Violence risk early warning system and method based on violence tendency analysis and electronic equipment
By designing a violent risk warning system based on violence tendency analysis in a pediatric emergency environment, identifying and analyzing the identity and behavioral characteristics of personnel, and calculating the violence tendency score, the problem of the inability to promptly warn in the existing technology is solved, and the efficiency of avoiding violence is improved. .
Patent Information
- Application Number
- CN202411294707.8
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2024-09-14
- Publication Date
- 2025-01-07
- Estimated Expiration
- Not applicable · inactive patent
AI Technical Summary
The existing technology cannot be promptly warned, resulting in the inability to efficiently avoid violent incidents, and pediatric emergency medical personnel are vulnerable to violent attacks.
A violent risk warning system based on violence tendency analysis was designed, including personnel identification module, behavior analysis module, propensity assessment module and risk warning module. Through identity characteristics and behavior characteristics analysis, the violence tendency score is calculated and based on preset thresholds. Risk warning.
Accurate and timely analysis of violence tendency is achieved, risk warnings are made on terminal devices in a timely manner, and the efficiency of avoiding violent incidents is improved.
Smart Images
Figure CN119274788A_ABST
Abstract
Description
Technical Field
[0001] The present application relates to the technical field of violence risk warning, and specifically to a violence risk warning system, method and electronic equipment based on violence tendency analysis. Background Art
[0002] Pediatric emergency department is the department that has the most contact with patients and their families. Children have poor language and cognitive abilities and cannot express their ideas accurately. Pediatric emergency medical staff, as the personnel who have the closest contact with patients' families, are easily targeted by patients' and their families' negative emotions and are more vulnerable to violent attacks.
[0003] At present, many countries and regions have successively issued guidelines, technical tools, laws and regulations related to violence prevention and control, including anti-riot assessment tools, the establishment of early warning systems, anti-riot training for nurses, the improvement of medical treatment processes, and the improvement of security facilities. However, existing technologies still cannot provide timely warnings, and therefore cannot effectively avoid violent incidents. Summary of the invention
[0004] The purpose of this application is to provide a violence risk warning system, method and electronic device based on violence tendency analysis to solve the technical problems raised in the above background.
[0005] To achieve the above objectives, this application discloses the following technical solutions:
[0006] The present application discloses, in a first aspect, a violence risk warning system based on violence tendency analysis, including a personnel identification module, a behavior analysis module, a tendency assessment module and a risk warning module;
[0007] The personnel identification module is configured to: collect identity features of personnel and identify the personnel, wherein the identity features include at least one of facial data, fingerprint data or ID data;
[0008] The behavior analysis module is configured to: perform behavior analysis on a person based on the person's behavior characteristics, wherein the behavior characteristics include one or more of facial expressions, body movements, language content, expression tone, or expression speed;
[0009] The tendency assessment module is configured to: perform a violence tendency analysis on the person to be assessed based on the results of the identity recognition and the behavior analysis, and obtain a violence tendency score of the person to be assessed;
[0010] The risk warning module is configured to make a risk warning based on a preset warning threshold and the violence tendency score of the person to be evaluated, when the violence tendency score is greater than the warning threshold.
[0011] Preferably, the violence tendency analysis specifically includes:
[0012] Based on big data, a data set indicating the existence of violent tendencies is obtained, and the data set is used to train the assessment model to obtain a risk assessment model;
[0013] Performing data preprocessing on the results of the identity recognition and behavior analysis corresponding to the person to be evaluated, wherein the data preprocessing includes data cleaning, feature analysis, and data standardization;
[0014] The result of data preprocessing is imported into the risk assessment model, and the risk score of violence tendency of the person to be assessed is scored according to the risk assessment model to calculate the violence tendency score.
[0015] Preferably, the data set includes a training data set and a test data set.
[0016] Preferably, when training the evaluation model, the data set is used as the original data set, and the original data set is enriched by a clustering algorithm to obtain a simulated data set, and both the original data set and the simulated data set are used as data for training.
[0017] Preferably, the risk assessment model performs multi-source information fusion on the feature values of each data source after data preprocessing, and performs risk scoring based on the fused feature values; wherein the calculation formula for the fused feature values is:
[0018]
[0019] Among them, F fuse is the fused eigenvalue, w i is the weight value, F i is the eigenvalue of the i-th data source before fusion.
[0020] Preferably, the calculation formula of the risk score is:
[0021]
[0022] Among them, RS is the calculated violent tendency score, e is the base of the natural logarithm, β0 is the preset constant term, and β i is the correlation coefficient of the i-th feature, F fusei is the i-th fused eigenvalue, and m is the total number of fused eigenvalues.
[0023] Preferably, the tendency assessment module is further configured to calculate a plurality of risk scores based on a plurality of risk assessment models trained using different data sets, and calculate a final violence tendency score through a score fusion algorithm.
[0024] Preferably, the calculation formula of the score fusion algorithm is:
[0025]
[0026] Among them, Final RS is the calculated score of the violent tendency of a person to be assessed, k is the number of risk assessment models, α j is the weight coefficient, RS j is the risk score calculated for the j-th risk assessment model.
[0027] In a second aspect, the present application discloses a violence risk early warning method based on violence tendency analysis, which is applied to the violence risk early warning system based on violence tendency analysis as described above. The method comprises the following steps:
[0028] Collecting identity features of personnel to identify the personnel, wherein the identity features include at least one of facial data, fingerprint data or ID data;
[0029] Conducting behavioral analysis on personnel based on their behavioral characteristics, where the behavioral characteristics include one or more of facial expressions, body movements, language content, expression tone, or expression speed;
[0030] Based on the results of the identity recognition and the behavior analysis, a violence tendency analysis is performed on the person to be assessed to obtain a violence tendency score of the person to be assessed;
[0031] Based on the preset warning threshold and the violence tendency score of the person to be assessed, a risk warning is issued when the violence tendency score is greater than the warning threshold.
[0032] The present application discloses an electronic device in a third aspect, comprising at least one memory and at least one processor, wherein the memory is communicatively connected to the processor;
[0033] The memory stores a computer program that can be executed by the processor. When the computer program is executed by the processor, the violence risk warning method based on violence tendency analysis as described above is implemented.
[0034] Beneficial effects: This application realizes accurate and timely analysis of violence tendencies through identification of personnel identities and analysis of personnel behaviors, and compares the obtained violence tendency scores of the personnel to be evaluated with the preset warning thresholds, thereby timely issuing risk warnings at the corresponding terminals (such as nurse computers, security room monitoring terminals, etc.), thereby achieving the purpose of efficiently avoiding violent incidents. BRIEF DESCRIPTION OF THE DRAWINGS
[0035] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For those skilled in the art, other drawings can be obtained based on these drawings without paying any creative work.
[0036] Figure 1 A structural block diagram of a violence risk warning system based on violence tendency analysis provided in an embodiment of the present application;
[0037] Figure 2 A flowchart of a violence risk warning method based on violence tendency analysis provided in an embodiment of the present application;
[0038] Figure 3 A schematic diagram of the structure of an electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION
[0039] The technical solutions in the embodiments of the present application are described clearly and completely below. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present application.
[0040] In this article, the term "comprising" is intended to cover non-exclusive inclusion, so that a process, method, article or device that includes a series of elements includes not only those elements, but also includes other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of more restrictions, the elements defined by the sentence "comprising..." do not exclude the presence of other identical elements in the process, method, article or device that includes the elements.
[0041] In a first aspect, this embodiment provides Figure 1 A violence risk warning system based on violence tendency analysis is shown, comprising a personnel identification module, a behavior analysis module, a tendency assessment module and a risk warning module.
[0042] Specifically, the personnel identification module is configured to: collect identity features of personnel and perform identity identification on personnel, wherein the identity features include at least one of facial data, fingerprint data or ID data; the behavior analysis module is configured to: perform behavior analysis on personnel based on the behavior features of personnel, wherein the behavior features include one or more of facial expressions, body movements, language content, expression tone or expression speed; the tendency assessment module is configured to: perform violence tendency analysis on the personnel to be assessed based on the results of the identity identification and the behavior analysis, and obtain the violence tendency score of the personnel to be assessed; the risk warning module is configured to: based on a preset warning threshold and the violence tendency score of the personnel to be assessed, when the violence tendency score is greater than the warning threshold, issue a risk warning.
[0043] Preferably, the violence tendency analysis specifically includes:
[0044] Based on big data, a data set indicating the existence of violent tendencies is obtained, and the data set is used to train the assessment model to obtain a risk assessment model;
[0045] Performing data preprocessing on the results of the identity recognition and behavior analysis corresponding to the person to be evaluated, wherein the data preprocessing includes data cleaning, feature analysis, and data standardization;
[0046] The result of data preprocessing is imported into the risk assessment model, and the risk score of violence tendency of the person to be assessed is scored according to the risk assessment model to calculate the violence tendency score.
[0047] Here, the data sources may include data that has been made public or can be used (such as data that has been permitted to be used / accessed through judicial filings), such as: basic personal information: age, gender, educational background, etc.; historical behavior records: criminal records, social media activities, past violent incidents, etc.; psychological assessment data: psychological tests, questionnaire results, etc.; environmental factors: life pressure, work environment, family relationships, etc. These data can generally be collected from multiple sources, including basic personal information, historical behavior records, psychological assessment data, environmental factors, and social media activities.
[0048] During the data preprocessing process, the functions of each step are: data cleaning: removing invalid, missing or abnormal data; feature extraction: extracting useful features from the original data, such as behavioral patterns, emotional changes, etc.; data standardization: ensuring that the data format and scope are consistent for subsequent analysis.
[0049] The results of data preprocessing generally correspond to performance characteristics that indicate whether a person has a tendency to be violent. These performance characteristics can be: behavioral characteristics: frequent conflicts, threats to others, aggressive speech, etc.; psychological characteristics: irritability, impulse control disorder, low self-esteem, etc.; social environmental factors: social isolation, unemployment, history of domestic violence, etc.; historical records: past violent behavior, criminal records, etc. When implementing, feature selection techniques (such as PCA and Lasso regression) can be used to reduce feature dimensions and improve model performance. When analyzing the corresponding performance characteristics, representative features are extracted for each data source. For example, the frequency of conflicts and the number of threats are extracted from historical behavior records; the degree of irritability and impulse control disorder are extracted from psychological assessment data; the proportion of aggressive speech and emotional changes are extracted from social media activities.
[0050] In order to improve the reliability of the model training results, in this embodiment, the data set includes a training data set and a test data set. In addition, when training the evaluation model, the data set is used as the original data set, and the original data set is enriched by a clustering algorithm to obtain a simulated data set, and the original data set and the simulated data set are used as data for training. In this way, the amount of data used for model training can be enriched, and the reliability of the model can be improved as much as possible.
[0051] As a preferred implementation of this embodiment, the risk assessment model performs multi-source information fusion on the feature values of each data source after data preprocessing, and performs risk scoring based on the fused feature values; wherein the calculation formula for the fused feature values is:
[0052]
[0053] Among them, F fuse is the fused eigenvalue, w i is the weight value, F i is the eigenvalue of the i-th data source before fusion.
[0054] In this way, features from different sources are fused to form a comprehensive feature set, thereby ensuring the accuracy of the model output.
[0055] Furthermore, the risk score is calculated as follows:
[0056]
[0057] Among them, RS is the calculated violent tendency score, e is the base of the natural logarithm, β0 is the preset constant term, and β i is the correlation coefficient of the i-th feature, F fusei is the i-th fused eigenvalue, and m is the total number of fused eigenvalues.
[0058] In order to improve the reliability of the early warning, the tendency assessment module is further configured to calculate a number of risk scores based on multiple risk assessment models trained using different data sets, and calculate the final violence tendency score through a score fusion algorithm. Specifically, the calculation formula of the score fusion algorithm is:
[0059]
[0060] Among them, Final RS is the calculated score of the violent tendency of a person to be assessed, k is the number of risk assessment models, α j is the weight coefficient, RS j is the risk score calculated for the j-th risk assessment model.
[0061] In the above calculations, actual applications need to be adjusted according to specific data sets, task requirements, and model selection.
[0062] Through the above calculation process, a new risk scoring algorithm was obtained, which can integrate multi-source information, improve the accuracy and reliability of scoring, and support efficient data processing and calculation. In this way, the timeliness and reliability of violent behavior warning can be ensured.
[0063] Based on the above, the violence risk warning system based on violence tendency analysis of this embodiment realizes accurate and timely violence tendency analysis through identification of personnel identity and analysis of personnel behavior, and compares the obtained violence tendency score of the person to be evaluated with the preset warning threshold, so as to timely make risk warnings on the corresponding terminals (such as nurse computers, security room monitoring terminals, etc.), thereby achieving the purpose of efficiently avoiding violent incidents.
[0064] In a second aspect, this embodiment provides Figure 2 A violence risk warning method based on violence tendency analysis is shown, which is applied to the violence risk warning system based on violence tendency analysis as described above, and the method comprises the following steps:
[0065] Collecting identity features of personnel to identify the personnel, wherein the identity features include at least one of facial data, fingerprint data or ID data;
[0066] Conducting behavioral analysis on personnel based on their behavioral characteristics, where the behavioral characteristics include one or more of facial expressions, body movements, language content, expression tone, or expression speed;
[0067] Based on the results of the identity recognition and the behavior analysis, a violence tendency analysis is performed on the person to be assessed to obtain a violence tendency score of the person to be assessed;
[0068] Based on the preset warning threshold and the violence tendency score of the person to be assessed, a risk warning is issued when the violence tendency score is greater than the warning threshold.
[0069] It should be noted that the violence risk warning method based on violence tendency analysis of the present embodiment corresponds to the aforementioned violence risk warning system based on violence tendency analysis. Therefore, the violence risk warning method based on violence tendency analysis is not described in detail in this text (including but not limited to technical effects, further defined technical contents, etc.). The corresponding reference may be made to the description in the aforementioned violence risk warning system based on violence tendency analysis, and this text will not elaborate on it here.
[0070] The present embodiment provides an electronic device in a third aspect, and the electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device may also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices (such as helmets, glasses, watches, etc.) and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present invention described and / or required herein.
[0071] like Figure 3 As shown, the electronic device 10 includes at least one processor 11, and a memory connected to the at least one processor 11, such as a read-only memory (ROM) 12, a random access memory (RAM) 13, etc., wherein the memory stores a computer program that can be executed by at least one processor, and the processor 11 can perform various appropriate actions and processes according to the computer program stored in the read-only memory (ROM) 12 or the computer program loaded from the storage unit 18 to the random access memory (RAM) 13. In the RAM 13, various programs and data required for the operation of the electronic device 10 can also be stored. The processor 11, the ROM 12, and the RAM 13 are connected to each other through a bus 14. An input / output (I / O) interface 15 is also connected to the bus 14.
[0072] A number of components in the electronic device 10 are connected to the I / O interface 15, including: an input unit 16, such as a keyboard, a mouse, etc.; an output unit 17, such as various types of displays, speakers, etc.; a storage unit 18, such as a disk, an optical disk, etc.; and a communication unit 19, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 19 allows the electronic device 10 to exchange information / data with other devices through a computer network such as the Internet and / or various telecommunication networks.
[0073] The processor 11 may be a variety of general and / or special processing components with processing and computing capabilities. Some examples of the processor 11 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, a digital signal processor (DSP), and any appropriate processor, controller, microcontroller, etc. The processor 11 executes the various methods and processes described above, such as a violence risk warning method based on violence tendency analysis.
[0074] In some embodiments, the violence risk warning method based on violence tendency analysis may be implemented as a computer program, which is tangibly contained in a computer-readable storage medium, such as a storage unit 18. In some embodiments, part or all of the computer program may be loaded and / or installed on the electronic device 10 via the ROM 12 and / or the communication unit 19. When the computer program is loaded into the RAM 13 and executed by the processor 11, one or more steps of the violence risk warning method based on violence tendency analysis described above may be performed. Alternatively, in other embodiments, the processor 11 may be configured to execute the violence risk warning method based on violence tendency analysis in any other appropriate manner (e.g., by means of firmware).
[0075] Various implementations of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on chips (SOCs), load programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various implementations can include: being implemented in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which can be a special purpose or general purpose programmable processor that can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.
[0076] Computer programs for implementing the methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, so that when the computer program is executed by the processor, the functions / operations specified in the flow chart and / or block diagram are implemented. The computer program may be executed entirely on the machine, partially on the machine, partially on the machine and partially on a remote machine as a stand-alone software package, or entirely on a remote machine or server.
[0077] In the context of the present invention, a computer-readable storage medium may be a tangible medium that may contain or store a computer program for use by or in combination with an instruction execution system, device or equipment. A computer-readable storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices or equipment, or any suitable combination of the foregoing. Alternatively, a computer-readable storage medium may be a machine-readable signal medium. A more specific example of a machine-readable storage medium may include an electrical connection based on one or more lines, 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 foregoing.
[0078] To provide interaction with a user, the systems and techniques described herein may be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and a pointing device (e.g., a mouse or trackball) through which the user can provide input to the electronic device. Other types of devices may also be used to provide interaction with the user; for example, the feedback provided to the user may be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form (including acoustic input, voice input, or tactile input).
[0079] The systems and techniques described herein may be implemented in a computing system that includes backend components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes frontend components (e.g., a user computer with a graphical user interface or a web browser through which a user can interact with implementations of the systems and techniques described herein), or a computing system that includes any combination of such backend components, middleware components, or frontend components. The components of the system may be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: a local area network (LAN), a wide area network (WAN), a blockchain network, and the Internet.
[0080] A computing system may include a client and a server. The client and the server are generally remote from each other and usually interact through a communication network. The client and server relationship is generated by computer programs running on the corresponding computers and having a client-server relationship with each other. The server may be a cloud server, also known as a cloud computing server or cloud host, which is a host product in the cloud computing service system to solve the defects of difficult management and weak business scalability in traditional physical hosts and VPS services.
[0081] Finally, it should be noted that the above are only preferred embodiments of the present application and are not intended to limit the present application. Although the present application has been described in detail with reference to the aforementioned embodiments, it is still possible for those skilled in the art to modify the technical solutions described in the aforementioned embodiments, or to make equivalent substitutions for some of the technical features therein. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present application shall be included in the protection scope of the present application.
Claims
1. A violence risk early warning system based on violence tendency analysis, characterized in that: It includes personnel identification module, behavior analysis module, tendency assessment module and risk warning module; The personnel identification module is configured to: collect identity features of personnel and identify the personnel, wherein the identity features include at least one of facial data, fingerprint data or ID data; The behavior analysis module is configured to: perform behavior analysis on a person based on the person's behavior characteristics, wherein the behavior characteristics include one or more of facial expressions, body movements, language content, expression tone, or expression speed; The tendency assessment module is configured to: perform a violence tendency analysis on the person to be assessed based on the results of the identity recognition and the behavior analysis, and obtain a violence tendency score of the person to be assessed; The risk warning module is configured to make a risk warning based on a preset warning threshold and the violence tendency score of the person to be evaluated, when the violence tendency score is greater than the warning threshold.
2. The violence risk early warning system based on violence tendency analysis according to claim 1 is characterized in that: The analysis of violence tendency specifically includes: Based on big data, a data set indicating the existence of violent tendencies is obtained, and the data set is used to train the assessment model to obtain a risk assessment model; Performing data preprocessing on the results of the identity recognition and behavior analysis corresponding to the person to be evaluated, wherein the data preprocessing includes data cleaning, feature analysis, and data standardization; The result of data preprocessing is imported into the risk assessment model, and the risk score of violence tendency of the person to be assessed is scored according to the risk assessment model to calculate the violence tendency score.
3. The violence risk early warning system based on violence tendency analysis according to claim 2 is characterized in that: The data set includes a training data set and a test data set.
4. The violence risk early warning system based on violence tendency analysis according to claim 2 is characterized in that: When training the evaluation model, the data set is used as the original data set, and the original data set is enriched by a clustering algorithm to obtain a simulated data set, and both the original data set and the simulated data set are used as data for training.
5. The violence risk early warning system based on violence tendency analysis according to claim 2, characterized in that: The risk assessment model performs multi-source information fusion on the feature values of each data source after data preprocessing, and performs risk scoring based on the fused feature values; wherein the calculation formula for the fused feature values is: Among them, F fuse is the fused eigenvalue, w i is the weight value, F i is the eigenvalue of the i-th data source before fusion.
6. The violence risk early warning system based on violence tendency analysis according to claim 5 is characterized in that: The risk score is calculated as follows: Among them, RS is the calculated violent tendency score, e is the base of the natural logarithm, β0 is the preset constant term, and β i is the correlation coefficient of the i-th feature, F fusei is the i-th fused eigenvalue, and m is the total number of fused eigenvalues.
7. The violence risk early warning system based on violence tendency analysis according to claim 6, characterized in that: The tendency assessment module is further configured to calculate a plurality of risk scores based on a plurality of risk assessment models trained using different data sets, and calculate a final violence tendency score through a score fusion algorithm.
8. The violence risk early warning system based on violence tendency analysis according to claim 7, characterized in that: The calculation formula of the score fusion algorithm is: Among them, Final RS is the calculated score of the violent tendency of a person to be assessed, k is the number of risk assessment models, α j is the weight coefficient, RS j is the risk score calculated for the j-th risk assessment model.
9. A violence risk warning method based on violence tendency analysis, applied to the violence risk warning system based on violence tendency analysis as claimed in any one of claims 1 to 8, characterized in that: The method comprises the following steps: Collecting identity features of personnel to identify the personnel, wherein the identity features include at least one of facial data, fingerprint data or ID data; Conducting behavioral analysis on personnel based on their behavioral characteristics, where the behavioral characteristics include one or more of facial expressions, body movements, language content, expression tone, or expression speed; Based on the results of the identity recognition and the behavior analysis, a violence tendency analysis is performed on the person to be assessed to obtain a violence tendency score of the person to be assessed; Based on the preset warning threshold and the violence tendency score of the person to be assessed, a risk warning is issued when the violence tendency score is greater than the warning threshold.
10. An electronic device, characterized in that: comprising at least one memory and at least one processor, wherein the memory is communicatively connected to the processor; The memory stores a computer program that can be executed by the processor. When the computer program is executed by the processor, the violence risk warning method based on violence tendency analysis as claimed in claim 9 is implemented.