Intelligent early warning system for hyperbaric oxygen chamber environment based on data analysis

Abstract

An intelligent early warning system for a hyperbaric oxygen chamber environment based on data analysis, configured to solve the problems that traditional hyperbaric oxygen chamber monitoring systems generally only have a simple data collection function and lack deep analysis capability for data, fail to detect potential anomalies in a timely manner and to issue early warnings, and consequently cannot ensure comfort and safety of patients during treatment. The intelligent early warning system for the hyperbaric oxygen chamber environment comprises the following modules: an environment early warning platform, a state monitoring module, a data analysis module, an anomaly alert module, and an environment monitoring module. By combining real-time monitoring and data analysis technologies, the intelligent early warning system for the hyperbaric oxygen chamber environment realizes omnidirectional monitoring and early warning of the state and environment of a hyperbaric oxygen chamber, thereby improving the response speed and handling capability of medical personnel to anomalies, and providing a strong guarantee for the treatment safety and comfort of patients.

Description

An oxygen pressurized cabin environment intelligent early warning system based on data analysis

[0001] Cross-reference to Related Applications

[0002] The present disclosure claims priority to the Chinese patent application No. CN202410438561.3, filed on April 12, 2024, and entitled "An oxygen pressurized cabin environment intelligent early warning system based on data analysis", the entire content of which is incorporated herein by reference. TECHNICAL FIELD

[0003] The present disclosure relates to the field of data processing, in particular to an oxygen pressurized cabin environment intelligent early warning system based on data analysis. BACKGROUND

[0004] An oxygen pressurized cabin is a medical device used for treating certain respiratory system diseases. By providing high-concentration oxygen into the cabin, the patient's respiratory condition is improved. However, during the use of the oxygen pressurized cabin, various abnormal situations may occur, such as abnormal state of the oxygen pressurized cabin, existence of faults, and abnormal internal environment of the oxygen pressurized cabin, which may pose a threat to the health of the patient. However, the traditional oxygen pressurized cabin monitoring system usually only has a simple data acquisition function and lacks deep analysis ability of data, which cannot timely discover potential abnormal situations and give early warnings, and cannot guarantee the comfort and safety of the patient during the treatment process.

[0005] DISCLOSURE

[0006] In order to overcome the above technical problems, the purpose of the present disclosure is to provide an oxygen pressurized cabin environment intelligent early warning system based on data analysis, which solves the problem that the traditional oxygen pressurized cabin monitoring system usually only has a simple data acquisition function and lacks deep analysis ability of data, which cannot timely discover potential abnormal situations and give early warnings, and cannot guarantee the comfort and safety of the patient during the treatment process.

[0007] The purpose of the present disclosure can be achieved by the following technical solutions:

[0008] An oxygen pressurized cabin environment intelligent early warning system based on data analysis, comprising:

[0009] An environment monitoring module configured to perform environment monitoring on the oxygen pressurized cabin after receiving an environment monitoring instruction, acquire environment abnormal information of the oxygen pressurized cabin, and send the environment abnormal information to a data analysis module; wherein the environment abnormal information includes oxygen deviation value OP, oxygen ratio value OB, humidity deviation value SP, humidity ratio value SB, temperature deviation value WP, and temperature ratio value WB.

[0010] The specific process of acquiring the environment abnormal information by the environment monitoring module is as follows:

[0011] After receiving the environmental monitoring instruction, the oxygen pressurized cabin is monitored, the average oxygen concentration in the oxygen pressurized cabin is obtained, and it is marked as the average concentration value JN, the preset oxygen concentration of the oxygen pressurized cabin is obtained, and it is marked as the preset concentration value YN, the difference between the average concentration value JN and the preset concentration value YN is obtained, and it is marked as the oxygen offset value OP, the ratio between the average concentration value JN and the preset concentration value YN is obtained, and it is marked as the oxygen ratio value OB;

[0012] The average humidity in the oxygen pressurized cabin is obtained, and it is marked as the average humidity value JS, the preset humidity of the oxygen pressurized cabin is obtained, and it is marked as the preset humidity value YS, the difference between the average humidity value JS and the preset humidity value YS is obtained, and it is marked as the humidity offset value SP, the ratio between the average humidity value JS and the preset humidity value YS is obtained, and it is marked as the humidity ratio value SB;

[0013] The average temperature in the oxygen pressurized cabin is obtained, and it is marked as the average temperature value JW, the preset temperature of the oxygen pressurized cabin is obtained, and it is marked as the preset temperature value YW, the difference between the average temperature value JW and the preset temperature value YW is obtained, and it is marked as the temperature offset value WP, the ratio between the average temperature value JW and the preset temperature value YW is obtained, and it is marked as the temperature ratio value WB;

[0014] The oxygen offset value OP, the oxygen ratio value OB, the humidity offset value SP, the humidity ratio value SB, the temperature offset value WP, and the temperature ratio value WB are sent to the data analysis module;

[0015] The data analysis module is configured to obtain an environmental abnormality coefficient HY according to the environmental abnormality information, and send the environmental abnormality coefficient HY to the environmental warning platform;

[0016] The environmental warning platform is configured to generate an environmental abnormality instruction according to the environmental abnormality coefficient HY, and send the environmental abnormality instruction to the abnormality alarm module;

[0017] The abnormality alarm module is configured to ring an environmental abnormality ring tone after receiving the environmental warning instruction.

[0018] As a further scheme of the present disclosure, the specific process of the data analysis module obtaining the environmental abnormality coefficient HY is as follows:

[0019] The oxygen offset value OP, the oxygen ratio value OB, the humidity offset value SP, the humidity ratio value SB, the temperature offset value WP, and the temperature ratio value WB are quantitatively processed, the numerical values of the oxygen offset value OP, the oxygen ratio value OB, the humidity offset value SP, the humidity ratio value SB, the temperature offset value WP, and the temperature ratio value WB are extracted, and they are substituted into the formula to calculate, according to the formula Obtain the environmental anomaly coefficient HY, where p1, p2, and p3 are respectively the preset weighting factors corresponding to the set oxygen deviation OP, moisture deviation SP, and temperature deviation WP, and p1, p2, and p3 satisfy p1>p2>p3>2.365, and p1=3.67, p2=3.03, and p3=2.61 are taken; where b1, b2, and b3 are respectively the preset proportional coefficients corresponding to the set oxygen ratio OB, moisture ratio SB, and temperature ratio WB, and b1, b2, and b3 satisfy b1+b2+b3=1, 0<b3<b2<b1<1, and b1=0.51, b2=0.29, and b3=0.20;

[0020] The environmental anomaly coefficient HY is sent to the environmental early warning platform.

[0021] As an achievable solution of the present disclosure, the specific process of the environmental warning platform generating an environmental anomaly instruction is as follows:

[0022] Compare the environmental anomaly coefficient HY with the preset environmental anomaly threshold HYy:

[0023] If the environmental abnormality coefficient HY≥the environmental abnormality threshold HYy, an environmental abnormality instruction is generated and sent to the abnormality alarm module.

[0024] As an achievable solution of the present disclosure, the following is also included:

[0025] The status monitoring module is configured to monitor the status of the oxygen compression chamber after receiving the status monitoring instruction, obtain the abnormal status information of the oxygen compression chamber, and send the abnormal status information to the environmental early warning platform; wherein the abnormal status information includes the electrical difference value DC, the acoustic vibration value SZ and the time value YS.

[0026] As an achievable solution of the present disclosure, the specific process of the status monitoring module acquiring status abnormality information is as follows:

[0027] After receiving the status monitoring instruction, the oxygen compression chamber is monitored, the maximum voltage value and the minimum voltage value of the oxygen compression chamber per unit time are obtained, the difference between the two is obtained, and it is marked as the pressure difference value YC, the maximum current value and the minimum current value of the oxygen compression chamber per unit time are obtained, the difference between the two is obtained, and it is marked as the flow difference value LC, the pressure difference value YC and the flow difference value LC are quantified, the values ​​of the pressure difference value YC and the flow difference LC are extracted, and they are substituted into the formula for calculation. According to the formula Obtain the electrical difference DC, where c1 and c2 are the preset proportional coefficients corresponding to the set pressure difference YC and flow difference LC, respectively. c1 and c2 satisfy c1+c2=1, 0<c1<c2<1, and c1=0.41 and c2=0.59;

[0028] Obtain the maximum sound intensity per unit time in the oxygen compression chamber and mark it as the sound intensity value SQ. Obtain the total number of vibrations and the maximum vibration amplitude per unit time in the oxygen compression chamber and mark them as the vibration number value ZC and the amplitude value ZF respectively. Quantify the sound intensity value SQ, the vibration number value ZC and the amplitude value ZF, extract the values ​​of the sound intensity value SQ, the vibration number value ZC and the amplitude value ZF, and substitute them into the formula for calculation. According to the formula Obtain the acoustic vibration value SZ, where s1, s2, and s3 are the preset proportional coefficients corresponding to the set sound intensity value SQ, vibration frequency value ZC, and amplitude value ZF, respectively. s1, s2, and s3 satisfy s1+s2+s3=1, 0<s2<s3<s1<1, and s1=0.45, s2=0.24, and s3=0.31.

[0029] Obtain the moment when the user clicks the operation button on the computer and generates the relevant operation instruction, and mark it as the adjustment time value TS; obtain the moment when the oxygen compression chamber operates and runs according to the operation instruction, and mark it as the operation time value YS; obtain the difference between the adjustment time value TS and the operation time value YS, and mark it as the response time value YS;

[0030] The electrical difference value DC, acoustic vibration value SZ and time value YS are sent to the data analysis module.

[0031] As an achievable solution of the present disclosure: the data analysis module is further configured to obtain a state abnormality coefficient ZY according to the state abnormality information, and send the state abnormality coefficient ZY to the environmental early warning platform.

[0032] As an achievable solution of the present disclosure, the specific process of the data analysis module obtaining the state abnormality coefficient ZY is as follows:

[0033] The electrical difference DC, acoustic vibration value SZ and time-sensitive value YS are quantified, the values ​​of the electrical difference DC, acoustic vibration value SZ and time-sensitive value YS are extracted, and substituted into the formula for calculation. According to the formula Obtain the state abnormality coefficient ZY, where δ is the preset error adjustment factor, δ=1.102, e is a mathematical constant, z1, z2, and z3 are the preset weighting factors corresponding to the set electrical difference DC, acoustic vibration value SZ, and time value YS, respectively. z1, z2, and z3 satisfy z2>z1>z3>1.121, and z1=1.96, z2=2.55, and z3=1.41;

[0034] The state abnormality coefficient ZY is sent to the environmental early warning platform.

[0035] As an achievable solution of the present disclosure: the environmental early warning platform is further configured to generate a state abnormality instruction or an environmental monitoring instruction according to the state abnormality coefficient ZY, and send the state abnormality instruction to the abnormal alarm module and send the environmental monitoring instruction to the environmental monitoring module.

[0036] As an achievable solution of the present disclosure, the specific process of the environmental early warning platform generating a status abnormality instruction or an environmental monitoring instruction is as follows:

[0037] Compare the state abnormality coefficient ZY with the preset state abnormality threshold ZYy:

[0038] If the state abnormality coefficient ZY ≥ the state abnormality threshold ZYy, a state abnormality instruction is generated and sent to the abnormality alarm module;

[0039] If the state abnormality coefficient ZY is less than the state abnormality threshold ZYy, an environment monitoring instruction is generated and sent to the environment monitoring module.

[0040] As an achievable solution of the present disclosure: the environmental warning platform is further configured to generate a status monitoring instruction after starting the oxygen pressurization chamber, and send the status monitoring instruction to the status monitoring module; the abnormal alarm module is further configured to sound a status abnormality ringtone after receiving the abnormal alarm instruction.

[0041] Beneficial effects of the present disclosure:

[0042] The present invention discloses an intelligent early warning system for oxygen compression chamber environment based on data analysis, which first monitors the state of the oxygen compression chamber and obtains state abnormality information. The state abnormality coefficient obtained according to the state abnormality information can comprehensively measure the degree of state abnormality of the oxygen compression chamber, and the larger the state abnormality coefficient, the higher the degree of state abnormality. When the state abnormality coefficient is too high, an early warning is issued. When the state is normal, the environment of the oxygen compression chamber is monitored and environmental abnormality information is obtained. The environmental abnormality coefficient obtained according to the environmental abnormality information can comprehensively measure the degree of environmental abnormality of the oxygen compression chamber, and the larger the environmental abnormality coefficient, the higher the degree of environmental abnormality. When the environmental abnormality coefficient is too high, an early warning is issued. The system realizes all-round monitoring and early warning of the state and environment of the oxygen compression chamber by combining real-time monitoring and data analysis technology, improves the response speed and processing ability of medical personnel to abnormal situations, and provides strong protection for the treatment safety and comfort of patients. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] The present disclosure will be further described below with reference to the accompanying drawings.

[0044] FIG1 is a block diagram of a data analysis-based intelligent early warning system for an oxygen compression chamber environment in the present disclosure;

[0045] FIG2 is a process flow chart of an intelligent early warning system for oxygen compression chamber environment based on data analysis in the present disclosure. DETAILED DESCRIPTION

[0046] The following will be combined with the embodiments of the present disclosure to clearly and completely describe the technical solutions in the embodiments of the present disclosure. Obviously, the embodiments described are only part of the embodiments of the present disclosure, not all of the embodiments. Based on the embodiments of the present disclosure, all other embodiments obtained by ordinary technicians in this field without making any creative efforts shall fall within the scope of protection of the present disclosure.

[0047] Please refer to Figure 1 , this embodiment is an intelligent early warning system for oxygen compression chamber environment based on data analysis, which includes the following modules: an environmental early warning platform, a status monitoring module, a data analysis module, an abnormal alarm module, and an environmental monitoring module;

[0048] The environmental early warning platform is configured to generate a state monitoring instruction after starting the oxygen compression chamber, and send the state monitoring instruction to the state monitoring module; it is also configured to generate a state abnormality instruction or an environmental monitoring instruction according to the state abnormality coefficient ZY, and send the state abnormality instruction to the abnormal alarm module, and send the environmental monitoring instruction to the environmental monitoring module; it is also configured to generate an environmental abnormality instruction according to the environmental abnormality coefficient HY, and send the environmental abnormality instruction to the abnormal alarm module;

[0049] The state monitoring module is configured to monitor the state of the oxygen compression chamber after receiving the state monitoring instruction, obtain abnormal state information of the oxygen compression chamber, and send the abnormal state information to the environmental early warning platform; wherein the abnormal state information includes the electrical difference value DC, the acoustic vibration value SZ and the time value YS;

[0050] The data analysis module is configured to obtain a state abnormality coefficient ZY according to the state abnormality information, and send the state abnormality coefficient ZY to the environmental early warning platform; and is further configured to obtain an environmental abnormality coefficient HY according to the environmental abnormality information, and send the environmental abnormality coefficient HY to the environmental early warning platform;

[0051] The abnormal alarm module is configured to sound a state abnormality ring tone after receiving an abnormal alarm instruction; and is also configured to sound an environment abnormality ring tone after receiving an environment alarm instruction;

[0052] Among them, the environmental monitoring module is configured to perform environmental monitoring on the oxygen compression chamber after receiving the environmental monitoring instruction, obtain environmental abnormality information of the oxygen compression chamber, and send the environmental abnormality information to the data analysis module; wherein the environmental abnormality information includes oxygen deviation OP, oxygen ratio OB, humidity deviation SP, humidity ratio SB, temperature deviation WP and temperature ratio WB.

[0053] Referring to FIG. 2 , this embodiment is a method for operating an intelligent early warning system for an oxygen compression chamber environment based on data analysis, comprising the following steps:

[0054] Step 1: After starting the oxygen compression chamber, the environmental warning platform generates a status monitoring instruction and sends the status monitoring instruction to the status monitoring module;

[0055] Step 2: After receiving the status monitoring instruction, the status monitoring module monitors the status of the oxygen compression chamber, obtains abnormal status information of the oxygen compression chamber, wherein the abnormal status information includes the electrical difference value DC, the acoustic vibration value SZ and the time value YS, and sends the abnormal status information to the environmental early warning platform;

[0056] Step 3: The data analysis module obtains the state abnormality coefficient ZY according to the state abnormality information, and sends the state abnormality coefficient ZY to the environmental early warning platform;

[0057] Step 4: The environmental early warning platform generates a state abnormality instruction or an environmental monitoring instruction according to the state abnormality coefficient ZY, and sends the state abnormality instruction to the abnormal alarm module and sends the environmental monitoring instruction to the environmental monitoring module;

[0058] Step 5: After receiving the abnormal alarm instruction, the abnormal alarm module sounds an abnormal status ring tone;

[0059] Step 6: After receiving the environmental monitoring instruction, the environmental monitoring module monitors the oxygen compression chamber environment and obtains environmental abnormality information of the oxygen compression chamber, wherein the environmental abnormality information includes oxygen deviation value OP, oxygen ratio value OB, humidity deviation value SP, humidity ratio value SB, temperature deviation value WP and temperature ratio value WB, and sends the environmental abnormality information to the data analysis module;

[0060] Step 7: The data analysis module obtains the environmental anomaly coefficient HY based on the environmental anomaly information, and sends the environmental anomaly coefficient HY to the environmental early warning platform;

[0061] Step 8: The environmental early warning platform generates an environmental abnormality instruction according to the environmental abnormality coefficient HY, and sends the environmental abnormality instruction to the abnormal alarm module;

[0062] Step 9: After receiving the environmental alarm command, the abnormal alarm module sounds an environmental abnormality ring tone.

[0063] Based on any of the above embodiments, the embodiment of the present disclosure is an environmental early warning platform, which has three functions:

[0064] One of the functions is to generate status monitoring instructions. The specific process is as follows:

[0065] The oxygen compression chamber is started. After the oxygen compression chamber has been operating for a preset time, the environmental warning platform generates a status monitoring instruction and sends the status monitoring instruction to the status monitoring module;

[0066] The second function is to generate status abnormality instructions or environment monitoring instructions. The specific process is as follows:

[0067] The environmental early warning platform compares the state anomaly coefficient ZY with the preset state anomaly threshold ZYy:

[0068] If the state abnormality coefficient ZY ≥ the state abnormality threshold ZYy, a state abnormality instruction is generated and sent to the abnormality alarm module;

[0069] If the state abnormality coefficient ZY<state abnormality threshold ZYy, then generate an environment monitoring instruction and send the environment monitoring instruction to the environment monitoring module;

[0070] The third function is to generate an environmental exception instruction. The specific process is as follows:

[0071] The environmental early warning platform compares the environmental anomaly coefficient HY with the preset environmental anomaly threshold HYy:

[0072] If the environmental abnormality coefficient HY≥the environmental abnormality threshold HYy, an environmental abnormality instruction is generated and sent to the abnormality alarm module.

[0073] Based on any of the above embodiments, the embodiment of the present disclosure is a state monitoring module. The function of the state monitoring module is to obtain state abnormality information, wherein the state abnormality information includes the electrical difference value DC, the acoustic vibration value SZ, and the time value YS. The specific process is as follows:

[0074] After receiving the status monitoring instruction, the status monitoring module monitors the status of the oxygen compression chamber, obtains the maximum voltage value and the minimum voltage value of the oxygen compression chamber per unit time, obtains the difference between the two, and marks it as the pressure difference value YC, obtains the maximum current value and the minimum current value of the oxygen compression chamber per unit time, obtains the difference between the two, and marks it as the flow difference value LC, quantifies the pressure difference value YC and the flow difference value LC, extracts the values ​​of the pressure difference value YC and the flow difference LC, and substitutes them into the formula for calculation. According to the formula Obtain the electrical difference DC, where c1 and c2 are the preset proportional coefficients corresponding to the set pressure difference YC and flow difference LC, respectively. c1 and c2 satisfy c1+c2=1, 0<c1<c2<1, and c1=0.41 and c2=0.59;

[0075] The status monitoring module obtains the maximum sound intensity per unit time in the oxygen compression chamber and marks it as the sound intensity value SQ. It also obtains the total number of vibrations and the maximum vibration amplitude per unit time in the oxygen compression chamber and marks them as the vibration number value ZC and the amplitude value ZF respectively. The sound intensity value SQ, the vibration number value ZC and the amplitude value ZF are quantified, and the values ​​of the sound intensity value SQ, the vibration number value ZC and the amplitude value ZF are extracted and substituted into the formula for calculation. According to the formula Obtain the acoustic vibration value SZ, where s1, s2, and s3 are the preset proportional coefficients corresponding to the set sound intensity value SQ, vibration frequency value ZC, and amplitude value ZF, respectively. s1, s2, and s3 satisfy s1+s2+s3=1, 0<s2<s3<s1<1, and s1=0.45, s2=0.24, and s3=0.31.

[0076] The status monitoring module obtains the moment when the user clicks the operation button on the computer and generates the relevant operation instruction, and marks it as the adjustment time value TS, obtains the moment when the oxygen compression chamber operates and runs according to the operation instruction, and marks it as the operation time value YS, obtains the difference between the adjustment time value TS and the operation time value YS, and marks it as the response time value YS;

[0077] The status monitoring module sends the electrical difference value DC, the acoustic vibration value SZ and the stress value YS to the data analysis module.

[0078] Based on any of the above embodiments, the embodiment of the present disclosure is a data analysis module, which has two functions:

[0079] One of the functions is to obtain the state abnormality coefficient ZY. The specific process is as follows:

[0080] The data analysis module quantifies the electrical difference DC, acoustic vibration value SZ and time value YS, extracts the values ​​of the electrical difference DC, acoustic vibration value SZ and time value YS, and substitutes them into the formula for calculation. Obtain the state abnormality coefficient ZY, where δ is the preset error adjustment factor, δ=1.102, e is a mathematical constant, z1, z2, and z3 are the preset weighting factors corresponding to the set electrical difference DC, acoustic vibration value SZ, and time value YS, respectively. z1, z2, and z3 satisfy z2>z1>z3>1.121, and z1=1.96, z2=2.55, and z3=1.41;

[0081] The data analysis module sends the state abnormality coefficient ZY to the environmental early warning platform;

[0082] The second function is to obtain the environmental anomaly coefficient HY. The specific process is as follows:

[0083] The data analysis module quantifies the oxygen deviation OP, oxygen ratio OB, humidity deviation SP, humidity ratio SB, temperature deviation WP and temperature ratio WB, extracts the values ​​of oxygen deviation OP, oxygen ratio OB, humidity deviation SP, humidity ratio SB, temperature deviation WP and temperature ratio WB, and substitutes them into the formula for calculation. According to the formula Obtain the environmental anomaly coefficient HY, where p1, p2, and p3 are respectively the preset weighting factors corresponding to the set oxygen deviation OP, moisture deviation SP, and temperature deviation WP, and p1, p2, and p3 satisfy p1>p2>p3>2.365, and p1=3.67, p2=3.03, and p3=2.61 are taken; where b1, b2, and b3 are respectively the preset proportional coefficients corresponding to the set oxygen ratio OB, moisture ratio SB, and temperature ratio WB, and b1, b2, and b3 satisfy b1+b2+b3=1, 0<b3<b2<b1<1, and b1=0.51, b2=0.29, and b3=0.20;

[0084] The data analysis module sends the environmental anomaly coefficient HY to the environmental early warning platform.

[0085] Based on any of the above embodiments, the embodiment of the present disclosure is an environmental monitoring module. The function of the environmental monitoring module is to obtain environmental abnormality information, wherein the environmental abnormality information includes oxygen deviation OP, oxygen ratio OB, humidity deviation SP, humidity ratio SB, temperature deviation WP, and temperature ratio WB. The specific process is as follows:

[0086] After receiving the environmental monitoring instruction, the environmental monitoring module monitors the environment of the oxygen compression chamber, obtains the average oxygen concentration in the oxygen compression chamber cavity and marks it as the average concentration value JN, obtains the preset oxygen concentration of the oxygen compression chamber and marks it as the pre-concentration value YN, obtains the difference between the average concentration value JN and the pre-concentration value YN and marks it as the oxygen deviation value OP, and obtains the ratio between the average concentration value JN and the pre-concentration value YN and marks it as the oxygen ratio value OB;

[0087] The environmental monitoring module obtains the average humidity in the oxygen compression chamber cavity and marks it as the average humidity value JS, obtains the preset humidity of the oxygen compression chamber and marks it as the pre-humidification value YS, obtains the difference between the average humidity value JS and the pre-humidification value YS and marks it as the humidity deviation value SP, obtains the ratio between the average humidity value JS and the pre-humidification value YS and marks it as the humidity ratio value SB;

[0088] The environmental monitoring module obtains the average temperature in the oxygen compression chamber cavity and marks it as the average temperature value JW, obtains the preset temperature of the oxygen compression chamber and marks it as the pre-temperature value YW, obtains the difference between the average temperature value JW and the pre-temperature value YW and marks it as the temperature deviation value WP, obtains the ratio between the average temperature value JW and the pre-temperature value YW and marks it as the temperature ratio value WB;

[0089] The environmental monitoring module sends the oxygen deviation value OP, the oxygen ratio value OB, the humidity deviation value SP, the humidity ratio value SB, the temperature deviation value WP, and the temperature ratio value WB to the data analysis module.

[0090] Based on any of the above embodiments, the embodiment of the present disclosure is an abnormal alarm module. The function of the abnormal alarm module is to sound an abnormal ring tone. The specific process is as follows:

[0091] The abnormal alarm module sounds a status abnormality ring after receiving the abnormal alarm instruction; the abnormal alarm module sounds an environmental abnormality ring after receiving the environmental alarm instruction.

[0092] Based on the above embodiments, the working principle of the present disclosure is as follows:

[0093] The present invention discloses an intelligent early warning system for oxygen compression cabin environment based on data analysis, which generates a status monitoring instruction after the oxygen compression cabin is started through the environmental early warning platform, monitors the status of the oxygen compression cabin after receiving the status monitoring instruction through the status monitoring module, obtains abnormal status information of the oxygen compression cabin, obtains an abnormal status coefficient according to the abnormal status information through the data analysis module, generates an abnormal status instruction or an environmental monitoring instruction according to the abnormal status coefficient through the environmental early warning platform, sounds an abnormal status ring after receiving the abnormal alarm instruction through the abnormal alarm module, monitors the environment of the oxygen compression cabin after receiving the environmental monitoring instruction through the environmental monitoring module, obtains abnormal environment information of the oxygen compression cabin, obtains an abnormal environment coefficient according to the abnormal environment information through the data analysis module, generates an abnormal environment instruction according to the abnormal environment coefficient through the environmental early warning platform, and sounds an abnormal environment alarm after receiving the abnormal alarm instruction through the abnormal alarm module. Ringtone; The oxygen compression chamber environment intelligent early warning system first monitors the status of the oxygen compression chamber and obtains status abnormality information. The status abnormality coefficient obtained according to the status abnormality information can comprehensively measure the degree of status abnormality of the oxygen compression chamber, and the larger the status abnormality coefficient, the higher the degree of status abnormality. When the status abnormality coefficient is too high, an early warning is issued. When the status is normal, the oxygen compression chamber environment is monitored to obtain environmental abnormality information. The environmental abnormality coefficient obtained according to the environmental abnormality information can comprehensively measure the degree of environmental abnormality of the oxygen compression chamber, and the larger the environmental abnormality coefficient, the higher the degree of environmental abnormality. When the environmental abnormality coefficient is too high, an early warning is issued. The system realizes all-round monitoring and early warning of the status and environment of the oxygen compression chamber by combining real-time monitoring and data analysis technology, improves the response speed and processing ability of medical personnel to abnormal situations, and provides strong protection for the safety and comfort of patient treatment.

[0094] It should be further explained that before patients enter the oxygen compression chamber for treatment, they need to comply with and check that the following conditions of use are met:

[0095] 1. Patients must be examined and approved by a hyperbaric oxygen specialist, fill out a hyperbaric oxygen specialist treatment form, and register with a card before entering the cabin and comply with the treatment schedule;

[0096] 2. You should wash your hair and take a shower the day before entering the cabin. It is strictly forbidden to use hairspray and facial oil-based cosmetics;

[0097] 3. All passengers must change into specially made cotton clothes and bedding provided by the hospital. Private underwear, socks, bras, diapers and bedding are not allowed in the cabin.

[0098] 4. Wet your hair and stuff it all into the cotton hat;

[0099] 5. Empty your bowels and bladder before entering the cabin;

[0100] 6. It is strictly forbidden to bring in fire and other flammable and explosive items, such as matches, lighters, firecrackers, detonators, gasoline, hand warmers, electric or flashing toys, and baby bottles. It is strictly forbidden to bring in pens, watches, mobile phones, pagers, books, newspapers, and anything not related to treatment.

[0101] 7. Lie down as naturally and comfortably as possible in the cabin. Vigorous activities are strictly prohibited, especially do not move your head to prevent static electricity fire.

[0102] It should be further explained that the above formulas are obtained by collecting a large amount of data and performing software simulation, and a formula close to the actual value is selected. The coefficients in the formula are set by technical personnel in this field according to actual conditions.

[0103] Throughout this specification, references to terms such as "one embodiment," "example," or "specific example" indicate that a specific feature, structure, material, or characteristic described in conjunction with that embodiment or example is included in at least one embodiment or example of the present disclosure. In this specification, schematic representations of these terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.

[0104] The above contents are merely examples and explanations of the present disclosure. Those skilled in the art may make various modifications or additions to the described specific embodiments or replace them in similar ways. As long as they do not deviate from the disclosure or exceed the scope defined by the present disclosure, they should all fall within the scope of protection of the present disclosure. Industrial Applicability

[0105] By adopting the above solution, the oxygen compression chamber environment intelligent early warning system realizes all-round monitoring and early warning of the oxygen compression chamber status and environment through a combination of real-time monitoring and data analysis technology, improving the medical staff's response speed and handling ability to abnormal situations, and providing strong protection for the patient's treatment safety and comfort.

Claims

1. An intelligent early warning system for oxygen compression chamber environment based on data analysis, characterized in that: include: An environmental monitoring module is configured to monitor the environment of the oxygen compression chamber after receiving an environmental monitoring instruction, obtain environmental abnormality information of the oxygen compression chamber, and send the environmental abnormality information to the data analysis module; wherein the environmental abnormality information includes oxygen deviation value OP, oxygen ratio value OB, humidity deviation value SP, humidity ratio value SB, temperature deviation value WP, and temperature ratio value WB; The specific process of the environmental monitoring module obtaining environmental abnormality information is as follows: After receiving the environmental monitoring instruction, the oxygen compression chamber is monitored, the average oxygen concentration in the oxygen compression chamber is obtained and marked as the average concentration value JN, the preset oxygen concentration of the oxygen compression chamber is obtained and marked as the pre-concentration value YN, the difference between the average concentration value JN and the pre-concentration value YN is obtained and marked as the oxygen deviation value OP, and the ratio between the average concentration value JN and the pre-concentration value YN is obtained and marked as the oxygen ratio value OB; Obtain the average humidity in the oxygen compression chamber cavity and mark it as the average humidity value JS, obtain the preset humidity of the oxygen compression chamber and mark it as the pre-humidification value YS, obtain the difference between the average humidity value JS and the pre-humidification value YS and mark it as the humidity deviation value SP, obtain the ratio between the average humidity value JS and the pre-humidification value YS and mark it as the humidity ratio value SB; Obtain the average temperature in the oxygen compression chamber cavity and mark it as the average temperature value JW, obtain the preset temperature of the oxygen compression chamber and mark it as the pre-temperature value YW, obtain the difference between the average temperature value JW and the pre-temperature value YW and mark it as the temperature deviation value WP, obtain the ratio between the average temperature value JW and the pre-temperature value YW and mark it as the temperature ratio value WB; Send the oxygen deviation value OP, oxygen ratio value OB, moisture deviation value SP, moisture ratio value SB, temperature deviation value WP and temperature ratio value WB to the data analysis module; A data analysis module is configured to obtain an environmental anomaly coefficient HY according to the environmental anomaly information, and send the environmental anomaly coefficient HY to the environmental early warning platform; The environmental early warning platform is configured to generate an environmental abnormality instruction according to the environmental abnormality coefficient HY and send the environmental abnormality instruction to the abnormality alarm module; The abnormality alarm module is configured to ring an environmental abnormality ring tone after receiving an environmental alarm instruction.

2. The oxygen compression chamber environment intelligent early warning system based on data analysis according to claim 1 is characterized in that: The specific process of the data analysis module obtaining the environmental anomaly coefficient HY is as follows: The oxygen deviation OP, oxygen ratio OB, moisture deviation SP, moisture ratio SB, temperature deviation WP and temperature ratio WB are quantified according to the formula Obtain an environmental anomaly coefficient HY, where p1, p2, and p3 are respectively preset weight factors corresponding to the set oxygen deviation value OP, moisture deviation value SP, and temperature deviation value WP, and b1, b2, and b3 are respectively preset proportional coefficients corresponding to the set oxygen ratio value OB, moisture ratio value SB, and temperature ratio value WB; The environmental anomaly coefficient HY is sent to the environmental early warning platform.

3. The intelligent early warning system for oxygen compression chamber environment based on data analysis according to claim 1 or 2, characterized in that: The specific process of the environmental warning platform generating an environmental anomaly instruction is as follows: Compare the environmental anomaly coefficient HY with the preset environmental anomaly threshold HYy: If the environmental abnormality coefficient HY≥the environmental abnormality threshold HYy, an environmental abnormality instruction is generated and sent to the abnormality alarm module.

4. The intelligent early warning system for oxygen compression chamber environment based on data analysis according to any one of claims 1 to 3, characterized in that: Also includes: The status monitoring module is configured to monitor the status of the oxygen compression chamber after receiving the status monitoring instruction, obtain the abnormal status information of the oxygen compression chamber, and send the abnormal status information to the environmental early warning platform; wherein the abnormal status information includes the electrical difference value DC, the acoustic vibration value SZ and the time value YS.

5. The intelligent early warning system for oxygen compression chamber environment based on data analysis according to claim 4 is characterized in that: The specific process of the status monitoring module obtaining status abnormality information is as follows: After receiving the status monitoring instruction, the oxygen compression chamber is monitored, the maximum voltage value and the minimum voltage value of the oxygen compression chamber per unit time are obtained, the difference between the two is obtained, and it is marked as the pressure difference value YC, the maximum current value and the minimum current value of the oxygen compression chamber per unit time are obtained, the difference between the two is obtained, and it is marked as the flow difference value LC, the pressure difference value YC and the flow difference LC are quantified, and according to the formula Obtain the electrical difference DC, where c1 and c2 are the preset proportional coefficients corresponding to the set pressure difference YC and flow difference LC respectively; Obtain the maximum sound intensity per unit time in the oxygen compression chamber and mark it as the sound intensity value SQ. Obtain the total number of vibrations and the maximum vibration amplitude per unit time in the oxygen compression chamber and mark them as the vibration number value ZC and the amplitude value ZF respectively. Quantify the sound intensity value SQ, the vibration number value ZC and the amplitude value ZF according to the formula Obtain the sound vibration value SZ, where s1, s2, and s3 are the preset proportional coefficients corresponding to the set sound intensity value SQ, vibration frequency value ZC, and amplitude value ZF respectively; Obtain the moment when the user clicks the operation button on the computer and generates the relevant operation instruction, and mark it as the adjustment time value TS; obtain the moment when the oxygen compression chamber operates and runs according to the operation instruction, and mark it as the operation time value YS; obtain the difference between the adjustment time value TS and the operation time value YS, and mark it as the response time value YS; The electrical difference value DC, acoustic vibration value SZ and time value YS are sent to the data analysis module.

6. The oxygen compression chamber environment intelligent early warning system based on data analysis according to any one of claims 1 to 5, characterized in that: The data analysis module is further configured to obtain a state abnormality coefficient ZY according to the state abnormality information, and send the state abnormality coefficient ZY to the environmental early warning platform.

7. The intelligent early warning system for oxygen compression chamber environment based on data analysis according to claim 6, characterized in that: The specific process of the data analysis module obtaining the state abnormality coefficient ZY is as follows: The electrical difference DC, acoustic vibration value SZ and time value YS are quantified according to the formula Obtain the state abnormality coefficient ZY, where δ is the preset error adjustment factor, e is a mathematical constant, and z1, z2, and z3 are the preset weighting factors corresponding to the set electrical difference DC, acoustic vibration value SZ, and time value YS, respectively; The state abnormality coefficient ZY is sent to the environmental early warning platform.

8. The intelligent early warning system for oxygen compression chamber environment based on data analysis according to any one of claims 1 to 7, characterized in that: The environmental early warning platform is further configured to generate a state abnormality instruction or an environmental monitoring instruction according to the state abnormality coefficient ZY, and send the state abnormality instruction to the abnormal alarm module and send the environmental monitoring instruction to the environmental monitoring module.

9. The intelligent early warning system for oxygen compression chamber environment based on data analysis according to any one of claims 1 to 8, characterized in that: The specific process of the environmental early warning platform generating a status abnormality instruction or an environmental monitoring instruction is as follows: Compare the state abnormality coefficient ZY with the preset state abnormality threshold ZYy: If the state abnormality coefficient ZY ≥ the state abnormality threshold ZYy, a state abnormality instruction is generated and sent to the abnormality alarm module; If the state abnormality coefficient ZY is less than the state abnormality threshold ZYy, an environment monitoring instruction is generated and sent to the environment monitoring module.

10. The intelligent early warning system for oxygen compression chamber environment based on data analysis according to any one of claims 1 to 9, characterized in that: The environmental warning platform is further configured to generate a status monitoring instruction after starting the oxygen compression chamber, and send the status monitoring instruction to the status monitoring module; the abnormal alarm module is further configured to sound a status abnormality ringtone after receiving the abnormal alarm instruction.

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