A method for integrated processing of multi-source information

Abstract

The present invention relates to the technical field of multi-source information processing, and discloses a multi-source information integration processing method, comprising: acquiring target gas data of a target area; analyzing and processing the target gas data according to the target gas data through a data analysis strategy to form analysis data; judging and processing the analysis data according to the analysis data through a data judgment strategy to form a judgment result; checking and processing the target area according to the judgment result; evaluating the risk of the target area through a comprehensive analysis of multiple indicators to more comprehensively grasp the dangerous situation; accurately evaluating and controlling the risk according to the characteristics of different areas according to the specific situation of the target area, so as to improve the accuracy and effectiveness of risk response; providing more comprehensive and accurate information support for emergency disposal, making the emergency response more rapid and accurate, and reducing the degree of damage to personnel and property caused by accidents.

Description

Technical Field

[0001] The present invention relates to the technical field of multi-source information processing, and in particular to a multi-source information integration processing method. Background Art

[0002] Multi-source information integration processing refers to the process of collecting, integrating and processing data from different information sources. These information sources may come from different databases, files, networks or other data storage media. Multi-source information integration processing usually includes the following steps: Data collection: Collecting data from various sources, which may involve data extraction, data crawling or other methods; Data cleaning: Cleaning and preprocessing data to ensure data quality and consistency. This may include removing duplicates, handling missing values, solving problems such as inconsistent data formats; Data integration: Integrating data from different sources into a unified data storage or data model. This may involve operations such as data conversion, data mapping, and data merging; Data conversion: Converting data from its original format to a target format for subsequent analysis and processing. This may include data format conversion, data structure conversion, etc.; Data storage: Storing the integrated data in appropriate data storage media, such as databases, data warehouses, etc.; Data analysis: Analyzing and mining the integrated data to obtain useful information and insights; Data visualization: Presenting the analysis results in a visual way for users to understand and use. Multi-source information integration processing has applications in many fields, including data warehouse construction, business intelligence, and big data.

[0003] When detecting gas leaks, existing multi-source information integration processing usually uses gas sensors and alarm devices to determine whether there is a gas leak in the target area and perform alarm processing. However, the gas sensor will issue an abnormal alarm when it detects abnormal gas. During processing, it is impossible to obtain the gas leakage situation in time and perform corresponding processing, which can easily cause greater accidents. Its practicality has certain limitations. Summary of the invention

[0004] The present invention provides a multi-source information integration processing method. Through comprehensive analysis of multiple parameters and indicators, the risk of the target area can be evaluated from multiple dimensions such as the concentration of combustible materials, toxic properties, and the number of people, so as to more comprehensively grasp the dangerous situation; according to the specific situation of the target area, combined with different diffusion judgment strategies and data analysis strategies, risk assessment and control can be accurately carried out according to the characteristics of different areas, thereby improving the accuracy and effectiveness of risk response; the comprehensive diffusion judgment strategy and data analysis strategy provide more comprehensive and accurate information support for emergency disposal, making the emergency response faster and more accurate, and reducing the degree of damage to personnel and property caused by the accident. The problem that the existing multi-source information integration processing mentioned in the above background technology, when detecting gas leaks, mostly uses gas sensors and alarm equipment to determine whether there is a gas leak in the target area and performs alarm processing, but the gas sensor issues an abnormal alarm when detecting abnormal gas, and cannot obtain the gas leakage situation in time and perform corresponding processing during processing, which is easy to cause greater accidents, and its practicality has certain limitations.

[0005] The present invention provides the following technical solution: a multi-source information integration processing method, comprising: obtaining target gas data of a target area, wherein the target gas data specifically includes: type and concentration data of the gas in the target area;

[0006] The gas concentration data includes gas properties and gas distribution;

[0007] According to the target gas data, the target gas data is analyzed and processed through the data analysis strategy to form analysis data to analyze the gas situation in the target area, so as to judge and process the analysis data to determine whether there is gas anomaly in the target area, and then investigate and process the target area;

[0008] According to the analysis data, the analysis data is judged and processed through the data judgment strategy to form a judgment result, which is used to judge the level of the target gas data in the target area, and then the target area is graded;

[0009] According to the determination results, the target area is checked and processed;

[0010] The data analysis strategy is specifically as follows:

[0011] Establish a gas risk factor level model;

[0012] Obtain target leakage data in the gas risk factor level model;

[0013] The target leakage data specifically includes the type and amount of leaked gas;

[0014] Execute data judgment strategy based on target leakage data.

[0015] As an optional solution of the multi-source information integration processing method described in the present invention, the gas risk coefficient level model is specifically:

[0016] The target area is provided with a number of sensors for acquiring gas data;

[0017] S100, obtaining any target gas data through any sensor and defining it as target leakage data;

[0018] S110, analyzing the target leakage data through the data determination strategy;

[0019] S120, obtaining standard results and risk results of the current target leakage data;

[0020] S130, presetting a first risk threshold, a second risk threshold, and a third risk threshold;

[0021] S131. When the risk result corresponding to any type of target leakage data is greater than the second risk threshold, determining a risk value accumulation gradient;

[0022] S132, updating the initial risk coefficient based on the risk value accumulation gradient and the standard result;

[0023] S133, when the risk result corresponding to any type of target leakage data is greater than the third risk threshold, executing the first process;

[0024] S134. When the updated risk coefficient is less than or equal to the first risk threshold, the updated risk coefficient is set as the first risk threshold and used as the starting risk coefficient for the next analysis;

[0025] S140, obtaining target gas data of a next sensor and defining it as second leakage data;

[0026] S150, recognizing the second leakage data as target leakage data, and executing S100-S150; until all sensors are acquired to obtain the risk coefficient.

[0027] As an optional solution of the multi-source information integration processing method described in the present invention, the data analysis strategy specifically includes:

[0028] S200, obtaining gas properties in the target area, wherein the gas properties include gas flammability and toxicity, which are defined as a target gas flammability value and a target gas toxicity value;

[0029] S210, presetting a first leakage flammability threshold and a first leakage toxicity threshold; (used to determine whether the target gas is flammable)

[0030] S220, performing flammability determination on the target gas data of the target area, and comparing the target gas flammability value with the first leakage flammability threshold;

[0031] S221. If the target gas flammability value is less than the first leakage flammability threshold, the gas flammability risk coefficient of the target area is set to the first flammability;

[0032] S222: If the target gas flammability value is ≧ the first leakage flammability threshold, the gas property risk coefficient of the target area is set to the second flammability, and the first diffusion determination strategy is executed.

[0033] As an optional solution of the multi-source information integration processing method described in the present invention, the first diffusion determination strategy is specifically:

[0034] Obtaining the concentration information of combustion-supporting materials in the target area;

[0035] The combustion-supporting material concentration information specifically includes: fire source information and oxygen concentration in the target area;

[0036] Setting oxygen thresholds;

[0037] comparing the oxygen concentration in the target area with the oxygen threshold;

[0038] If the oxygen concentration is ≧ the oxygen threshold, the danger value of the target area is determined to be the first value, which is recorded as Q1;

[0039] If the oxygen concentration is less than the oxygen threshold, the fire source location in the target area is obtained;

[0040] Compare the distance values ​​between all fire sources and the target area, sort them in order from small to large, and form a fire source location set V, recorded as V1...Vn;

[0041] Set the first threshold value of fire source distance;

[0042] Extract the number of fire sources in the fire source location set V whose distance value from all fire sources to the target area is less than or equal to the first threshold of the fire source distance, and record it as the first number of fire sources;

[0043] If the number of first fire sources ≧ 1, the danger value of the target area is determined to be the first value, recorded as Q1;

[0044] If the number of the first fire sources = 0, the second diffusion determination strategy is executed.

[0045] As an optional solution of the multi-source information integration processing method of the present invention, the second diffusion determination strategy is specifically:

[0046] Obtain historical flammable dispersion data;

[0047] The historical flammable diffusion data include historical flammable gas concentration, historical flammable diffusion wind force and historical flammable diffusion distance;

[0048] Calculate the flammable distance ratio, specifically: flammable distance ratio = historical flammable diffusion distance ÷ (historical flammable diffusion wind speed ÷ historical flammable gas concentration);

[0049] Get target weather conditions;

[0050] The target weather conditions include the target wind direction and target wind force for the day;

[0051] Calculate the first flammable diffusion distance, specifically: first flammable diffusion distance = (target wind force ÷ target gas flammability value) × flammable distance ratio;

[0052] The flammable determination area is divided with the target area as the center point, the target wind direction as the direction, and the first flammable diffusion distance as the distance;

[0053] Obtain the number of fire sources in the flammable determination area, recorded as the second fire source number;

[0054] If the number of second fire sources is ≧1, the danger value of the target area is determined to be the second value, recorded as Q2;

[0055] If the number of the first fire sources = 0, the danger value of the target area is determined to be the third value, recorded as Q3.

[0056] As an optional solution of the multi-source information integration processing method described in the present invention, the data analysis strategy further includes:

[0057] S230, determining the toxicity of the target gas data in the target area, and comparing the toxicity value of the target gas with the first leakage toxicity threshold;

[0058] S231, if the target gas toxicity value is less than the first leakage toxicity threshold, the gas toxicity risk coefficient of the target area is set to the first toxicity, which is recorded as B1;

[0059] S232: If the target gas toxicity value is less than the first leakage toxicity threshold, the gas toxicity risk coefficient of the target area is set to the second toxicity, recorded as B2, and the third diffusion determination strategy is executed.

[0060] As an optional solution of the multi-source information integration processing method described in the present invention, the third diffusion determination strategy is specifically:

[0061] Get the number of people in the target area;

[0062] If the number of personnel is ≧1, the danger value of the target area is determined to be the first value, recorded as Q1;

[0063] If the number of personnel = 0, a toxic diffusion strategy is implemented in the target area;

[0064] The toxic diffusion strategy is:

[0065] The historical toxic diffusion data include historical toxic gas concentration, historical toxic diffusion wind speed and historical toxic diffusion distance;

[0066] Calculate the toxic distance ratio, specifically: toxic distance ratio = historical toxic diffusion distance ÷ (historical toxic diffusion wind speed ÷ historical toxic gas concentration);

[0067] Calculate the first toxic diffusion distance, specifically: first toxic diffusion distance = (target wind force ÷ target gas toxicity value) × toxic distance ratio;

[0068] The toxicity determination area is divided with the target area as the center point, the target wind direction as the direction, and the first distance of toxicity diffusion as the distance;

[0069] Get the target buildings around the target area;

[0070] Obtain the number of target buildings in the toxicity determination area, recorded as the number of the first building;

[0071] If the number of the first building = 0, the danger value of the target area is determined to be the third value, denoted as Q3;

[0072] If the first number of buildings is ≧1, the danger value of the target area is determined to be the first value, which is recorded as Q1.

[0073] As an optional solution of the multi-source information integration processing method of the present invention, wherein: obtaining the danger values ​​of all sensors;

[0074] Determine whether there is a first value Q1 among all dangerous values ​​of sensors;

[0075] If the first value Q1 exists, obtain the gas property determination corresponding to the first value Q1;

[0076] If the corresponding gas property is determined to be flammable, the first level of processing is performed on the target area;

[0077] If the corresponding gas property is determined to be toxic, personnel in the target area will be warned to wear protective masks and evacuate, and then the first level of treatment will be implemented;

[0078] The first level of treatment is as follows: stop activities in the target area, organize relevant departments to clean up the gas, and allocate medical resources;

[0079] If the first value Q1 does not exist, the risk values ​​of all sensors are added together to form a processed value.

[0080] As an optional solution of the multi-source information integration processing method described in the present invention, the gas determination strategy is specifically as follows;

[0081] Obtaining a first risk threshold and a second risk threshold;

[0082] If the processing value is ≦ the first risk threshold, the target gas data monitoring will continue in the target area;

[0083] If the first risk threshold < processing value ≦ the second risk threshold, the second level processing is performed on the target area;

[0084] The second level of processing is as follows:

[0085] Obtain particle diffusion distribution of target gas data;

[0086] Determine the particle diffusion distribution;

[0087] If the particle diffusion distribution is upper diffusion, the target gas in the target area is protected by upper automatic diffusion;

[0088] If the particle diffusion distribution is lower diffusion, the target gas in the target area is protected by lower automatic diffusion;

[0089] If the treatment value > the second risk threshold, the third level treatment is performed on the target area;

[0090] The third level of treatment is as follows:

[0091] According to the particle diffusion distribution, a combination of automatic diffusion protection and manual processing is adopted.

[0092] The present invention has the following beneficial effects:

[0093] 1. This multi-source information integration processing method, by setting up multiple sensors and combining data analysis strategies, can more comprehensively analyze the gas situation in the target area and improve the accuracy of gas detection; by establishing a gas risk coefficient model and analyzing and judging the target leakage data, it can make a grade judgment based on the specific gas situation in the target area, and use different processing methods according to different judgment levels to avoid accidents and ensure personal and property safety, while reducing unnecessary processing operations and saving manpower and resources.

[0094] 2. This multi-source information integration processing method can evaluate the risks of the target area from multiple dimensions such as the concentration of combustible materials, toxic properties, and the number of people through comprehensive analysis of multiple parameters and indicators, so as to grasp the dangerous situation more comprehensively; according to the specific conditions of the target area, combined with different diffusion determination strategies and data analysis strategies, it can accurately carry out risk assessment and control according to the characteristics of different areas, and improve the accuracy and effectiveness of risk response; the comprehensive diffusion determination strategy and data analysis strategy provide more comprehensive and accurate information support for emergency disposal, making the emergency response more rapid and accurate, and reducing the degree of damage to personnel and property caused by accidents. BRIEF DESCRIPTION OF THE DRAWINGS

[0095] Figure 1 This is a block diagram of a multi-source information integration processing method of the present invention. DETAILED DESCRIPTION

[0096] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0097] Example 1, see Figure 1 , a multi-source information integration processing method, comprising: obtaining target gas data of a target area, wherein the target gas data specifically includes: type and concentration data of the gas in the target area;

[0098] Gas concentration data includes gas properties and gas distribution;

[0099] According to the target gas data, the target gas data is analyzed and processed through the data analysis strategy to form analysis data to analyze the gas situation in the target area, so as to judge and process the analysis data to determine whether there is gas anomaly in the target area, and then investigate and process the target area;

[0100] According to the analysis data, the analysis data is judged and processed through the data judgment strategy to form a judgment result, which is used to judge the level of the target gas data in the target area, and then the target area is graded;

[0101] According to the determination results, the target area is checked and processed;

[0102] Data analysis strategy, specifically:

[0103] Establish a gas risk factor level model;

[0104] Obtain target leakage data in the gas risk factor level model;

[0105] The target leakage data specifically includes the type and amount of leaked gas;

[0106] Execute data judgment strategy based on target leakage data.

[0107] Gas risk factor level model, specifically:

[0108] The target area is provided with a number of sensors for acquiring gas data, including a gas sensor, a smoke sensor, a spectrum sensor and a temperature sensor;

[0109] S100, obtaining any target gas data through any sensor and defining it as target leakage data;

[0110] S110, analyzing the target leakage data through the data determination strategy;

[0111] S120, obtaining standard results and risk results of the current target leakage data;

[0112] S130, presetting a first risk threshold, a second risk threshold, and a third risk threshold;

[0113] S131. When the risk result corresponding to any type of target leakage data is greater than the second risk threshold, determining a risk value accumulation gradient;

[0114] S132, updating the initial risk coefficient based on the risk value accumulation gradient and the standard result;

[0115] S133, when the risk result corresponding to any type of target leakage data is greater than the third risk threshold, executing the first process;

[0116] S134. When the updated risk coefficient is less than or equal to the first risk threshold, the updated risk coefficient is set as the first risk threshold and used as the starting risk coefficient for the next analysis;

[0117] S140, obtaining target gas data of a next sensor and defining it as second leakage data;

[0118] S150, recognizing the second leakage data as target leakage data, and executing S100-S150; until all sensors are acquired to obtain the risk coefficient.

[0119] Through the above scheme, by setting up multiple sensors and combining data analysis strategies, the gas situation in the target area can be analyzed more comprehensively to improve the accuracy of gas detection; by establishing a gas risk coefficient model and analyzing and judging the target leakage data, the level can be judged according to the specific situation of the gas in the target area, and different processing methods can be used according to different judgment levels to avoid accidents and ensure personal and property safety, while reducing unnecessary processing operations and saving manpower and resources.

[0120] Example 2, see Figure 1 This embodiment is an improvement made on the basis of the first embodiment, wherein the first diffusion determination strategy is specifically:

[0121] Obtaining the concentration information of combustion-supporting materials in the target area;

[0122] The concentration information of the combustion-supporting materials specifically includes: the fire source information and oxygen concentration in the target area, and the fire source information includes high-voltage wires, high-temperature objects or ignition sources, etc.;

[0123] Setting oxygen thresholds;

[0124] comparing the oxygen concentration in the target area with the oxygen threshold;

[0125] If the oxygen concentration is ≧ the oxygen threshold, the danger value of the target area is determined to be the first value, which is recorded as Q1;

[0126] If the oxygen concentration is less than the oxygen threshold, the fire source location in the target area is obtained;

[0127] Compare the distance values ​​between all fire sources and the target area, sort them in order from small to large, and form a fire source location set V, recorded as V1...Vn;

[0128] Setting a first threshold of fire source distance to determine whether there is a fire source in the room;

[0129] Extract the number of fire sources in the fire source location set V whose distance value from all fire sources to the target area is less than or equal to the first threshold of the fire source distance, and record it as the first number of fire sources;

[0130] If the number of first fire sources is ≧1, the danger value of the target area is determined to be the first value, denoted as Q1, indicating that there is a fire source in the room, and the highest level judgment is directly performed;

[0131] If the number of the first fire sources = 0, the second diffusion determination strategy is executed, which means that there is no fire source in the room.

[0132] Data analysis strategies also include:

[0133] S230, determining the toxicity of the target gas data in the target area, and comparing the toxicity value of the target gas with the first leakage toxicity threshold;

[0134] S231, if the target gas toxicity value is less than the first leakage toxicity threshold, the gas toxicity risk coefficient of the target area is set to the first toxicity, which is recorded as B1;

[0135] S232: If the target gas toxicity value is less than the first leakage toxicity threshold, the gas toxicity risk coefficient of the target area is set to the second toxicity, recorded as B2, and the third diffusion determination strategy is executed.

[0136] The third diffusion determination strategy is as follows:

[0137] Get the number of people in the target area;

[0138] If the number of personnel is ≧1, the danger value of the target area is determined to be the first value, recorded as Q1;

[0139] If the number of personnel = 0, a toxic diffusion strategy is implemented in the target area;

[0140] The toxic diffusion strategy is:

[0141] Historical toxic dispersion data include historical toxic gas concentrations, historical toxic dispersion wind speeds, and historical toxic dispersion distances;

[0142] Calculate the toxic distance ratio, specifically: toxic distance ratio = historical toxic diffusion distance ÷ (historical toxic diffusion wind speed ÷ historical toxic gas concentration);

[0143] Calculate the first toxic diffusion distance, specifically: first toxic diffusion distance = (target wind force ÷ target gas toxicity value) × toxic distance ratio;

[0144] The toxicity determination area is divided with the target area as the center point, the target wind direction as the direction, and the first distance of toxicity diffusion as the distance;

[0145] Obtain target buildings around the target area, where the target buildings are residential areas or crop planting areas around the target area;

[0146] Obtain the number of target buildings in the toxicity determination area, recorded as the number of the first building;

[0147] If the number of the first building = 0, the danger value of the target area is determined to be the third value, denoted as Q3;

[0148] If the number of the first building is ≧1, the danger value of the target area is determined to be the first value, denoted as Q1;

[0149] Get the dangerous values ​​of all sensors;

[0150] Determine whether there is a first value Q1 among all dangerous values ​​of sensors;

[0151] If the first value Q1 exists, obtain the gas property determination corresponding to the first value Q1;

[0152] If the corresponding gas property is determined to be flammable, the first level of processing is performed on the target area;

[0153] If the corresponding gas property is determined to be toxic, personnel in the target area will be warned to wear protective masks and evacuate, and then the first level of treatment will be implemented;

[0154] The first level of treatment is as follows: stop activities in the target area, organize relevant departments to clean up the gas, and allocate medical resources;

[0155] If the first value Q1 does not exist, the dangerous values ​​of all sensors are added together to form a processed value;

[0156] Gas determination strategy, specifically:

[0157] Obtaining a first risk threshold and a second risk threshold;

[0158] If the processing value is ≦ the first risk threshold, the target gas data monitoring will continue in the target area;

[0159] If the first risk threshold < processing value ≦ the second risk threshold, the second level processing is performed on the target area;

[0160] The second level of processing is as follows:

[0161] Obtain particle diffusion distribution of target gas data;

[0162] Determine the particle diffusion distribution;

[0163] If the particle diffusion distribution is upper diffusion, the target gas in the target area is protected by upper automatic diffusion;

[0164] If the particle diffusion distribution is lower diffusion, the target gas in the target area is protected by lower automatic diffusion;

[0165] If the treatment value > the second risk threshold, the third level treatment is performed on the target area;

[0166] The third level of treatment is as follows:

[0167] According to the particle diffusion distribution, a combination of automatic diffusion protection and manual processing is adopted

[0168] In this embodiment, through comprehensive analysis of multiple parameters and indicators, the risk of the target area can be evaluated from multiple dimensions such as the concentration of combustible materials, toxic properties, and the number of people, so as to grasp the dangerous situation more comprehensively; according to the specific situation of the target area, combined with different diffusion determination strategies and data analysis strategies, risk assessment and control can be accurately carried out according to the characteristics of different areas, thereby improving the accuracy and effectiveness of risk response; the comprehensive diffusion determination strategy and data analysis strategy provide more comprehensive and accurate information support for emergency disposal, making the emergency response faster and more accurate, and reducing the degree of damage to personnel and property caused by accidents.

[0169] The electronic devices in the embodiments of the present disclosure may include, but are not limited to, mobile terminals such as laptop computers, PADs (tablet computers), vehicle-mounted terminals (such as vehicle-mounted navigation terminals), and the like, as well as fixed terminals.

[0170] Electronic devices may include processing devices (e.g., central processing units, graphics processing units, etc.), which may perform various appropriate actions and processes according to programs stored in a read-only memory (ROM) or programs loaded from a storage device into a random access memory (RAM). In RAM, various programs and data required for the operation of the electronic device are also stored. The processing device, ROM, and RAM are connected to each other via a bus. An input / output (I / O) interface is also connected to the bus.

[0171] Typically, the following devices may be connected to the I / O interface: input devices including, for example, a touch screen, a touch pad, an image sensor, a microphone, etc.; output devices including, for example, a liquid crystal display (LCD), a speaker, etc.; storage devices including, for example, a magnetic tape, a hard disk, etc.; and communication devices. The communication devices may allow the electronic device to communicate with other devices wirelessly or by wire to exchange data.

[0172] The embodiment of the present disclosure also provides a non-transitory computer-readable storage medium, which stores computer instructions, and the computer instructions are used to enable the computer to execute a multi-source information integration processing method in the aforementioned method embodiment.

[0173] It should be noted that more specific examples of computer-readable storage media may include portable computer disks, hard disks, erasable programmable read-only memories (EPROMs or flash memories), optical storage devices, magnetic storage devices, or any suitable combination thereof. In the present disclosure, computer-readable storage media may be any tangible medium that contains or stores a program, which may be used by or in conjunction with an instruction execution system, apparatus, or device, and the computer-readable medium may be contained in the electronic device; or it may exist independently without being assembled into the electronic device.

[0174] The computer-readable medium carries one or more programs. When the one or more programs are executed by the electronic device, the electronic device can implement the solution provided by the method embodiment.

[0175] Computer program code for performing operations of the present disclosure may be written in one or more programming languages ​​or a combination thereof, and may be executed entirely on a user's computer, partially on a user's computer, as a stand-alone software package, partially on a user's computer and partially on a remote computer, or entirely on a remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., through the Internet using an Internet service provider).

[0176] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device.

[0177] The above is only a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the technical principles of the present invention. These improvements and modifications should also be regarded as the scope of protection of the present invention.

Claims

1. A multi-source information integration processing method, characterized in that: include: Acquire target gas data of the target area, wherein the target gas data specifically includes: type and concentration data of the gas in the target area; The gas concentration data includes gas properties and gas distribution; According to the target gas data, the target gas data is analyzed and processed through the data analysis strategy to form analysis data to analyze the gas situation in the target area, so as to judge and process the analysis data to determine whether there is gas anomaly in the target area, and then investigate and process the target area; According to the analysis data, the analysis data is judged and processed through the data judgment strategy to form a judgment result, which is used to judge the level of the target gas data in the target area, and then the target area is graded; According to the determination results, the target area is checked and processed; The data analysis strategy is specifically as follows: Establish a gas risk factor level model; Obtain target leakage data in the gas risk factor level model; The target leakage data specifically includes the type and amount of leaked gas; Execute data judgment strategy based on target leaked data; The gas risk coefficient level model is specifically: The target area is provided with a number of sensors for acquiring gas data; S100, obtaining any target gas data through any sensor and defining it as target leakage data; S110, analyzing the target leakage data through the data determination strategy; S120, obtaining standard results and risk results of the current target leakage data; S130, presetting a first risk threshold, a second risk threshold, and a third risk threshold; S131. When the risk result corresponding to any type of target leakage data is greater than the second risk threshold, determining a risk value accumulation gradient; S132, updating the initial risk coefficient based on the risk value accumulation gradient and the standard result; S133, when the risk result corresponding to any type of target leakage data is greater than the third risk threshold, executing the first process; S134. When the updated risk coefficient is less than or equal to the first risk threshold, the updated risk coefficient is set as the first risk threshold and used as the starting risk coefficient for the next analysis; S140, obtaining target gas data of a next sensor and defining it as second leakage data; S150, recognizing the second leakage data as the target leakage data, and executing S100-S150; until all sensors are acquired to obtain the risk coefficient; The data analysis strategy specifically includes: S200, obtaining gas properties in the target area, wherein the gas properties include gas flammability and toxicity, which are defined as a target gas flammability value and a target gas toxicity value; S210, presetting a first leakage flammability threshold and a first leakage toxicity threshold; S220, performing flammability determination on the target gas data of the target area, and comparing the target gas flammability value with the first leakage flammability threshold; S221. If the target gas flammability value is less than the first leakage flammability threshold, the gas flammability risk coefficient of the target area is set to the first flammability; S222, if the target gas flammability value is ≧ the first leakage flammability threshold, the gas property risk coefficient of the target area is set to the second flammability, and the first diffusion determination strategy is executed; The first diffusion determination strategy is specifically: Obtaining the concentration information of combustion-supporting materials in the target area; The combustion-supporting material concentration information specifically includes: fire source information and oxygen concentration in the target area; Setting oxygen thresholds; comparing the oxygen concentration in the target area with the oxygen threshold; If the oxygen concentration is ≧ the oxygen threshold, the danger value of the target area is determined to be the first value, which is recorded as Q1; If the oxygen concentration is less than the oxygen threshold, the fire source location in the target area is obtained; Compare the distance values ​​between all fire sources and the target area, sort them in order from small to large, and form a fire source location set V, recorded as V1...Vn; Set the first threshold value of fire source distance; Extract the number of fire sources in the fire source location set V whose distance value from all fire sources to the target area is less than or equal to the first threshold of the fire source distance, and record it as the first number of fire sources; If the number of first fire sources ≧ 1, the danger value of the target area is determined to be the first value, recorded as Q1; If the number of the first fire source = 0, the second diffusion determination strategy is executed; The second diffusion determination strategy is specifically: Obtain historical flammable dispersion data; The historical flammable diffusion data include historical flammable gas concentration, historical flammable diffusion wind force and historical flammable diffusion distance; Calculate the flammable distance ratio, specifically: flammable distance ratio = historical flammable diffusion distance ÷ (historical flammable diffusion wind speed ÷ historical flammable gas concentration); Get target weather conditions; The target weather conditions include the target wind direction and target wind force for the day; Calculate the first flammable diffusion distance, specifically: first flammable diffusion distance = (target wind force ÷ target gas flammability value) × flammable distance ratio; The flammable determination area is divided with the target area as the center point, the target wind direction as the direction, and the first flammable diffusion distance as the distance; Obtain the number of fire sources in the flammable determination area, recorded as the second fire source number; If the number of second fire sources is ≧1, the danger value of the target area is determined to be the second value, recorded as Q2; If the number of the first fire sources = 0, the danger value of the target area is determined to be the third value, recorded as Q3.

2. A multi-source information integration processing method according to claim 1, characterized in that: The data analysis strategy also includes: S230, determining the toxicity of the target gas data in the target area, and comparing the toxicity value of the target gas with the first leakage toxicity threshold; S231, if the target gas toxicity value is less than the first leakage toxicity threshold, the gas toxicity risk coefficient of the target area is set to the first toxicity, which is recorded as B1; S232: If the target gas toxicity value is less than the first leakage toxicity threshold, the gas toxicity risk coefficient of the target area is set to the second toxicity, recorded as B2, and the third diffusion determination strategy is executed.

3. A multi-source information integration processing method according to claim 2, characterized in that: The third diffusion determination strategy is as follows: Get the number of people in the target area; If the number of personnel is ≧1, the danger value of the target area is determined to be the first value, recorded as Q1; If the number of personnel = 0, a toxic diffusion strategy is implemented in the target area; The toxic diffusion strategy is: Historical toxic dispersion data include historical toxic gas concentrations, historical toxic dispersion wind speeds, and historical toxic dispersion distances; Calculate the toxic distance ratio, specifically: toxic distance ratio = historical toxic diffusion distance ÷ (historical toxic diffusion wind speed ÷ historical toxic gas concentration); Calculate the first toxic diffusion distance, specifically: first toxic diffusion distance = (target wind force ÷ target gas toxicity value) × toxic distance ratio; The toxicity determination area is divided with the target area as the center point, the target wind direction as the direction, and the first distance of toxicity diffusion as the distance; Get the target buildings around the target area; Obtain the number of target buildings in the toxicity determination area, recorded as the number of the first building; If the number of the first building = 0, the danger value of the target area is determined to be the third value, denoted as Q3; If the first number of buildings is ≧1, the danger value of the target area is determined to be the first value, which is recorded as Q1.

4. A multi-source information integration processing method according to claim 3, characterized in that: Get the dangerous values ​​of all sensors; Determine whether there is a first value Q1 among all dangerous values ​​of sensors; If the first value Q1 exists, obtain the gas property determination corresponding to the first value Q1; If the corresponding gas property is determined to be flammable, the first level of processing is performed on the target area; If the corresponding gas property is determined to be toxic, personnel in the target area will be warned to wear protective masks and evacuate, and then the first level of treatment will be implemented; The first level of treatment is as follows: stop activities in the target area, organize relevant departments to clean up the gas, and allocate medical resources; If the first value Q1 does not exist, the risk values ​​of all sensors are added together to form a processed value.

5. A multi-source information integration processing method according to claim 4, characterized in that: Gas determination strategy, specifically: Obtaining a first risk threshold and a second risk threshold; If the processing value is ≦ the first risk threshold, the target gas data monitoring will continue in the target area; If the first risk threshold < processing value ≦ the second risk threshold, the second level processing is performed on the target area; The second level of processing is as follows: Obtain particle diffusion distribution of target gas data; Determine the particle diffusion distribution; If the particle diffusion distribution is upper diffusion, the target gas in the target area is protected by upper automatic diffusion; If the particle diffusion distribution is lower diffusion, the target gas in the target area is protected by lower automatic diffusion; If the treatment value > the second risk threshold, the third level treatment is performed on the target area; The third level of treatment is as follows: According to the particle diffusion distribution, a combination of automatic diffusion protection and manual processing is adopted.

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