Intelligent airbag pressure data analysis management system and method based on artificial intelligence

Abstract

The invention discloses an intelligent air bag pressure data analysis management system and method based on artificial intelligence, and relates to the technical field of air bag pressure management, and the method comprises the steps: analyzing the equipment conditions of intelligent air bag equipment under different monitoring indexes; acquiring medical sign data of a user using the intelligent air bag equipment, acquiring historical medical sign records of historical users using the intelligent air bag equipment, and analyzing the medical sign imitation degree between the historical users and the users to obtain a target imitation historical user; acquiring a historical pressure setting record of a target similar historical user, acquiring pressure data preset by the user, and analyzing a matching degree between a pressure index in the pressure data and the user to obtain target pressure data; and based on the target pressure data, controlling the air bag pressure of the intelligent air bag equipment used by the user, evaluating the equipment state of the intelligent air bag equipment in combination with the target operation data, and performing intelligent management on the intelligent air bag equipment.

Description

Technical Field

[0001] The present invention relates to the technical field of airbag pressure management, and in particular to an intelligent airbag pressure data analysis and management system and method based on artificial intelligence. Background Art

[0002] At present, with the widespread application of smart airbag devices in the medical field, there are more and more scenarios for using smart airbag devices. For example, when a person suffers from traumatic bleeding, medical personnel will use smart airbag devices and place them at the bleeding site. By inflating the air, the local pressure of the body is increased, which can effectively compress the peripheral blood vessels and reduce blood flow to the wound, thereby controlling bleeding. At the same time, medical personnel also need to accurately adjust the pressure of the smart airbag device to control the applied pressure and achieve treatment for bleeding people.

[0003] However, in real life, not only may problems arise during the use of smart airbag devices, but the physical conditions of users of smart airbag devices are different. Different users need to set different airbag pressure data based on their actual physical conditions. This generally requires medical personnel to set different airbag pressure data based on the user's actual physical condition and needs to treat and rehabilitate the user. However, even the airbag pressure data set by professionals takes a lot of time, which not only wastes time, but may even cause further harm to the user's body due to misjudgment of the user's actual situation. Summary of the invention

[0004] The purpose of the present invention is to provide an intelligent airbag pressure data analysis management system and method based on artificial intelligence to solve the problems raised in the prior art.

[0005] To achieve the above object, the present invention provides the following technical solution: an intelligent airbag pressure data analysis and management method based on artificial intelligence, the method comprising:

[0006] Step S100: obtaining equipment failure time data of the intelligent airbag equipment, obtaining historical equipment monitoring records of the intelligent airbag equipment, analyzing equipment conditions of the intelligent airbag equipment under different monitoring indicators, and obtaining target operation data;

[0007] Step S200: obtaining medical vital sign data of the user using the smart airbag device, obtaining historical medical vital sign records of historical users using the smart airbag device, analyzing the similarity of medical vital signs between historical users and users, and obtaining target similar historical users;

[0008] Step S300: Obtain the historical pressure setting records of the target user, obtain the pressure data preset by the user, analyze the degree of fit between the pressure index in the pressure data and the user, and obtain the target pressure data;

[0009] Step S400: Based on the target pressure data, the airbag pressure of the smart airbag device used by the user is controlled, and in combination with the target operation data, the device status of the smart airbag device is evaluated to perform intelligent management of the smart airbag device.

[0010] Furthermore, step S100 includes:

[0011] Step S101: acquiring equipment failure time data of the smart airbag device, the equipment failure time data including the historical period in which the smart airbag device fails, acquiring historical equipment monitoring records of the smart airbag device, and marking the historical equipment monitoring records within the historical period;

[0012] Step S102: Obtain data corresponding to various monitoring indicators of the intelligent airbag device from historical device monitoring records;

[0013] For example, various testing indicators include device temperature, device voltage, etc.;

[0014] Step S103: analyzing the equipment status of the intelligent airbag device under various monitoring indicators, wherein the equipment status of the intelligent airbag device under the monitoring indicator item a is analyzed, and the specific process is as follows:

[0015] Get the preset threshold value B of the monitoring indicator item a in the intelligent airbag device a , obtain the maximum value B of the monitoring indicator of item a in a marked historical device monitoring record max and minimum value B min , when B max >B a And B min max When a certain historical device monitoring record is recorded as a marked historical device monitoring record;

[0016] Step S104: Calculate the mark change range C of the monitoring indicator item a in the marked historical equipment monitoring record a =B a,max -B a,min / B a,△ , where B a,max To mark the minimum value of the monitoring indicator item a in the historical equipment monitoring record;

[0017] Step S105: Obtain the minimum value C of the change range of the mark of the a-th monitoring indicator in each mark history device monitoring record of the a-th monitoring indicator a，min ;

[0018] When the minimum value C a，min is less than the preset mark change amplitude threshold C′, and the minimum value C is obtained.​a The minimum value of the monitoring indicator item a in the corresponding marked historical device monitoring record is recorded as the target threshold value of the monitoring indicator item a in the smart airbag device;

[0019] When the minimum value C a ≥C′, the threshold B a It is recorded as the target threshold of the monitoring indicator in item a;

[0020] Step S106: Obtain target thresholds of various monitoring indicators in the smart airbag device, and aggregate them to obtain target operating data of the smart airbag device.

[0021] Further, step S200 includes:

[0022] Step S201: obtaining the medical vital signs data of the user, and obtaining the medical text information input by the staff on the platform according to the user's description from the medical vital signs data;

[0023] Step S202: pre-processing the medical text information, extracting and aggregating the keywords in the medical text information, and obtaining the user's marked keyword group;

[0024] Step S203: Calculate the marked word frequency of each keyword in the marked keyword group, where the marked word frequency E of the dth keyword in the marked keyword group is d :

[0025]

[0026] Among them, G d,sum is the total number of times the dth keyword appears in the medical text information; j is the total number of keywords in the marked keyword group; G i,sum is the total number of times the i-th keyword in the tag keyword group appears in the medical text information;

[0027] Step S204: Obtaining various historical medical text information Q stored in the preset cloud platform sum , calculate the token inverse document frequency F of the dth keyword d :

[0028]

[0029] Among them, Q d,sum is the total number of historical medical text information containing the dth keyword;

[0030] Calculate the tag value S of the dth keyword a =E d ×F d, obtain the tag value of each keyword in the user's tag keyword group, and based on the tag value of each keyword, perform vector conversion on the tag keyword group to obtain the user's first medical sign vector W 1 ;

[0031] Step S205: Obtain the historical medical vital sign records of each historical user who used the smart airbag device in the historical period, and analyze the similarity between each historical user and the user. The specific process of analyzing the similarity between the βth historical user and the user is as follows:

[0032] Obtain various historical medical vital sign records generated during the use of the smart airbag device by the β-th historical user, obtain a first medical vital sign vector of the historical user from the historical medical vital sign records, obtain the average values ​​corresponding to various medical indicators of the β-th historical user from the historical medical vital sign records, and normalize the average values ​​corresponding to various medical indicators to construct a second medical vital sign vector of the β-th historical user in the historical medical vital sign records;

[0033] For example, various medical indicators include weight, height, etc.;

[0034] Obtain the average value corresponding to each medical indicator from the user's medical sign data, and construct the user's second medical sign vector W 2 ;

[0035] Calculate the similarity between the user and the βth historical user in each historical medical sign record, where the similarity between the user and the βth historical user in the αth historical medical sign record is H β,α :

[0036]

[0037] Wherein, γ1 and γ2 represent the preset first characteristic coefficient and the second characteristic coefficient respectively, γ1>0, γ2>0, γ1+γ2=1; W 2 β,α W is represented as the second medical sign vector of the βth historical user in the αth historical medical sign record; 1 β,α It is represented as the first medical sign vector of the βth historical user in the αth historical medical sign record;

[0038] Step S206: When the medical signs are similar to each other β,α If the similarity is greater than a preset threshold, it is determined that the medical signs between the user and the βth historical user in the αth historical medical sign record are similar, the αth historical medical sign record is used as the user's marked historical medical sign record, and the βth historical user is recorded as the user's target similar historical user.

[0039] Furthermore, step S300 includes:

[0040] Step S301: Obtain the marked historical medical feature records of each target similar historical user of the user, obtain the historical pressure setting records generated by the target similar user using the smart airbag device based on the marked historical medical records, mark them, and obtain the values ​​of various pressure indicators in the smart airbag device from the marked historical pressure setting records;

[0041] For example, various pressure indicators include the highest pressure, lowest pressure, pressurization delay, and pressure relief delay in the smart airbag device;

[0042] Step S302: obtaining the preset pressure data before the user uses the smart airbag device, the pressure data including the values ​​of various pressure indicators, analyzing the degree of fit between the various pressure indicators in the pressure data and the user, wherein the degree of fit between the mth pressure indicator in the pressure data and the user is analyzed, and the specific process is as follows:

[0043] Obtain each marked historical pressure setting record of each target similar historical user, sort them in chronological order according to the time point when the historical pressure setting record was generated, and record each marked historical pressure setting record as each reference historical pressure setting record of the user;

[0044] Step S303: Obtain the average value K′ of the mth pressure index in each reference historical pressure setting record m , calculate the characteristic value U of the mth pressure index m :

[0045]

[0046] Where n is the total number of reference historical pressure setting records; K′ m,z The value of the mth pressure indicator in the zth reference historical pressure setting record;

[0047] Get the characteristic range K of the mth pressure index m =[K′ m -λ×U m ,K′ m +λ×U m ], where λ is the preset characteristic coefficient. When the value of the mth pressure index in the pressure data is not within the characteristic range, it is determined that the mth pressure index in the pressure data does not match the user, and the average value K′ is used. m , replace the value of the mth pressure indicator in the pressure data to obtain the target reference value of the mth pressure indicator;

[0048] On the contrary, it is determined that the mth pressure indicator in the pressure data matches the user, and the value of the mth pressure indicator in the pressure data is not processed, and the value of the mth pressure indicator in the pressure data is used as the target reference value of the mth pressure indicator;

[0049] Step S304: Obtain target reference values ​​of various pressure indicators of the user on the smart airbag device, and collect them to obtain target pressure data of the user using the smart airbag device.

[0050] Furthermore, step S400 includes:

[0051] Step S401: Acquire target pressure data, set various pressure indicators of the intelligent airbag device, and control the airbag pressure of the intelligent airbag device during use by the user;

[0052] Step S402: monitor the use of the smart airbag device in the current cycle, and evaluate the smart airbag device based on the target operation data of the smart airbag device. The specific process is as follows:

[0053] When the value of a monitoring indicator in the smart airbag device is greater than the target threshold, it is determined that the smart airbag device has a risk of equipment failure, the smart airbag device is adjusted, and an alarm is issued to perform intelligent management of the smart airbag device;

[0054] In the above steps, the smart airbag device is set through the target pressure data. The operator does not need to have professional medical knowledge to achieve the reasonable use of the smart airbag device. In addition, considering the possibility of equipment failure during actual use of the smart airbag device, which may cause harm to the user, the smart airbag device is evaluated and measures are taken in advance to ensure the user's physical safety.

[0055] In order to better implement the above method, an intelligent airbag pressure data analysis and management system based on artificial intelligence is also proposed, the system includes a target operation data module, a similarity analysis module, a pressure data analysis module, and an intelligent management module;

[0056] The target operation data module is used to analyze the equipment status of the intelligent airbag equipment under different monitoring indicators and obtain the target operation data;

[0057] A similarity analysis module is used to analyze the similarity of medical signs between historical users and users to obtain target similar historical users;

[0058] The pressure data analysis module is used to analyze the degree of fit between the pressure indicators in the pressure data and the user to obtain the target pressure data;

[0059] The intelligent management module is used to control the airbag pressure of the intelligent airbag device used by the user according to the target pressure data, and to evaluate the device status of the intelligent airbag device in combination with the target operation data, so as to perform intelligent management on the intelligent airbag device.

[0060] Further, the target operation data module includes a data marking unit and a target operation data unit;

[0061] A data marking unit, used to obtain the historical time period when the intelligent airbag device fails, and mark the historical equipment monitoring records within the historical time period;

[0062] The target operation data unit is used to collect the target thresholds of various monitoring indicators in the intelligent airbag device to obtain target operation data.

[0063] Furthermore, the similarity analysis module includes a vector construction unit and a similarity analysis unit;

[0064] A vector construction unit, used to construct a first medical sign vector and a second medical sign vector of a user;

[0065] The similarity analysis unit is used to analyze the similarity of medical signs between historical users and users according to the first medical sign vector and the second medical sign vector to obtain a target similar historical user.

[0066] Further, the pressure data analysis module includes a characteristic range unit and a pressure data analysis unit;

[0067] A characteristic range unit, used to obtain the characteristic range of each pressure indicator in the user's pressure data;

[0068] The pressure data analysis unit is used to analyze the degree of fit between each pressure indicator and the user according to the characteristic range of each pressure indicator to obtain target pressure data.

[0069] Further, the intelligent management module includes an intelligent management unit;

[0070] The intelligent management unit is used to set various pressure indicators of the intelligent airbag device, and to control the airbag pressure of the intelligent airbag device during user use, thereby performing intelligent management of the intelligent airbag device.

[0071] Compared with the prior art, the beneficial effects of the present invention are as follows: the present invention realizes intelligent management of the intelligent airbag device by performing intelligent analysis on the airbag pressure data in the intelligent airbag device. Taking into account the practical problem that in the process of using the intelligent airbag device to treat patients, the pressure index of the intelligent airbag device needs to be manually set. By analyzing the characteristics of users using the device and the historical data of the intelligent airbag device, the target pressure data is obtained, so that people without professional knowledge can also use the intelligent airbag device to treat users. BRIEF DESCRIPTION OF THE DRAWINGS

[0072] Figure 1 It is a method flow chart of an intelligent airbag pressure data analysis and management method based on artificial intelligence of the present invention;

[0073] Figure 2 It is a module schematic diagram of an intelligent airbag pressure data analysis and management system based on artificial intelligence of the present invention. DETAILED DESCRIPTION

[0074] 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.

[0075] Example: Figure 1-Figure 2 As shown, the present invention provides a technical solution, an intelligent airbag pressure data analysis and management method based on artificial intelligence, the method comprising:

[0076] Step S100: obtaining equipment failure time data of the intelligent airbag equipment, obtaining historical equipment monitoring records of the intelligent airbag equipment, analyzing equipment conditions of the intelligent airbag equipment under different monitoring indicators, and obtaining target operation data;

[0077] Wherein, step S100 includes:

[0078] Step S101: acquiring equipment failure time data of the smart airbag device, the equipment failure time data including the historical period in which the smart airbag device fails, acquiring historical equipment monitoring records of the smart airbag device, and marking the historical equipment monitoring records within the historical period;

[0079] Step S102: Obtain data corresponding to various monitoring indicators of the intelligent airbag device from historical device monitoring records;

[0080] Step S103: analyzing the equipment status of the intelligent airbag device under various monitoring indicators, wherein the equipment status of the intelligent airbag device under the monitoring indicator item a is analyzed, and the specific process is as follows:

[0081] Get the preset threshold value B of the monitoring indicator item a in the intelligent airbag device a , obtain the maximum value B of the monitoring indicator of item a in a marked historical device monitoring record max and minimum value B min , when B max >B a And B min max When a certain historical device monitoring record is recorded as a marked historical device monitoring record;

[0082] Step S104: Calculate the mark change range C of the monitoring indicator item a in the marked historical equipment monitoring record a =B a,max -B a,min / B a,△ , where B a,max To mark the minimum value of the monitoring indicator item a in the historical equipment monitoring record;

[0083] Step S105: Obtain the minimum value C of the change range of the mark of the a-th monitoring indicator in each mark history device monitoring record of the a-th monitoring indicator a，min ;

[0084] When the minimum value C a，min is less than the preset mark change amplitude threshold C′, and the minimum value C is obtained. a The minimum value of the monitoring indicator item a in the corresponding marked historical device monitoring record is recorded as the target threshold value of the monitoring indicator item a in the smart airbag device;

[0085] When the minimum value C a ≥C′, the threshold B a It is recorded as the target threshold of the monitoring indicator in item a;

[0086] Step S106: obtaining target thresholds of various monitoring indicators in the smart airbag device, and collecting them to obtain target operation data of the smart airbag device;

[0087] Step S200: obtaining medical vital sign data of the user using the smart airbag device, obtaining historical medical vital sign records of historical users using the smart airbag device, analyzing the similarity of medical vital signs between historical users and users, and obtaining target similar historical users;

[0088] Wherein, step S200 includes:

[0089] ​Step S201: obtaining the medical vital signs data of the user, and obtaining the medical text information input by the staff on the platform according to the user's description from the medical vital signs data;

[0090] Step S202: pre-processing the medical text information, extracting and aggregating the keywords in the medical text information, and obtaining the user's marked keyword group;

[0091] Step S203: Calculate the marked word frequency of each keyword in the marked keyword group, where the marked word frequency E of the dth keyword in the marked keyword group is d :

[0092]

[0093] Among them, G d,sum is the total number of times the dth keyword appears in the medical text information; j is the total number of keywords in the marked keyword group; G i,sum is the total number of times the i-th keyword in the tag keyword group appears in the medical text information;

[0094] For example, j is 5; G 1,sum =20; G 2,sum =13; G 3,sum =15; G 4,sum =20; G 5,sum is 17; calculate the marked word frequency E1 of the first keyword in the marked keyword group:

[0095]

[0096] Step S204: Obtaining various historical medical text information Q stored in the preset cloud platform sum , calculate the token inverse document frequency F of the dth keyword d :

[0097]

[0098] Among them, Q d,sum is the total number of historical medical text information containing the dth keyword;

[0099] Calculate the tag value S of the dth keyword a =E d ×F d , obtain the tag value of each keyword in the user's tag keyword group, and based on the tag value of each keyword, perform vector conversion on the tag keyword group to obtain the user's first medical sign vector W 1 ;

[0100] Step S205: Obtain the historical medical vital sign records of each historical user who used the smart airbag device in the historical period, and analyze the similarity between each historical user and the user. The specific process of analyzing the similarity between the βth historical user and the user is as follows:

[0101] Obtain various historical medical vital sign records generated during the use of the smart airbag device by the β-th historical user, obtain a first medical vital sign vector of the historical user from the historical medical vital sign records, obtain the average values ​​corresponding to various medical indicators of the β-th historical user from the historical medical vital sign records, and normalize the average values ​​corresponding to various medical indicators to construct a second medical vital sign vector of the β-th historical user in the historical medical vital sign records;

[0102] Obtain the average value corresponding to each medical indicator from the user's medical sign data, and construct the user's second medical sign vector W 2 ;

[0103] Calculate the similarity between the user and the βth historical user in each historical medical sign record, where the similarity between the user and the βth historical user in the αth historical medical sign record is H β,α :

[0104]

[0105] Wherein, γ1 and γ2 represent the preset first characteristic coefficient and the second characteristic coefficient respectively, γ1>0, γ2>0, γ1+γ2=1; W 2 β,α W is represented as the second medical sign vector of the βth historical user in the αth historical medical sign record; 1 β,α It is represented as the first medical sign vector of the βth historical user in the αth historical medical sign record;

[0106] Step S206: When the medical signs are similar to each other β,α If the similarity is greater than a preset threshold, it is determined that the medical signs between the user and the βth historical user in the αth historical medical sign record are similar, the αth historical medical sign record is used as the marked historical medical sign record of the user, and the βth historical user is recorded as the target similar historical user of the user;

[0107] Step S300: Obtain the historical pressure setting records of the target user, obtain the pressure data preset by the user, analyze the degree of fit between the pressure index in the pressure data and the user, and obtain the target pressure data;

[0108] Wherein, step S300 includes:

[0109] Step S301: Obtain the marked historical medical feature records of each target similar historical user of the user, obtain the historical pressure setting records generated by the target similar user using the smart airbag device based on the marked historical medical records, mark them, and obtain the values ​​of various pressure indicators in the smart airbag device from the marked historical pressure setting records;

[0110] Step S302: obtaining the preset pressure data before the user uses the smart airbag device, the pressure data including the values ​​of various pressure indicators, analyzing the degree of fit between the various pressure indicators in the pressure data and the user, wherein the degree of fit between the mth pressure indicator in the pressure data and the user is analyzed, and the specific process is as follows:

[0111] Obtain each marked historical pressure setting record of each target similar historical user, sort them in chronological order according to the time point when the historical pressure setting record was generated, and record each marked historical pressure setting record as each reference historical pressure setting record of the user;

[0112] Step S303: Obtain the average value K′ of the mth pressure index in each reference historical pressure setting record m , calculate the characteristic value U of the mth pressure index m :

[0113]

[0114] Where n is the total number of reference historical pressure setting records; K′ m,z The value of the mth pressure indicator in the zth reference historical pressure setting record;

[0115] Get the characteristic range K of the mth pressure index m =[K′ m -λ×U m ,K′ m +λ×U m ], where λ is the preset characteristic coefficient. When the value of the mth pressure index in the pressure data is not within the characteristic range, it is determined that the mth pressure index in the pressure data does not match the user, and the average value K′ is used. m , replace the value of the mth pressure indicator in the pressure data to obtain the target reference value of the mth pressure indicator;

[0116] On the contrary, it is determined that the mth pressure indicator in the pressure data matches the user, and the value of the mth pressure indicator in the pressure data is not processed, and the value of the mth pressure indicator in the pressure data is used as the target reference value of the mth pressure indicator;

[0117] Step S304: obtaining target reference values ​​of various pressure indicators of the user on the smart airbag device, and collecting them to obtain target pressure data of the user using the smart airbag device;

[0118] Step S400: Based on the target pressure data, the airbag pressure of the smart airbag device used by the user is controlled, and in combination with the target operation data, the device status of the smart airbag device is evaluated, and the smart airbag device is intelligently managed;

[0119] Wherein, step S400 includes:

[0120] Step S401: Acquire target pressure data, set various pressure indicators of the intelligent airbag device, and control the airbag pressure of the intelligent airbag device during use by the user;

[0121] Step S402: monitor the use of the smart airbag device in the current cycle, and evaluate the smart airbag device based on the target operation data of the smart airbag device. The specific process is as follows:

[0122] When the value of a monitoring indicator in the smart airbag device is greater than the target threshold, it is determined that the smart airbag device has a risk of equipment failure, the smart airbag device is adjusted, and an alarm is issued to perform intelligent management of the smart airbag device;

[0123] In order to better implement the above method, an intelligent airbag pressure data analysis and management system based on artificial intelligence is also proposed, the system includes a target operation data module, a similarity analysis module, a pressure data analysis module, and an intelligent management module;

[0124] The target operation data module is used to analyze the equipment status of the intelligent airbag equipment under different monitoring indicators and obtain the target operation data;

[0125] A similarity analysis module is used to analyze the similarity of medical signs between historical users and users to obtain target similar historical users;

[0126] The pressure data analysis module is used to analyze the degree of fit between the pressure indicators in the pressure data and the user to obtain the target pressure data;

[0127] An intelligent management module is used to control the airbag pressure of the intelligent airbag device used by the user according to the target pressure data, and to evaluate the device status of the intelligent airbag device in combination with the target operation data, so as to perform intelligent management on the intelligent airbag device;

[0128] Among them, the target operation data module includes a data marking unit and a target operation data unit;

[0129] A data marking unit, used to obtain the historical time period when the intelligent airbag device fails, and mark the historical equipment monitoring records within the historical time period;

[0130] A target operation data unit is used to collect target thresholds of various monitoring indicators in the intelligent airbag device to obtain target operation data;

[0131] Among them, the similarity analysis module includes a vector construction unit and a similarity analysis unit;

[0132] A vector construction unit, used to construct a first medical sign vector and a second medical sign vector of a user;

[0133] A similarity analysis unit, configured to analyze the similarity of medical signs between historical users and users according to the first medical sign vector and the second medical sign vector, and obtain a target similar historical user;

[0134] Wherein, the pressure data analysis module includes a characteristic range unit and a pressure data analysis unit;

[0135] A characteristic range unit, used to obtain the characteristic range of each pressure indicator in the user's pressure data;

[0136] A pressure data analysis unit is used to analyze the degree of fit between each pressure indicator and the user according to the characteristic range of each pressure indicator to obtain target pressure data;

[0137] Wherein, the intelligent management module includes an intelligent management unit;

[0138] The intelligent management unit is used to set various pressure indicators of the intelligent airbag device, and to control the airbag pressure of the intelligent airbag device during user use, thereby performing intelligent management of the intelligent airbag device.

[0139] It will be apparent to those skilled in the art that the invention is not limited to the details of the exemplary embodiments described above and that the invention can be implemented in other specific forms without departing from the spirit or essential features of the invention. Therefore, the embodiments should be considered exemplary and non-limiting in all respects, and the scope of the invention is defined by the appended claims rather than the foregoing description, and it is intended that all variations falling within the meaning and scope of the equivalent elements of the claims be included in the invention. Any reference numeral in a claim should not be considered as limiting the claim to which it relates.

Claims

1. An intelligent airbag pressure data analysis and management method based on artificial intelligence, characterized in that: The method comprises: Step S100: obtaining equipment failure time data of the intelligent airbag device, obtaining historical equipment monitoring records of the intelligent airbag device, analyzing the equipment status of the intelligent airbag device under different monitoring indicators, and obtaining target operation data; Step S200: obtaining medical vital sign data of the user using the smart airbag device, obtaining historical medical vital sign records of historical users using the smart airbag device, analyzing the similarity between the medical vital signs of the historical users and the user, and obtaining a target similar historical user; Step S300: obtaining the historical pressure setting records of the target user, obtaining the pressure data preset by the user, analyzing the degree of fit between the pressure index in the pressure data and the user, and obtaining the target pressure data; Step S400: Based on the target pressure data, the airbag pressure of the smart airbag device used by the user is controlled, and in combination with the target operation data, the device status of the smart airbag device is evaluated to intelligently manage the smart airbag device.

2. The intelligent airbag pressure data analysis and management method based on artificial intelligence according to claim 1 is characterized in that: The step S100 includes: Step S101: acquiring device failure time data of the smart airbag device, the device failure time data including the historical time period in which the smart airbag device fails, acquiring historical device monitoring records of the smart airbag device, and marking the historical device monitoring records within the historical time period; Step S102: acquiring data corresponding to various monitoring indicators of the smart airbag device from the historical device monitoring records; Step S103: analyzing the equipment status of the smart airbag device under the monitoring indicators, wherein the equipment status of the smart airbag device under the monitoring indicator item a is analyzed, and the specific process is: Obtain the preset threshold value B of the monitoring indicator item a in the smart airbag device a , obtain the maximum value B of the monitoring indicator item a in a marked historical device monitoring record max and minimum value B min , when B max >B a And B min max When the monitoring record of a certain historical device is recorded as a marked historical device monitoring record;​ Step S104: Calculate the mark change range C of the item a monitoring indicator in the mark history device monitoring record a =B a,max -B a,min / B a,△ , where B a,max The minimum value of the monitoring indicator item a in the monitoring record of the marked historical device; Step S105: Obtain the minimum value C of the change range of the mark of the a-th monitoring indicator in each mark history device monitoring record of the a-th monitoring indicator a，min ; When the minimum value C a，min is smaller than the preset mark change amplitude threshold C′, and the minimum value C is obtained. a The minimum value of the monitoring indicator item a in the corresponding marked historical device monitoring record is recorded as the target threshold value of the monitoring indicator item a in the smart airbag device; When the minimum value C a ≥C′, the threshold B a Recorded as the target threshold of the monitoring indicator in item a; Step S106: Obtain target thresholds of various monitoring indicators in the smart airbag device, and aggregate them to obtain target operating data of the smart airbag device.

3. The intelligent airbag pressure data analysis and management method based on artificial intelligence according to claim 1 is characterized in that: The step S200 includes: Step S201: obtaining medical vital sign data of the user, and obtaining medical text information input by the staff on the platform according to the description of the user from the medical vital sign data; Step S202: pre-processing the medical text information, extracting and aggregating keywords in the medical text information, and obtaining the user's marked keyword group; Step S203: Calculate the marked word frequency of each keyword in the marked keyword group, wherein the marked word frequency E of the dth keyword in the marked keyword group is d : Among them, G d,sum is the total number of times the dth keyword appears in the medical text information; j is the total number of each keyword in the marked keyword group; G i,sum is the total number of times the i-th keyword in the marked keyword group appears in the medical text information; Step S204: Obtaining various historical medical text information Q stored in the preset cloud platform sum , calculate the labeled inverse document frequency F of the dth keyword d : Among them, Q d,sum is the total number of historical medical text information containing the dth keyword; Calculate the tag value S of the dth keyword a =E d ×F d , obtain the tag value of each keyword in the tag keyword group of the user, and based on the tag value of each keyword, perform vector conversion on the tag keyword group to obtain the first medical sign vector W of the user 1 ; Step S205: Obtain the historical medical vital sign records of each historical user who used the smart airbag device in the historical period, and analyze the similarity between each historical user and the user. The specific process of analyzing the similarity between the βth historical user and the user is as follows: Obtaining various historical medical vital sign records generated during the process of the β-th historical user using the smart airbag device, obtaining a first medical vital sign vector of the historical user from the historical medical vital sign records, obtaining average values ​​corresponding to various medical indicators of the β-th historical user from the historical medical vital sign records, and normalizing the average values ​​corresponding to various medical indicators to construct a second medical vital sign vector of the β-th historical user in the historical medical vital sign records; Obtain the average value corresponding to each medical indicator from the medical sign data of the user, and construct the second medical sign vector W of the user 2 ; Calculate the degree of similarity between the medical signs of the user and the βth historical user in each of the historical medical sign records, wherein the degree of similarity between the medical signs of the user and the βth historical user in the αth historical medical sign record is H β,α : Wherein, γ1 and γ2 represent the preset first characteristic coefficient and the second characteristic coefficient respectively, γ1>0, γ2>0, γ1+γ2=1; W 2 β,α W is represented by the second medical vital sign vector of the βth historical user in the αth historical medical vital sign record; 1 β,α Represented as the first medical vital sign vector of the β-th historical user in the α-th historical medical vital sign record; Step S206: When the medical signs are similar to each other β,α If the similarity is greater than a preset threshold, it is determined that the medical signs between the user and the β-th historical user in the α-th historical medical sign record are similar, and the α-th historical medical sign record is used as the marked historical medical sign record of the user, and the β-th historical user is recorded as the target similar historical user of the user.

4. The method for analyzing and managing airbag pressure data based on artificial intelligence according to claim 3 is characterized in that: The step S300 includes: Step S301: obtaining the marked historical medical feature records of each target similar historical user of the user, obtaining the historical pressure setting records generated by the target similar user using the smart airbag device based on the marked historical medical records, marking them, and obtaining the values ​​of various pressure indicators in the smart airbag device from the marked historical pressure setting records; Step S302: obtaining the preset pressure data of the user before using the smart airbag device, wherein the pressure data includes the values ​​of the various pressure indicators, and analyzing the degree of fit between the various pressure indicators in the pressure data and the user, wherein the degree of fit between the mth pressure indicator in the pressure data and the user is analyzed, and the specific process is as follows: Obtaining the marked historical pressure setting records of the target-similar historical users, sorting the historical pressure setting records in chronological order according to the time points when the historical pressure setting records were generated, and recording the marked historical pressure setting records as reference historical pressure setting records of the users; Step S303: Obtain the average value K′ of the m-th pressure index in each reference historical pressure setting record m , calculate the characteristic value U of the mth pressure index m : Wherein, n is the total number of the reference historical pressure setting records; K′ m,z is the value of the mth pressure indicator in the zth reference historical pressure setting record; Get the characteristic range K of the mth pressure index m =[K′ m -λ×U m ,K′ m +λ×U m ], where λ is a preset characteristic coefficient. When the value of the mth pressure indicator in the pressure data is not within the characteristic range, it is determined that the mth pressure indicator in the pressure data does not match the user, and the average value K′ is used. m , replacing the value of the m-th pressure indicator in the pressure data to obtain a target reference value of the m-th pressure indicator; On the contrary, it is determined that the mth pressure indicator in the pressure data matches the user, and the value of the mth pressure indicator in the pressure data is not processed, and the value of the mth pressure indicator in the pressure data is used as the target reference value of the mth pressure indicator; Step S304: Obtain target reference values ​​of the various pressure indicators of the user on the smart airbag device, and collect them to obtain target pressure data of the user using the smart airbag device.

5. The method for analyzing and managing airbag pressure data based on artificial intelligence according to claim 4 is characterized in that: The step S400 includes: Step S401: acquiring the target pressure data, setting various pressure indicators of the intelligent airbag device, and controlling the airbag pressure of the intelligent airbag device during use by the user; Step S402: monitoring the use of the smart airbag device in the current cycle, and evaluating the smart airbag device based on the target operation data of the smart airbag device, the specific process is as follows: When the value of a monitoring indicator of the smart airbag device is greater than a target threshold, it is determined that the smart airbag device has a risk of equipment failure, the smart airbag device is adjusted, an alarm is issued, and the smart airbag device is intelligently managed.

6. An intelligent airbag pressure data analysis and management system based on artificial intelligence, used to execute an intelligent airbag pressure data analysis and management method based on artificial intelligence as claimed in any one of claims 1 to 5, characterized in that: The system includes a target operation data module, a similarity analysis module, a pressure data analysis module, and an intelligent management module; The target operation data module is used to analyze the equipment status of the intelligent airbag equipment under different monitoring indicators to obtain target operation data; The similarity analysis module is used to analyze the similarity of medical signs between the historical user and the user to obtain a target similar historical user; The pressure data analysis module is used to analyze the degree of fit between the pressure index in the pressure data and the user to obtain target pressure data; The intelligent management module is used to control the airbag pressure of the intelligent airbag device used by the user according to the target pressure data, and to evaluate the device status of the intelligent airbag device in combination with the target operation data to perform intelligent management of the intelligent airbag device.

7. The intelligent airbag pressure data analysis and management system based on artificial intelligence according to claim 6 is characterized in that: The target operation data module includes a data marking unit and a target operation data unit; The data marking unit is used to obtain the historical period in which the intelligent airbag device fails, and mark the historical device monitoring records within the historical period; The target operation data unit is used to collect target thresholds of various monitoring indicators in the intelligent airbag device to obtain target operation data.

8. The intelligent airbag pressure data analysis and management system based on artificial intelligence according to claim 6 is characterized in that: The similarity analysis module includes a vector construction unit and a similarity analysis unit; The vector construction unit is used to construct a first medical sign vector and a second medical sign vector of the user; The similarity analysis unit is used to analyze the similarity of medical signs between the historical user and the user according to the first medical sign vector and the second medical sign vector, so as to obtain a target similar historical user.

9. The intelligent airbag pressure data analysis and management system based on artificial intelligence according to claim 6 is characterized in that: The pressure data analysis module includes a characteristic range unit and a pressure data analysis unit; The characteristic range unit is used to obtain the characteristic range of each pressure indicator in the pressure data of the user; The pressure data analysis unit is used to analyze the degree of fit between the various pressure indicators and the user according to the characteristic range of the various pressure indicators to obtain target pressure data.

10. The intelligent airbag pressure data analysis and management system based on artificial intelligence according to claim 6, characterized in that: The intelligent management module includes an intelligent management unit; The intelligent management unit is used to set various pressure indicators of the intelligent airbag device, and to control the airbag pressure of the intelligent airbag device during use by the user, thereby performing intelligent management on the intelligent airbag device.

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