A quarantine data collection integration method and system

Abstract

The application provides a quarantine data acquisition and integration method and system, the method comprising: pre-configuring an API interface corresponding to a quarantine processing system, judging the type of a target quarantine commodity, and calling the API interface of the corresponding quarantine processing system according to the type of the commodity; acquiring quarantine processing data of each quarantine commodity of different quarantine processing systems by using the API interface of the different quarantine processing systems, wherein the quarantine processing data comprises pre-treatment data and post-treatment data; standardizing the quarantine processing data of the different quarantine processing systems to obtain standardized quarantine processing data, and further performing data analysis and calculation on the standardized quarantine processing data; obtaining quarantine data before and after quarantine processing of the corresponding target quarantine commodity, respectively, and further performing detection analysis on the quarantine data before and after quarantine processing to judge the quarantine processing effect of the current target quarantine commodity.

Description

Technical Field

[0001] The present invention relates to the technical field of quarantine data collection, and in particular to a quarantine data collection and integration method and system. Background Art

[0002] Currently, invasive alien species have become a global environmental problem. Preventing and controlling alien species invasions and protecting biodiversity are crucial components of building a strong biosecurity barrier. The General Administration of Customs has emphasized the importance of maintaining national border security, continuously improving risk prevention and control capabilities, and strictly implementing monitoring, testing, and preventive disinfection supervision for imported cold chain foods, agricultural products, and high-risk non-cold chain containerized cargo.

[0003] Quarantine treatment refers to measures taken on inbound and outbound goods, vehicles, containers, and other quarantined items using biological, physical, and chemical methods. The goal is to eliminate epidemic risks or potential hazards, and prevent the spread of human infectious diseases and the introduction and export of animal and plant pests and diseases. This measure is an important component of entry-exit inspection and quarantine and is of great significance for safeguarding public health and ecological security.

[0004] Existing methods for processing quarantine data primarily rely on tracking quarantine data for human targets. Data processing for quarantined items remains separate and dependent on human data processing, as exemplified by the previously published patent CN114420305A. This patent utilizes a dynamic data analysis platform for quarantine of entry-exit personnel. However, data on individual items carried by entry-exit personnel under different quarantine conditions is not integrated and analyzed. Consequently, existing technologies still have significant loopholes in the quarantine process, hindering accurate analysis. Summary of the Invention

[0005] One of the inventive purposes of the present invention is to provide a quarantine data collection and integration method and system, wherein the method and system collect different quarantine data, and integrate the collected different quarantine data, perform data analysis and judgment after data analysis on the integrated quarantine data, and provide effective risk warnings for possible species invasions and disease transmission, thereby greatly improving the analysis and prevention and control effects of border quarantine.

[0006] Another object of the present invention is to provide a quarantine data collection and integration method and system, which detects, analyzes and processes diversified quarantine data based on commodities. The present invention utilizes a central control system to connect different types of quarantine processing systems, obtains the parameters of corresponding equipment in the quarantine processing system for data extraction, and integrates the data of different quarantine processing systems through different APIs, and forms standardized and unified quarantine data with the parameters of corresponding equipment in the quarantine processing system. The standardized and unified quarantine data of the integrated API is used to perform risk analysis of the quarantine data, thereby improving the effect of quarantine risk analysis and control.

[0007] Another object of the present invention is to provide a quarantine data collection and integration method and system. After obtaining the corresponding quarantined commodities, the method and system extract and analyze the quarantine data of the quarantined commodities before and after processing. After comparing and analyzing the quarantine data before and after processing of the quarantined commodities, the quarantine treatment effect of the quarantine treatment system itself can be effectively judged, and the treatment effects of different quarantine treatment systems can be comprehensively compared and analyzed, and the effect of the diversified quarantine treatment can be judged in an intuitive indexed form.

[0008] In order to achieve at least one of the above-mentioned objects, the present invention further provides a quarantine data collection and integration method, comprising the following steps:

[0009] Pre-configure the API interface corresponding to the quarantine processing system, determine the commodity type of the target quarantined commodity, and call the API interface of the corresponding quarantine processing system according to the commodity type;

[0010] Using API interfaces of different quarantine processing systems to obtain quarantine processing data of each quarantined commodity by different quarantine processing systems, wherein the quarantine processing data includes pre-quarantine processing data and post-quarantine processing data;

[0011] Performing standardized data integration on the quarantine processing data of the different quarantine processing systems to obtain standardized quarantine processing data, and further performing data analysis and calculation on the standardized quarantine processing data;

[0012] After analysis and calculation, quarantine data before and after quarantine treatment of the corresponding target quarantined commodity are obtained respectively, and the quarantine data before and after quarantine treatment are further tested and analyzed to determine the quarantine treatment effect of the current target quarantined commodity.

[0013] According to one of the preferred embodiments of the present invention, the different quarantine treatment systems include: a channel-type radioactivity detection system, a channel-type outer box spray disinfection system, a fumigation warehouse system, a biomedical waste high-temperature sterilization system and a harmless treatment system; wherein a highly structured XML API interface is configured for each of the different treatment systems, and the XML API interface is used to obtain numerical data of different data types in the different quarantine treatment systems, and the numerical data of different data types are subjected to standardized data processing to obtain the standardized quarantine treatment data, and data analysis is performed based on the standardized quarantine treatment data.

[0014] According to another preferred embodiment of the present invention, a data processing method for standardizing the numerical data obtained by the XML API interface includes: encapsulating a method for identifying the category of quarantined goods in the XML API interface, and configuring a key-value pair list of numerical data corresponding to the commodity category in the XML API interface, wherein the key name key in the key-value pair is the quarantine type of the corresponding quarantined commodity, and the Value is the quarantine value of the corresponding quarantined commodity, and structured data based on the quarantined commodity is constructed using the key-value pair list encapsulated by the XML API interface, wherein a unique identifier is configured for each target quarantined commodity, and the corresponding key-value pair list is called based on the unique identifier and the identified quarantined commodity type, and the corresponding quarantine data is automatically obtained as the key value according to the key name of the key-value pair list.

[0015] According to another preferred embodiment of the present invention, a data processing method for standardizing the numerical data obtained by the XML API interface includes: encapsulating a standardized numerical conversion method based on multivariate quarantine data in the XML API interface, specifically comprising the following steps: obtaining the maximum quarantine data S of the target commodity type according to the key name in the key-value list of the key-value pair; max and minimum quarantine data S min , and calculate the standard deviation σ of the quarantine data S of each commodity type, and perform standardization according to the following formula based on the standard deviation: , where M i represents the standardized quarantine data of different quarantine data i corresponding to the commodity type, represents the average value of the quarantine data of the corresponding commodity type, and the standardized quarantine data M of different quarantine data i of the corresponding commodity type i The key-value list is stored according to the key-value encapsulated by the XML API interface to obtain highly structured and standardized quarantine processing data M i .

[0016] According to another preferred embodiment of the present invention, after obtaining the standardized quarantine processing data M corresponding to the commodity type, i After that, the standardized quarantine processing data M i Perform data integration and analysis, and standardize quarantine processing data M according to the following formula i Integrated analysis of:

[0017] P= , where y i and w i are respectively based on the standardized quarantine processing data M i The corresponding commodity type pre-quarantine data and post-quarantine data; P represents the integrated analysis result data of multivariate quarantine data of the same target commodity type, e is the natural constant, and σ is the standard deviation.

[0018] According to another preferred embodiment of the present invention, the risk warning level V is configured based on the integrated analysis result data P of the multi-element quarantine data. j , where j is the corresponding level value, and the corresponding risk warning level V is pre-configured j quarantine control strategy, when the integrated analysis result data P of the multivariate quarantine data meets the corresponding risk warning level V j , then execute the corresponding quarantine control strategy;

[0019] Build an automated parameter modulation model: ,in, are intercept one and intercept two, are regression coefficients 1 and 2, are error terms one and two; are input variable one and input variable two;

[0020] Import the preset risk warning level configuration table, and determine the values ​​of error one and error two based on the risk warning level configuration table.

[0021] According to another preferred embodiment of the present invention, the standardized quarantine processing data M corresponding to the commodity type obtained through the XML API interface i After that, obtain the equipment parameters f corresponding to the quarantine process i and environmental parameters r i , and according to the device parameter f i and environmental parameters r i According to the corresponding risk warning level V jAutomatic parameter modulation is performed to ensure that the final risk warning level meets the quarantine requirements, wherein the equipment parameters include channel-type radioactivity detection equipment parameters, channel-type outer box spray disinfection equipment parameters, fumigation warehouse equipment parameters, biomedical waste high-temperature sterilization equipment parameters, harmless treatment equipment parameters, and environmental parameters r i Including temperature, humidity and air pressure.

[0022] According to another preferred embodiment of the present invention, after completing the corresponding standardized quarantine processing data M i After analysis, pre-configure the risk warning level V j The numerical range v is obtained based on the corresponding integrated analysis result data P, and the quarantine data i of the corresponding commodity type are evaluated in different dimensions, and a comprehensive evaluation is performed by weighted summation to obtain the quarantine data evaluation results of different dimensions.

[0023] In order to achieve at least one of the above-mentioned objects of the invention, the present invention further provides a quarantine data collection and integration system, which executes the above-mentioned quarantine data collection and integration method.

[0024] The present invention further provides a computer-readable storage medium, which stores a computer program, and the computer program is executed by a processor to implement the above-mentioned quarantine data collection and integration method. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] Figure 1 Shown is a flow chart of a quarantine data collection and integration method in the present invention. DETAILED DESCRIPTION

[0026] The following description is intended to disclose the present invention so that those skilled in the art can implement the present invention. The preferred embodiments described below are for illustrative purposes only, and those skilled in the art will readily appreciate other obvious variations. The basic principles of the present invention defined in the following description may be applied to other embodiments, variations, improvements, equivalents, and other technical solutions that do not depart from the spirit and scope of the present invention.

[0027] It is to be understood that the term "one" should be understood as "at least one" or "one or more", that is, in one embodiment, the number of an element may be one, while in another embodiment, the number of the elements may be multiple, and the term "one" should not be understood as a limitation on the quantity.

[0028] Please combine Figure 1The present invention discloses a quarantine data collection and integration method and system. The method is primarily directed to quarantine methods for imported and exported animals, plants, chilled and fresh products. The method includes, but is not limited to, detecting pathogens, parasites, and harmful organisms carried by animals, plants, chilled and fresh products, and their products, and processing the detection results. Because the types of quarantine processing data vary, and different quarantine processing data require different quarantine processing systems for data acquisition and processing, the present invention analyzes the quarantine processing data of these different quarantine processing systems to determine whether there is a quarantine risk and to evaluate the effectiveness of the quarantine processing results. The specific method includes the following steps:

[0029] S01. Pre-configure the API interface corresponding to the quarantine processing system, determine the commodity type of the target quarantine commodity, and call the API interface corresponding to the quarantine processing system according to the commodity type;

[0030] S02. Using API interfaces of different quarantine processing systems, obtain quarantine processing data of each quarantined commodity by different quarantine processing systems, wherein the quarantine processing data includes pre-quarantine processing data and post-quarantine processing data;

[0031] S03. performing standardized data integration on the quarantine processing data of the different quarantine processing systems to obtain standardized quarantine processing data, and further performing data analysis and calculation on the standardized quarantine processing data;

[0032] S04. After analysis and calculation, the quarantine data of the corresponding target quarantined commodity before and after quarantine treatment are obtained respectively, and the quarantine data before and after quarantine treatment are further tested and analyzed to determine the quarantine treatment effect of the current target quarantined commodity.

[0033] It should be noted that the quarantine method in the present invention is based on quarantined commodities. Due to the quarantine of different quarantined products, the different quarantine treatment systems include: a channel-type radioactivity detection system, a channel-type outer box spray disinfection system, a fumigation warehouse system, a biomedical waste high-temperature sterilization system, and a harmless treatment system; the method for determining the commodity type of the target quarantined commodity can adopt physical radio frequency tag recognition or based on existing mature image recognition models, such as including but not limited to the YOLO model.

[0034] In one preferred embodiment of the present application, an RFID reader is configured in the corresponding quarantine system, and an RFID tag is attached to the corresponding quarantine commodity. By setting the reading range of the RFID reader, when the RFID tag of the corresponding quarantine commodity is detected, the RFID reader can identify the RFID tag within the detection range, thereby identifying the type of the corresponding quarantine commodity. This method is efficient and accurate, and is particularly suitable for the rapid identification of large quantities of commodities.

[0035] Using the YOLO model as the target detection model, the type of quarantine commodity on the corresponding quarantine system path can be identified. By image acquisition and analysis of the quarantine commodity, the system can automatically obtain the quarantine result data of the commodity type. This method not only improves the quarantine efficiency, but also reduces the possibility of human error.

[0036] The quarantine system detects the radioactive data of the target quarantine commodity through the radiation detection system, wherein the unit of the radioactive data can be microsievert / hour (μSv / h). The path type outer box spray disinfection system detects the pathogen before the target quarantine commodity passes through the path type outer box spray disinfection system, wherein the pathogen detection data includes but is not limited to the bacterial content per unit area, or the pathogen content per unit volume of liquid, wherein the unit of the pathogen content detection data can be CFU / L (colony forming unit / L). The pathogen is detected after the target quarantine commodity passes through the path type outer box spray disinfection system, i.e. the pre-quarantine data and post-quarantine data are obtained. When the target commodity passes through the fumigation warehouse system, the microbial content data of the target commodity before fumigation can be detected as pre-quarantine data, and the microbial content data of the target commodity after fumigation can be detected as post-quarantine data. The unit of the microbial content can be CFU / L (colony forming unit / L). It should be noted that different target commodity types need to pass through different quarantine channels due to different target commodity types. Not all target commodity types pass through the same quarantine channel. The selection of the quarantine channel needs to be set according to the nature of the commodity, such as the target commodity type selected for the quarantine channel of the fumigation warehouse system, which is generally for commodity types such as grains, nuts and tea that are prone to mold, or for wood or textiles that are prone to mold and pests. For the channel type outer box spray disinfection system, it is generally for commodity types with outer packaging, which may be contaminated with pathogens or toxic and harmful substances on the surface. Spray disinfection is a relatively convenient quarantine treatment method. Similarly, the biological and pharmaceutical waste high-temperature sterilization system and the harmless treatment system.

[0037] It is worth mentioning that, because different quarantine systems have different data types and data units, and data exchange between different data types is difficult, the present invention performs the following data processing on the relevant quarantine data of different quarantine systems: highly structured XML API interfaces are configured for the different quarantine processing systems respectively, and the XML API interfaces in the different quarantine processing systems correspond to the URLs of the systems. The URL addresses of the systems corresponding to the XML API interfaces are the detection data and quarantine processing result data of the corresponding quarantine systems, wherein the XML API interfaces pre-encapsulate the XML files that need to construct standard and structured data, and encapsulate the processing method code of the XML files. The XML API interfaces are used to obtain the numerical data of different data types in the different quarantine processing systems, and the numerical data of the different data types are subjected to standardized data processing to obtain the standardized quarantine processing data, and data analysis is performed based on the standardized quarantine processing data.

[0038] The data processing method for standardizing the numerical data obtained by the XML API interface includes: encapsulating a method for identifying the category of quarantined goods through the XML API interface, which can be implemented, but not limited to, using an RFID tag list matching method. In the XML API interface, a key-value pair list corresponding to the numerical data of the commodity category is configured as an XML file of standard and structured data. The system will fill in the corresponding XML file based on the key name, where the key name (key) is the quarantine type of the corresponding quarantined commodity, and the value (Value) is the quarantine value of the corresponding quarantined commodity.

[0039] The quarantine commodity classification method, encapsulated through an XML API, improves the system's scalability and maintainability. It utilizes RFID tag list matching technology to accurately identify different types of quarantine commodities. By configuring key-value lists, standardized and structured data is stored in XML files. This ensures that every quarantine commodity entering the system is quickly and accurately classified and assigned a unique identifier.

[0040] When the system receives a new quarantine request, it first determines the quarantine type for the item based on the unique item identifier provided in the request and pre-defined identification rules. Then, based on this information, it automatically searches for a corresponding key-value pair list, where the key represents the specific quarantine item, and the value is the specific value or status description for that item. For example, if a piece of goods requires temperature sensitivity testing, the corresponding key might be "Temperature_Sensitivity," and the value might be "High" or "Low."

[0041] Furthermore, to further ensure data quality, the original information is verified and adjusted according to a series of pre-set conversion rules before being incorporated into the final XML document. This prevents information inconsistencies caused by input errors and ensures that all output files adhere to a unified formatting standard, facilitating subsequent data analysis and processing.

[0042] The XML API interface encapsulates the quarantine commodity category identification process, streamlining the entire operation and reducing the risk of human intervention. Furthermore, by introducing a highly customizable key-value list mechanism, the system can easily adapt to future changes in requirements. Whether adding new quarantine items or modifying existing rules, this can be accomplished by simply updating the relevant configuration files, eliminating the need for large-scale changes to the underlying code. This significantly improves the flexibility and adaptability of the software project.

[0043] The data processing method for standardizing the numerical data obtained by the XML API interface includes: encapsulating a standardized numerical conversion method based on multivariate quarantine data in the XML API interface, specifically including the following steps: obtaining the maximum quarantine data Smax and the minimum quarantine data Smin of the target commodity type according to the key name in the key-value list, and calculating the standard deviation σ of different quarantine data S of each commodity type, and performing standardization processing according to the standard deviation according to the following formula: , where M i represents the standardized quarantine data of different quarantine data i corresponding to the commodity type, Represents the average value of the quarantine data of the corresponding commodity type, and stores the standardized quarantine data Mi of the different quarantine data i of the corresponding commodity type in the key-Value list according to the key value of the XML API interface. i After that, the standardized quarantine processing data Mi is further integrated and analyzed, and the standardized quarantine processing data M is obtained according to the following formula: i Integrated analysis of:

[0044] P= , where y i and w i are respectively based on the standardized quarantine processing data M i The corresponding commodity type pre-quarantine data and post-quarantine data; P represents the integrated analysis result data of multivariate quarantine data of the same target commodity type, e is the natural constant, and σ is the standard deviation.

[0045] Furthermore, the present invention configures the risk warning level V based on the integrated analysis result data P of the multivariate quarantine data. j, where j is the corresponding level value, and the corresponding risk warning level V is pre-configured j quarantine control strategy, when the integrated analysis result data P of the multivariate quarantine data meets the corresponding risk warning level V j , then execute the corresponding quarantine control strategy.

[0046] The quarantine data collection and integration method and system obtain standardized quarantine processing data M i After that, we further obtain the equipment parameters f corresponding to the quarantine process i and environmental parameters r i , and according to these parameters according to the preset risk warning level V j Perform automatic parameter modulation. Specifically:

[0047] Build an automated parameter modulation model: ,in, are intercept one and intercept two, are regression coefficients 1 and 2, are error terms one and two; are input variable one and input variable two;

[0048] Import the preset risk warning level configuration table:

[0049] Risk Level Level value Error term 1 / Error term 2 <![CDATA[V1]]> 1 0.1 / 0.1 <![CDATA[V2]]> 2 0.11 / 0.11 V3 3 0.12 / 0.12

[0050] Collecting device parameters for a preset time period. In this embodiment, the collected device parameter data is temperature data. Average temperature data is calculated based on the collected temperature data. The average temperature data is input into the automated parameter adjustment model as an input variable. The temperature value of the device at the next moment is output, and the temperature parameters of the device are adjusted based on the temperature data at the next moment.

[0051] Similarly, multiple environmental parameters within a preset time length are collected, which in this embodiment are environmental humidity data.

[0052] Calculate the average humidity data based on the multiple environmental humidity data obtained, input the average humidity data as input variable 2 into the automatic parameter adjustment model, and output the environmental humidity value at the next moment;

[0053] According to the corresponding ambient humidity values, adjust the parameters of equipment such as air conditioners or accelerators to ensure that the environmental parameters meet the standards.

[0054] The quarantine treatment parameters are automatically adjusted based on real-time data to ensure that the quarantine treatment meets the predetermined standards. At the same time, by comprehensively evaluating different dimensions of data, the effectiveness of quarantine treatment can be more fully understood, providing more accurate decision support for relevant departments. In addition, this automatic parameter modulation reduces the possibility of human intervention, improving the efficiency and accuracy of quarantine treatment.

[0055] This modulation is to ensure that the final risk alert level meets the quarantine requirements, thereby ensuring the safety of plants, animals, chilled and fresh products.

[0056] Device parameters f i Key performance indicators of various quarantine treatment equipment, such as sensitivity of channel type radioactive detection equipment, disinfection efficiency of channel type outer box spray disinfection equipment, temperature and humidity control ability of fumigation warehouse equipment, sterilization effect of biological medicine waste high temperature sterilization equipment, and treatment capacity of harmless treatment equipment, etc. i Related to temperature, humidity, air pressure and other environmental factors that affect quarantine treatment effectiveness.

[0057] Risk alert level V j The numerical range v is based on the corresponding integrated analysis result data P, and a reasonable risk alert threshold range is set according to historical data and current data analysis results. This range will be evaluated by different dimensions of quarantine data, and the results of different dimensions of quarantine data evaluation will be obtained by weighted summation.

[0058] The processes described above with reference to the flowcharts can be implemented as computer software programs in accordance with embodiments of the present disclosure. Embodiments of the present disclosure include a computer program product comprising a computer program carried on a computer readable medium, the computer program comprising program code for performing the methods illustrated by the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via a communication section, and / or installed from a detachable medium. When the computer program is executed by a central processing unit (CPU), the above-described functions defined in the methods of the present application are performed. It should be noted that the computer readable medium of the present application can be a computer readable signal medium or a computer readable storage medium or any combination of the two. The computer readable storage medium may, for example, but not limited to, be an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus or device, or any suitable combination of the above. More specific examples of the computer readable storage medium can include, but are not limited to, an electrical connection having one or more wires, a portable computer diskette, 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 disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In the present application, the computer readable storage medium can be any tangible medium that contains or stores a program that can be used by or in connection with an instruction execution system, apparatus or device. In the present application, the computer readable signal medium can include a data signal carried in a baseband or as part of a carrier wave, in which the computer readable program code is carried. Such a propagated data signal can take many forms, including but not limited to, an electromagnetic signal, an optical signal, or any suitable combination of the above. The computer readable signal medium can also be any computer readable medium that can send, propagate or transfer a program for use by or in connection with an instruction execution system, apparatus or device. The program code contained on the computer readable medium can be transmitted by any suitable medium, including but not limited to, wireless, wire, optical cable, RF, or any suitable combination of the above.

[0059] The flow charts and block diagrams in the accompanying drawings illustrate the possible implementation architecture, functions and operations of the system, method and computer program product according to various embodiments of the present invention. In this regard, each box in the flow chart or block diagram can represent a module, program segment, or a part of code, and the module, program segment, or a part of code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in a different order than that marked in the accompanying drawings. For example, two boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram and / or flow chart, and the combination of the boxes in the block diagram and / or flow chart, can be implemented with a dedicated hardware-based system that performs the specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0060] Those skilled in the art should understand that the embodiments of the present invention described above and shown in the accompanying drawings are only examples and do not limit the present invention. The objectives of the present invention have been fully and effectively achieved. The functional and structural principles of the present invention have been demonstrated and explained in the embodiments. Without departing from the principles, the implementation methods of the present invention may be subject to any deformation or modification.

Claims

1. A quarantine data collection and integration method, characterized in that: The method comprises the following steps: Pre-configure the API interface corresponding to the quarantine processing system, determine the commodity type of the target quarantined commodity, and call the API interface of the corresponding quarantine processing system according to the commodity type; Using API interfaces of different quarantine processing systems to obtain quarantine processing data of each quarantined commodity by different quarantine processing systems, wherein the quarantine processing data includes pre-quarantine processing data and post-quarantine processing data; Performing standardized data integration on the quarantine processing data of the different quarantine processing systems to obtain standardized quarantine processing data, and further performing data analysis and calculation on the standardized quarantine processing data; After analysis and calculation, quarantine data of the corresponding target quarantined commodity before and after quarantine treatment are obtained respectively, and the quarantine data before and after quarantine treatment are further tested and analyzed. The process is as follows: After obtaining the standardized quarantine processing data M for the corresponding commodity type i After that, the standardized quarantine processing data M i Perform data integration and analysis, and standardize quarantine processing data M according to the following formula i Integrated analysis of: P= , where y i and w i are respectively based on the standardized quarantine processing data M i The corresponding commodity type pre-quarantine data and post-quarantine data; P represents the integrated analysis result of multivariate quarantine data of the same target commodity type, e is the natural constant, and σ is the standard deviation; Then, the quarantine treatment effect of the current target quarantined commodity is judged. The process is as follows: Configure risk warning level V based on the integrated analysis result data P of the multivariate quarantine data j , where j is the corresponding level value, and the corresponding risk warning level V is pre-configured j quarantine control strategy, when the integrated analysis result data P of the multivariate quarantine data meets the corresponding risk warning level V j , then execute the corresponding quarantine control strategy; Build an automated parameter modulation model: ,in, are intercept one and intercept two, are regression coefficients 1 and 2, are error terms one and two; are input variable one and input variable two; Import the preset risk warning level configuration table, and determine the values ​​of error one and error two based on the risk warning level configuration table.

2. A quarantine data collection and integration method according to claim 1, characterized in that: The different quarantine treatment systems include: a channel-type radioactivity detection system, a channel-type outer box spray disinfection system, a fumigation warehouse system, a biomedical waste high-temperature sterilization system and a harmless treatment system; wherein a highly structured XMLAPI interface is configured for each of the different treatment systems, and the XML API interface is used to obtain numerical data of different data types in the different quarantine treatment systems, and the numerical data of different data types are subjected to standardized data processing to obtain the standardized quarantine treatment data, and data analysis is performed based on the standardized quarantine treatment data.

3. A quarantine data collection and integration method according to claim 2, characterized in that: The data processing method for standardizing the numerical data obtained by the XML API interface includes: encapsulating a method for identifying the category of quarantined goods in the XML API interface, and configuring a key-value pair list of numerical data corresponding to the commodity category in the XML API interface, wherein the key name key in the key-value pair is the quarantine type of the corresponding quarantined commodity, and the Value is the quarantine value of the corresponding quarantined commodity, and the key-value pair list encapsulated by the XML API interface is used to construct structured data based on quarantined goods, wherein a unique identifier is configured for each target quarantined commodity, and the corresponding key-value pair list is called based on the unique identifier and the identified quarantined commodity type, and the corresponding quarantine data is automatically obtained as the key value according to the key name of the key-value pair list.

4. A quarantine data collection and integration method according to claim 3, characterized in that: The data processing method for standardizing the numerical data obtained by the XML API interface includes: encapsulating a standardized numerical conversion method based on multivariate quarantine data in the XML API interface, specifically including the following steps: obtaining the maximum quarantine data S of the target commodity type according to the key name in the key-value list; max and minimum quarantine data S min , and calculate the standard deviation σ of the quarantine data S of each commodity type, and perform standardization according to the following formula based on the standard deviation: , where M i represents the standardized quarantine data of different quarantine data i corresponding to the commodity type, represents the average value of the quarantine data of the corresponding commodity type, and the standardized quarantine data M of different quarantine data i of the corresponding commodity type i The key-value list is stored according to the key value of the XML API interface to obtain highly structured and standardized quarantine processing data M i .

5. A quarantine data collection and integration method according to claim 4, characterized in that: Standardized quarantine processing data M of the corresponding commodity type obtained through the XML API interface i After that, obtain the equipment parameters f corresponding to the quarantine process i and environmental parameters r i , and according to the device parameter f i and environmental parameters r i According to the corresponding risk warning level V j Automatic parameter modulation is performed to ensure that the final risk warning level meets the quarantine requirements, wherein the equipment parameter f i Including channel type radioactivity detection equipment parameters, channel type outer box spray disinfection equipment parameters, fumigation warehouse equipment parameters, biomedical waste high temperature sterilization equipment parameters, harmless treatment equipment parameters, environmental parameters r i Including temperature, humidity and air pressure.

6. A quarantine data collection and integration method according to claim 5, characterized in that: After completing the corresponding standardized quarantine processing data M i After analysis, pre-configure the risk warning level V j The numerical range v is obtained based on the corresponding integrated analysis result data P, and the quarantine data i of the corresponding commodity type are evaluated in different dimensions, and a comprehensive evaluation is performed by weighted summation to obtain the quarantine data evaluation results of different dimensions.

7. A quarantine data collection and integration system, characterized in that: The system executes a quarantine data collection and integration method as described in any one of claims 1-6 above.

8. A computer-readable storage medium, characterized in that The computer-readable storage medium stores a computer program, and the computer program is executed by a processor to implement a quarantine data collection and integration method as described in any one of claims 1 to 6.

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