Drug rehabilitation data management system

Abstract

The invention provides a drug rehabilitation data management system, and the system comprises a collection module which is used for collecting the original data of a drug rehabilitation person in real time through an external access mode; the processing module is used for removing repeated data and error data in the original data and preprocessing the residual data to obtain structured data of the drug addict; the storage module is used for storing the structured data in a drug addict database in a distributed manner; the analysis module is used for identifying a viewing request of a user and analyzing the structured data corresponding to the viewing request to obtain an analysis result of the structured data; and the display module is used for visually displaying the analysis result of the structured data for the user to consult. According to the drug rehabilitation data management system, the overall situation of drug rehabilitation personnel in a drug rehabilitation place can be mastered in real time, the change trend and potential risks of the drug rehabilitation personnel are pre-judged, and finally scientific decision making and fine management of drug rehabilitation work are achieved.

Description

Technical Field

[0001] The present application belongs to the technical field of drug rehabilitation data management, and in particular, relates to a drug rehabilitation data management system. Background Art

[0002] With the continuous development of network information technology, big data and security prevention work have become inseparable. Compared with the traditional management of drug rehabilitation facilities, the introduction of big data has changed the structure and methods of the original management of drug rehabilitation facilities. The traditional management structure of drug rehabilitation facilities is a vertical hierarchical management structure. Information between the various monitoring areas is not interoperable, and horizontal communication is scarce. There is a lack of effective connection between different software and databases in the drug rehabilitation work system, which makes it impossible to exchange and share information, and it is impossible to form a good business collaboration model in a short period of time, which reduces the work efficiency of the system and makes it difficult to truly informatize security prevention and control. At the same time, the level of informatization of various drug rehabilitation facilities varies, there are incompatibility issues between the systems of various drug rehabilitation facilities, the data formats are obviously different, and the phenomenon of self-built and self-used often occurs. Summary of the invention

[0003] In view of this, the purpose of this application is to provide a drug rehabilitation data management system, which collects original data related to drug rehabilitation business by connecting with an external system, and stores the structured data obtained by processing the original data in a distributed manner in a database, so as to eliminate information island barriers and achieve data interoperability; at the same time, in response to user viewing requests, the corresponding structured data is retrieved from the storage module for statistical analysis and predictive analysis, so as to grasp the overall situation of drug addicts in drug rehabilitation facilities in real time, and predict the changing trends and potential risks of drug addicts, and ultimately achieve scientific decision-making and refined management of drug rehabilitation work.

[0004] The present application provides a drug rehabilitation data management system, including:

[0005] The collection module is used to collect the original data of drug addicts in real time through external access;

[0006] A processing module, used to remove duplicate data and erroneous data from the original data, and pre-process the remaining data to obtain structured data of the drug addict;

[0007] A storage module, used for distributing and storing the structured data in a drug rehabilitation personnel database;

[0008] An analysis module, used for identifying a user's viewing request and analyzing structured data corresponding to the viewing request to obtain an analysis result of the structured data;

[0009] The display module is used to visually display the analysis results of the structured data for the user to review.

[0010] Furthermore, the drug rehabilitation management system also includes:

[0011] The external access module is used to provide a software and hardware docking interface, on the one hand, to connect the drug rehabilitation business system so as to collect the original data of the drug rehabilitation personnel from the drug rehabilitation business system, and on the other hand, to allow a third-party system to call so as to share the structured data with the third-party system;

[0012] Among them, the drug rehabilitation business system includes: drug rehabilitation government affairs system, drug rehabilitation law enforcement system and drug rehabilitation supervision system.

[0013] Furthermore, when collecting the original data of drug addicts in real time through external access, the collection module is specifically used to:

[0014] At preset time intervals, the original data of drug rehabilitation personnel are collected from the drug rehabilitation government affairs system, the drug rehabilitation law enforcement system and the drug rehabilitation supervision system respectively;

[0015] Alternatively, for any of the drug addicts, when the original data of the drug addict in the drug rehabilitation government affairs system, the drug rehabilitation law enforcement system and the drug rehabilitation supervision system is updated, the updated original data of the drug addict will be synchronized to the drug rehabilitation management system.

[0016] Furthermore, the analysis module includes:

[0017] The identification unit is used to identify the triggering object, request type and computing task of the viewing request; wherein the request type includes: potential rules and trend changes; the computing task includes: statistical computing tasks and predictive computing tasks.

[0018] Furthermore, the analysis module further includes: a batch processing unit;

[0019] When the viewing request is identified as a potential rule for the structured data, when analyzing the structured data corresponding to the viewing request to obtain an analysis result of the structured data, the batch processing unit is used to:

[0020] The first category of structured data corresponding to the viewing request is extracted from the drug rehabilitation personnel database, and the first category of structured data is batch processed according to the statistical calculation task of the viewing request to obtain statistical results that can characterize the potential rules of the first category of structured data.

[0021] Further, when the viewing request is identified as being actively triggered by the drug rehabilitation management system, when extracting structured data corresponding to the viewing request from the drug rehabilitation personnel database, the batch processing unit is specifically used to:

[0022] According to a preset viewing rule, extracting at least one first category of structured data corresponding to the viewing request from the drug addict database;

[0023] When the viewing request is identified as being passively triggered by the user, when extracting structured data corresponding to the viewing request from the drug addict database, the batch processing unit is specifically used to:

[0024] According to a viewing request for any first category structured data triggered by the user, the first category structured data corresponding to the viewing request is extracted from the drug rehabilitation personnel database.

[0025] Furthermore, the first category of structured data includes: gender, place of origin, age structure, educational level, marital status, years of drug abuse, disability status, types of drug abuse and employment status before admission.

[0026] Furthermore, the analysis module further includes: a stream computing unit;

[0027] When the viewing request is identified as a trend change for any of the drug addicts, when analyzing the structured data corresponding to the viewing request to obtain the analysis result of the structured data, the stream computing unit is used to:

[0028] The second category structured data of the drug addict to be viewed is determined, and the second category structured data is stream-calculated using the prediction calculation task of the viewing request pre-arranged on the node of the second category structured data to obtain a prediction result that can characterize the trend change of the second category structured data of the drug addict.

[0029] Furthermore, the second category of structured data includes: scores of each stage of drug rehabilitation supervision; the drug rehabilitation supervision stages include: physiological detoxification period, educational adaptation period, rehabilitation consolidation period and regression guidance period.

[0030] The drug rehabilitation data management system provided by the present application, by connecting with an external system, collects raw data related to drug rehabilitation business, and stores the structured data obtained by processing the raw data in a distributed manner in a database, so as to eliminate information island barriers and realize data interoperability; at the same time, in response to user viewing requests, the corresponding structured data is retrieved from the storage module for statistical analysis and predictive analysis, so as to grasp the overall situation of drug addicts in drug rehabilitation facilities in real time, and predict the changing trends and potential risks of drug addicts, and finally realize scientific decision-making and refined management of drug rehabilitation work. BRIEF DESCRIPTION OF THE DRAWINGS

[0031] Figure 1 A schematic diagram of the structure of a drug rehabilitation data management system provided by an embodiment of the present application is shown;

[0032] Figure 2 A schematic diagram of the structure of a drug rehabilitation data management system provided by another embodiment of the present application is shown;

[0033] Figure 3 A visualization diagram showing the statistical results of the first category of structured data potential regularities provided by the embodiment of the present application is shown;

[0034] Figure 4 A visual representation of the prediction results provided by an embodiment of the present application that can characterize the trend changes of the second category of structured data is shown. DETAILED DESCRIPTION

[0035] In order to make the purpose, technical solution and advantages of the technical solution more clear, the technical solution is further described in detail below in conjunction with specific implementation methods. It should be understood that these descriptions are only exemplary and are not intended to limit the scope of the technical solution.

[0036] See also Figure 1 , Figure 1 This is a schematic diagram of the structure of the drug rehabilitation data management system provided in the embodiment of the present application. Figure 1 As shown, the drug rehabilitation data management system 100 includes:

[0037] The collection module 101 is used to collect the original data of drug addicts in real time through external access.

[0038] The processing module 102 is used to remove duplicate data and erroneous data from the original data and pre-process the remaining data to obtain the structured data of the drug addict.

[0039] The storage module 103 is used to store the structured data in a distributed manner in the drug rehabilitation personnel database 1031.

[0040] The analysis module 104 is used to identify the viewing request of the user and analyze the structured data corresponding to the viewing request to obtain the analysis result of the structured data.

[0041] The display module 105 is used to visually display the analysis results of the structured data for the user to review.

[0042] For further information, see Figure 2 , Figure 2 This is a schematic diagram of the structure of a drug rehabilitation data management system provided by another embodiment of the present application. Figure 2 As shown, the drug rehabilitation management system 100 also includes:

[0043] The external access module 106 is used to provide a software and hardware docking interface. On the one hand, it connects the drug rehabilitation business system 200 so as to collect the original data of the drug addicts from the drug rehabilitation business system 200, and on the other hand, it allows the third-party system 300 to call it so as to share the structured data with the third-party system 300.

[0044] The drug rehabilitation business system 200 includes: a drug rehabilitation government affairs system 201 , a drug rehabilitation law enforcement system 202 and a drug rehabilitation supervision system 203 .

[0045] like Figure 2 As shown, the external access module 106 is connected to the collection module 101 and the storage module 103. While collecting the original data of drug addicts from the drug rehabilitation service system 200, the structured data in the storage module 103 can also be shared with the drug rehabilitation service system 200. In addition, the external access module 106 is also connected to the third-party system 300 for sharing the structured data in the storage module 103 with the third-party system 300.

[0046] Furthermore, when collecting the original data of drug addicts in real time through external access, the collection module 101 is specifically used for:

[0047] According to the preset time intervals, the original data of drug addicts are collected from the drug addict government affairs system 201, the drug addict law enforcement system 202 and the drug addict supervision system 203 respectively;

[0048] Alternatively, for any of the drug addicts, when the original data of the drug addict in the drug rehabilitation government affairs system 201, the drug rehabilitation law enforcement system 202 and the drug rehabilitation supervision system 203 is updated, the updated original data of the drug addict will be synchronized to the drug rehabilitation management system 100.

[0049] In addition, the acquisition module 101 is also used to collect the original data of drug rehabilitation police officers and hardware facilities of drug rehabilitation facilities in real time.

[0050] Here, the drug rehabilitation service system 200 is also connected through the external access module 106, so as to collect the original data of drug rehabilitation police officers and hardware facilities of drug rehabilitation sites from the drug rehabilitation service system 200.

[0051] Furthermore, when the processing module 102 removes duplicate data and erroneous data from the original data and pre-processes the remaining data to obtain the structured data of the drug addict, the processing module 102 is specifically used to:

[0052] The law enforcement process data collected from the drug rehabilitation government affairs system 201, the government affairs business data collected from the drug rehabilitation law enforcement system 202, and the site safety management business data collected from the drug rehabilitation supervision system 203 are used as original data, the duplicate data and erroneous data in the original data are eliminated, and the remaining data are structured preprocessed to finally form standard structured data of drug rehabilitation personnel, providing a basis for drug rehabilitation data mining, visualization and external sharing.

[0053] Here, structured data refers to data that is logically expressed and implemented using a two-dimensional table structure, strictly follows data format and length specifications, and can be stored and managed through a relational database; the purpose of converting raw data into structured data in the embodiment of the present application is: in the drug rehabilitation data management scenario, structured data can meet the needs of high-speed storage applications, data backup needs, data sharing needs, and data disaster recovery needs.

[0054] In addition, the acquisition module 102 is also used to remove duplicate data and erroneous data from the original data of drug rehabilitation police officers and drug rehabilitation facility hardware facilities, and pre-process the remaining data to obtain structured data of drug rehabilitation police officers and drug rehabilitation facility hardware facilities.

[0055] Furthermore, the storage module 103 is also used to distribute and store the structured data of drug rehabilitation police officers in the drug rehabilitation police officer database 1032, and distribute and store the structured data of the hardware facilities of the drug rehabilitation facility in the drug rehabilitation facility hardware facilities database 1033; the drug rehabilitation personnel database 1031, the drug rehabilitation police officer database 1032 and the drug rehabilitation facility hardware facilities database 1033 are together composed.

[0056] Furthermore, the analysis module 104 includes:

[0057] The identification unit 1041 is used to identify the triggering object, request type and computing task of the viewing request; wherein the request type includes: potential rules and trend changes; the computing task includes: statistical computing task and predictive computing task.

[0058] Here, the potential law is a statistical analysis of the first category structured data of all drug addicts in any drug rehabilitation facility. Therefore, the computing task corresponding to the potential law is a statistical computing task; and the trend change is a predictive analysis of the second category structured data of any drug addict. Therefore, the computing task corresponding to the trend change is a predictive computing task.

[0059] Furthermore, the analysis module 104 also includes: a batch processing unit 1042;

[0060] When the viewing request is identified as a potential rule for the structured data, when analyzing the structured data corresponding to the viewing request to obtain the analysis result of the structured data, the batch processing unit 1042 is used to:

[0061] The first category of structured data corresponding to the viewing request is extracted from the drug rehabilitation database 1031, and the first category of structured data is batch processed according to the statistical calculation task of the viewing request to obtain statistical results that can characterize the potential rules of the first category of structured data.

[0062] Among them, the first category of structured data includes: gender, place of origin, age structure, educational level, marital status, years of drug abuse, disability status, type of drug abuse and employment status before admission.

[0063] Here, the batch processing unit 1042 first extracts the first category of structured data, and then performs the statistical calculation task in a batch processing manner.

[0064] Further, when the viewing request is identified as being actively triggered by the drug rehabilitation management system 100, when extracting structured data corresponding to the viewing request from the drug rehabilitation personnel database 1031, the batch processing unit 1042 is specifically used to:

[0065] According to a preset viewing rule, at least one first category structured data corresponding to the viewing request is extracted from the drug addict database.

[0066] Here, the preset viewing rule refers to a preset viewing request for at least one first category of structured data.

[0067] When the viewing request is identified as being passively triggered by the user, when extracting structured data corresponding to the viewing request from the drug addict database, the batch processing unit 1042 is specifically used to:

[0068] According to a viewing request for any first category structured data triggered by the user, the first category structured data corresponding to the viewing request is extracted from the drug rehabilitation personnel database.

[0069] Furthermore, the analysis module 104 further includes: a flow calculation unit 1043;

[0070] When the viewing request is identified as a trend change for any of the drug addicts, when analyzing the structured data corresponding to the viewing request to obtain the analysis result of the structured data, the stream computing unit 1043 is used to:

[0071] The second category structured data of the drug addict to be viewed is determined, and the second category structured data is stream-calculated using the prediction calculation task of the viewing request pre-arranged on the node of the second category structured data to obtain a prediction result that can characterize the trend change of the second category structured data of the drug addict.

[0072] Furthermore, the second category of structured data includes: scores of each stage of drug rehabilitation supervision; the drug rehabilitation supervision stages include: physiological detoxification period, educational adaptation period, rehabilitation consolidation period and regression guidance period.

[0073] Here, the stream computing unit 1043 is arranged on the node of the second category structured data, which node refers to the node of distributed storage, that is, the stream computing unit 1043 performs the prediction computing task by means of stream computing during the storage of the second category structured data; wherein, the prediction computing task is implemented by any of the following big data algorithms: decision tree algorithm, random forest algorithm, Pearson correlation coefficient and chi-square analysis; further, the trend change of the second category structured data is generated in real time, and the future performance of drug addicts in each stage of drug rehabilitation supervision can be predicted through the scores of the current stages of drug rehabilitation supervision, so as to show the overall situation, changing trends and potential risks of drug addicts. Then, by timely adjusting the rehabilitation plan and optimizing resource allocation, the rehabilitation effect and drug rehabilitation effect can be improved, and ultimately scientific decision-making and refined management of drug rehabilitation work can be achieved.

[0074] As an example, see Figure 3 and 4 , Figure 3 A visualization diagram of the statistical results of the embodiment of the present application that can characterize the potential regularities of the first category of structured data; Figure 4 A visual display diagram of the prediction results that can characterize the trend changes of the second category structured data provided in an embodiment of the present application.

[0075] The above contents are only preferred embodiments of the present invention. For ordinary technicians in this field, many changes can be made in the specific implementation methods and application scopes based on the ideas of the present technical content. As long as these changes do not deviate from the concept of the present invention, they all fall within the scope of protection of this patent.

Claims

1. A drug rehabilitation data management system, characterized in that: include: The collection module is used to collect the original data of drug addicts in real time through external access; A processing module, used to remove duplicate data and erroneous data from the original data, and pre-process the remaining data to obtain structured data of the drug addict; A storage module, used for distributing and storing the structured data in a drug rehabilitation personnel database; An analysis module, used for identifying a user's viewing request and analyzing structured data corresponding to the viewing request to obtain an analysis result of the structured data; The display module is used to visually display the analysis results of the structured data for the user to review.

2. The drug rehabilitation management system based on big data as claimed in claim 1, characterized in that: The drug rehabilitation management system also includes: The external access module is used to provide a software and hardware docking interface, on the one hand, to connect the drug rehabilitation business system so as to collect the original data of the drug rehabilitation personnel from the drug rehabilitation business system, and on the other hand, to allow a third-party system to call so as to share the structured data with the third-party system; Among them, the drug rehabilitation business system includes: drug rehabilitation government affairs system, drug rehabilitation law enforcement system and drug rehabilitation supervision system.

3. The drug rehabilitation management system based on big data as claimed in claim 2, characterized in that: When collecting the original data of drug addicts in real time through external access, the collection module is specifically used to: At preset time intervals, the original data of drug rehabilitation personnel are collected from the drug rehabilitation government affairs system, the drug rehabilitation law enforcement system and the drug rehabilitation supervision system respectively; Alternatively, for any of the drug addicts, when the original data of the drug addict in the drug rehabilitation government affairs system, the drug rehabilitation law enforcement system and the drug rehabilitation supervision system is updated, the updated original data of the drug addict will be synchronized to the drug rehabilitation management system.

4. The drug rehabilitation management system based on big data as claimed in claim 1, characterized in that: The analysis module comprises: The identification unit is used to identify the triggering object, request type and computing task of the viewing request; wherein the request type includes: potential rules and trend changes; the computing task includes: statistical computing tasks and predictive computing tasks.

5. The drug rehabilitation management system based on big data as claimed in claim 4, characterized in that: The analysis module also includes: a batch processing unit; When the viewing request is identified as a potential rule for the structured data, when analyzing the structured data corresponding to the viewing request to obtain an analysis result of the structured data, the batch processing unit is used to: The first category of structured data corresponding to the viewing request is extracted from the drug rehabilitation personnel database, and the first category of structured data is batch processed according to the statistical calculation task of the viewing request to obtain statistical results that can characterize the potential rules of the first category of structured data.

6. The drug rehabilitation management system based on big data as claimed in claim 5, characterized in that: When the viewing request is identified as being actively triggered by the drug rehabilitation management system, when extracting structured data corresponding to the viewing request from the drug rehabilitation personnel database, the batch processing unit is specifically used to: According to a preset viewing rule, extracting at least one first category of structured data corresponding to the viewing request from the drug addict database; When the viewing request is identified as being passively triggered by the user, when extracting structured data corresponding to the viewing request from the drug addict database, the batch processing unit is specifically used to: According to a viewing request for any first category structured data triggered by the user, the first category structured data corresponding to the viewing request is extracted from the drug rehabilitation personnel database.

7. The drug rehabilitation management system based on big data as claimed in claim 5, characterized in that: The first category of structured data includes: gender, place of origin, age structure, educational level, marital status, years of drug abuse, disability status, types of drug abuse and employment status before admission.

8. The drug rehabilitation management system based on big data as claimed in claim 4, characterized in that: The analysis module further includes: a stream computing unit; When the viewing request is identified as a trend change for any of the drug addicts, when analyzing the structured data corresponding to the viewing request to obtain the analysis result of the structured data, the stream computing unit is used to: The second category structured data of the drug addict to be viewed is determined, and the second category structured data is stream-calculated using the prediction calculation task of the viewing request pre-arranged on the node of the second category structured data to obtain a prediction result that can characterize the trend change of the second category structured data of the drug addict.

9. The drug rehabilitation management system based on big data as claimed in claim 8, characterized in that: The second category of structured data includes: scores of each stage of drug rehabilitation supervision; the drug rehabilitation supervision stages include: physiological detoxification period, educational adaptation period, rehabilitation consolidation period and regression guidance period.

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