addicts is characterized in that, comprising: Identifying the knuckle edges of the drug addicts' knuckles within a preset area, and acquiring first information about the knuckle edges sliding within the preset area; wherein the first information is that the knuckle edges are within the preset area When moving, the first spatial information collected at the first moment of movement and the second spatial information collected at the second moment; According to the comparison between the first spatial information collected at the first moment and the second spatial information collected at the second moment, confirm the movement state data of the phalangeal edge within the preset area; A comparison result is generated by comparing the movement state data with the preset state data, and a relapse risk prediction is performed on the drug addicts according to the comparison result. 2. The method for predicting relapse risk for drug addicts according to claim 1, wherein the step of confirming the movement state data of the phalanx edge in the preset area comprises: confirming that the phalangeal edge is in the preset area The horizontal displacement and vertical displacement of the phalanx are generated according to the horizontal displacement and vertical displacement, and the movement trajectory and velocity trajectory of the knuckle edge are generated. 3. The method for predicting the risk of relapse of drug addicts according to claim 2, wherein the preset state data comprises a preset movement track and a preset speed track, and the step passes through the movement state data and the preset state data. The comparison performed generates comparison results, and the preset state data includes: According to the comparison of the movement trajectory and movement data of the knuckle edge with the preset motion trajectory and the preset speed trajectory, respectively, the degree of movement correlation between the movement trajectory and the preset motion trajectory and the correlation between the movement data and the preset speed trajectory are obtained. Speed correlation. 4. drug relapse risk prediction method for drug addicts according to claim 3, is characterized in that, step carries out relapse risk prediction to drug addicts according to described comparison result, comprising: Judging whether the movement relevance degree and the speed relevance degree are within the movement relevance threshold range and the speed relevance threshold range respectively, when the movement relevance degree is within the movement relevance threshold range and the speed relevance degree is within the speed relevance degree When it is within the threshold range, it is judged that the drug addict is not at risk of relapse; When the movement correlation degree is not within the movement correlation degree threshold range and/or the speed correlation degree is not within the speed correlation degree threshold value range, it is determined that the drug addict is in a relapse risk state. 5. drug relapse risk prediction method for drug addicts according to claim 1, is characterized in that, before identifying the knuckle edge step of drug addicts' knuckles in preset area, also comprises: The historical test data of the drug addict is acquired, and preset state data is acquired according to the historical test data, and the preset state data includes the dynamic running track of the knuckle reference point. 6. The relapse risk prediction method for drug addicts according to claim 5 is characterized in that, after the step of obtaining preset state data according to historical test data, the step of identifying the knuckle edge of the knuckles of drug addicts in a preset area Before, also included: When it is detected that the knuckles of the drug addicts are in the preset area, the dynamic running track of the reference point of the knuckle is displayed, which is used for the drug addict to adjust the motion state of the edge of the knuckle according to the dynamic running track of the knuckle reference point. 7. A device for predicting relapse risk for drug addicts, wherein the device comprises: A knuckle edge data acquisition module, used to identify the knuckle edge of a drug addict's knuckle in a preset area, and acquire first information about the knuckle edge sliding in the preset area; wherein the first information When the phalangeal edge moves within the preset area, the first spatial information collected at the first moment of movement and the second spatial information collected at the second moment; The phalangeal edge state module is configured to confirm the movement state data of the phalangeal edge within the preset area according to the comparison between the first spatial information collected at the first moment and the second spatial information collected at the second moment; The result comparison module is configured to generate a comparison result by comparing the movement state data with the preset state data, and predict the relapse risk for the drug addicts according to the comparison result. 8. The device for predicting relapse risk for drug addicts according to claim 7, wherein the device further comprises: The test data calling module is used for acquiring historical test data of the relapsed person, and acquiring preset state data according to the historical test data, where the preset state data includes the dynamic running track of the reference point of the finger joint. 9. The device for predicting relapse risk for drug addicts according to claim 8, wherein the device further comprises: The display control module is used to display the dynamic running track of the reference point of the knuckle when it is detected that the knuckles of the drug addict are in the preset area, so that the drug addict can adjust the motion state of the edge of the knuckle according to the dynamic running track of the knuckle reference point. . 10. A computer-readable storage medium, characterized in that, an instruction is stored on the computer-readable storage medium, and when the instruction is executed on a computer, the method described in any one of claims 1 to 6 is executed. Relapse risk prediction method for drug addicts.

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