Medical clean workshop central air conditioner fault early warning method based on multi-source data

Abstract

The invention relates to the technical field of air conditioner fault early warning, and discloses a medical clean workshop central air conditioner fault early warning method based on multi-source data. Comprising the following steps that S1, the medicine clean workshop is divided into a plurality of clean area units; s2, acquiring environment information data of each clean area unit; s3, analyzing the environment information data of each clean area unit to obtain an environment risk index of each clean area unit; s4, according to the environment risk index analysis of each clean area unit, the current target working parameters of the air conditioner unit are dynamically adjusted; s5, response information data of the current target working parameters of the air conditioner units are obtained and analyzed, the current target working parameters are analyzed, whether response abnormity happens to all the air conditioner units or not is judged, and early warning information is sent to the management terminal; therefore, managers can find abnormal conditions of the air-conditioning system in time, environmental deterioration of a clean area caused by abnormal response of the air-conditioning system is avoided, and continuity and stability of medicine production are guaranteed.

Description

Technical Field

[0001] This invention relates to the field of air conditioning fault early warning technology, specifically to a method for early warning of central air conditioning faults in pharmaceutical cleanrooms based on multi-source data. Background Technology

[0002] In the pharmaceutical manufacturing industry, the environmental conditions of cleanrooms play a crucial role in drug quality. As the core equipment for maintaining the stability of the cleanroom environment, the central air conditioning system directly affects key environmental parameters such as temperature, humidity, and cleanliness within the facility. If the central air conditioning system malfunctions, it may lead to an uncontrolled environment within the facility, thereby affecting the normal production of drugs and even causing substandard drug quality, resulting in huge economic losses for the company and posing a potential threat to patients' medication safety.

[0003] Traditional methods for detecting faults in central air conditioning systems in pharmaceutical cleanrooms primarily rely on continuous monitoring of key operating parameters of the air conditioning equipment. Specifically, various sensors are typically used to collect real-time data on temperature changes, current fluctuations, and power consumption of the air conditioning equipment (such as compressors). The collected data is then compared with the parameter ranges during normal operation of the equipment, or specific algorithms are used to analyze the trends in the data to determine whether the air conditioning system has malfunctioned.

[0004] However, pharmaceutical cleanrooms are typically divided into different clean areas based on production processes and drug quality requirements, and each area may have different environmental parameter requirements. Traditional methods use uniform standards and approaches to monitor and control air conditioning systems, which cannot provide precise adjustments based on the characteristics and needs of different areas. This could lead to localized environmental malfunctions and affect drug quality. Summary of the Invention

[0005] The purpose of this invention is to provide a method for early warning of central air conditioning failures in pharmaceutical cleanrooms based on multi-source data, thereby solving the following technical problems:

[0006] How to optimize the early warning method for central air conditioning failures in pharmaceutical cleanrooms.

[0007] The objective of this invention can be achieved through the following technical solutions:

[0008] A fault early warning method for central air conditioning systems in pharmaceutical cleanrooms based on multi-source data, the method comprising the following steps:

[0009] S1: Divide the pharmaceutical cleanroom into several clean area units according to the zoning rules;

[0010] S2: An environmental monitoring module is set up in each clean area unit. The environmental monitoring module is used to acquire environmental information data of the corresponding clean area unit.

[0011] S3: By analyzing the environmental information data of each clean area unit over a preset time period in the past, the environmental risk index of each clean area unit is obtained;

[0012] S4: Analyze the environmental risk index of each clean area unit and dynamically adjust the current target operating parameters of the air conditioning units in each clean area unit.

[0013] S5: By setting up an air conditioning unit monitoring module on each air conditioning unit, the response information data of the current target operating parameters of the air conditioning unit is obtained; based on the response information data of the current target operating parameters and the current target operating parameters, it is determined whether each air conditioning unit has generated an abnormal response, and an early warning information is sent to the management terminal.

[0014] As a further aspect of the present invention: the environmental monitoring module includes several environmental monitoring groups; each environmental monitoring group is located at a different preset horizontal height in the clean area unit; the environmental monitoring group includes several environmental acquisition units, which are evenly arranged in the clean area unit according to preset horizontal heights and preset intervals.

[0015] As a further aspect of the present invention, the environmental information data includes ambient temperature, ambient humidity, and the concentration of each type of pollutant.

[0016] As a further aspect of the present invention: In step S3, the process of obtaining the environmental risk index of each clean area unit is as follows:

[0017] S31: Obtain the target cleanliness level for each clean area unit;

[0018] S32: Analyze the ambient temperature collected by each environmental acquisition unit in each clean area unit within a preset time period past the current time, and obtain the temperature risk index of the location of each environmental acquisition unit in each clean area unit.

[0019] S33: Analyze the ambient humidity collected by each environmental acquisition unit in each clean area unit within a preset time period past the current time, and obtain the humidity risk index of the location of each environmental acquisition unit in each clean area unit.

[0020] S34: Analyze the types of pollutants collected by each environmental collection unit in each clean area unit within a preset time period in the past, and obtain the pollution risk index of the location of each environmental collection unit in each clean area unit.

[0021] S35: Based on a comprehensive analysis of the temperature risk index, humidity risk index, and pollution risk index of each environmental acquisition unit in each clean area unit, the environmental risk index of each clean area unit is obtained.

[0022] As a further aspect of the present invention: In step S32, by formula one:

[0023]

[0024] Calculate the temperature risk index T at the location of the nth environmental acquisition unit in the mth clean area unit. mn ;

[0025] Where f(X) is the first judgment function, f(X) = X when X > 0; f(X) = 0 when X ≤ 0; M is the total number of clean area units, m ∈ M; t n The current time is Δt; the preset duration is Δt; N is the total number of environmental data acquisition units, n∈N; T mns (t) represents the temperature change curve over time at the location of the nth environmental acquisition unit in the mth clean area unit; ρ is the preset ambient temperature; mT A preset adjustment coefficient for the temperature error of the target cleanliness level of the m-th clean area unit; W T This is the preset temperature tolerance value.

[0026] As a further aspect of the present invention: In step S33, formula two is used:

[0027]

[0028] Calculate the humidity risk index S at the location of the nth environmental acquisition unit in the mth clean area unit. mn ;

[0029] Among them, S mns (t) represents the humidity change curve over time at the location of the nth environmental acquisition unit in the mth clean area unit; S0 represents the preset environmental humidity; ρ mS A preset adjustment coefficient for humidity error of the target cleanliness level of the m-th clean area unit; W T This is the preset humidity tolerance value.

[0030] As a further aspect of the present invention: In step S34, formula three is used:

[0031]

[0032] Calculate the pollution risk index R of the location of the nth environmental acquisition unit in the mth clean area unit. mn ;

[0033] Where K is the total number of pollutant types, k∈K; R mnks(t) represents the curve of the real-time value of the k-th pollutant type at the location of the nth environmental acquisition unit in the m-th clean area unit over time; ρ mk R is the preset adjustment coefficient for the k-th contaminant type of the target cleanliness level of the m-th clean area unit; k0 This is the preset concentration value for the kth pollutant type.

[0034] As a further aspect of the present invention: In step S34, formula four is used:

[0035]

[0036] Calculate the environmental risk index U of the m-th clean area unit. m ;

[0037] Wherein, γ1 is the first weighting coefficient; γ2 is the second weighting coefficient; γ3 is the third weighting coefficient; C1 is the first preset constant; C2 is the second preset constant; and C3 is the third preset constant.

[0038] As a further aspect of the present invention: through formula five:

[0039]

[0040] Calculate the target air volume F per unit time for the air conditioning unit corresponding to the m-th clean area unit. m0 ;

[0041] Where F0 is the preset air volume; C0 is the preset air volume constant; This is to round X up.

[0042] As a further aspect of the present invention: the process for determining whether the air conditioning unit of the m-th clean area unit is abnormal is as follows:

[0043] First, let's use formula six:

[0044]

[0045] Calculate the response exponent X of the current target working parameters. t ;

[0046] Among them, t s F represents the current target operating parameter response adjustment time of the air conditioning unit in the m-th clean area unit; ms (t) represents the curve showing the change in the actual air output of the air conditioning unit of the m-th clean area unit over time; t ms+1 ρ represents the time after the current time when the target airflow of the air conditioning unit corresponding to the m-th clean area unit changes per unit time; t ρ is the first weighting coefficient; F This is the second weighting coefficient; Ct C is the first preset constant; F This is the second preset constant;

[0047] The response index X of the current target working parameter t Compare with preset thresholds [D1, D2];

[0048] When 0≤X t When <D1, the air conditioning unit of the m-th clean area unit responds normally;

[0049] When D1≤X t When <D2, the air conditioning unit of the m-th clean area unit responds with a slight abnormality;

[0050] When D2≤X t At that time, the air conditioning unit of the m-th clean area unit responded with a serious anomaly.

[0051] The beneficial effects of this invention are:

[0052] (1) This invention first divides the pharmaceutical cleanroom into several clean area units according to the area division rules; it can carry out independent and detailed environmental monitoring and management for areas with different functions and different cleanliness requirements; this division method avoids the situation where local environmental problems may be overlooked in traditional overall monitoring; then, environmental monitoring modules are set up in each clean area unit to obtain the environmental information data of the corresponding clean area unit; then, by analyzing the environmental information data of each clean area unit over a preset time period, the environmental risk index of each clean area unit is obtained; then, based on the analysis of the environmental risk index of each clean area unit, the current target working parameters of the air conditioning units in each clean area unit are dynamically adjusted; finally, by setting up an air conditioning unit monitoring module on each air conditioning unit and obtaining the response information data of the current target working parameters of the air conditioning unit; based on the analysis of the response information data and the current target working parameters, it is determined whether each air conditioning unit has generated an abnormal response and an early warning message is sent to the management terminal; this enables the management personnel to detect the abnormal situation of the air conditioning system in a timely manner, avoid the deterioration of the clean area environment due to the abnormal response of the air conditioning system, and ensure the continuity and stability of pharmaceutical production.

[0053] (2) By setting up environmental monitoring groups at different preset horizontal heights, this invention can acquire environmental information data from multiple height levels. Compared with setting up environmental monitoring units only at the return air vent, it can more comprehensively and accurately reflect the environmental status of the entire clean area unit. On this basis, this embodiment can provide an accurate basis for adjusting the working parameters of the air conditioning unit, so that the air conditioning unit operates according to more reasonable current target working parameters. On this basis, by acquiring the response information data of the current target working parameters of the air conditioning unit and comparing and analyzing it with the current target working parameters, it can more accurately determine whether the air conditioning unit is operating normally according to the preset requirements and obtain a more reliable response level. Then, based on the response level, it can be determined whether each air conditioning unit has generated a response abnormality. Attached Figure Description

[0054] The invention will now be further described with reference to the accompanying drawings.

[0055] Figure 1 This is a flowchart of a method according to an embodiment of the present invention. Detailed Implementation

[0056] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0057] Please see Figure 1 As shown, in one embodiment, a method for early warning of central air conditioning failure in a pharmaceutical cleanroom based on multi-source data is provided. The method includes the following steps:

[0058] S1: Divide the pharmaceutical cleanroom into several clean area units according to the zoning rules;

[0059] Specifically, the area division rules can be based on different target cleanliness levels;

[0060] S2: An environmental monitoring module is set up in each clean area unit. The environmental monitoring module is used to acquire environmental information data of the corresponding clean area unit.

[0061] Specifically, environmental information data includes ambient temperature, ambient humidity, and the concentration of various pollutant types; pollutant types include particulate matter, chemical pollutants, and microorganisms, etc.

[0062] S3: By analyzing the environmental information data of each clean area unit over a preset time period in the past, the environmental risk index of each clean area unit is obtained;

[0063] Specifically, the environmental risk index is used to reflect the degree of environmental impact on the spread of pollutants in each clean area unit;

[0064] S4: Analyze the environmental risk index of each clean area unit and dynamically adjust the current target operating parameters of the air conditioning units in each clean area unit.

[0065] Specifically, the current target operating parameters include the target air volume per unit time;

[0066] S5: By setting up an air conditioning unit monitoring module on each air conditioning unit, the response information data of the current target operating parameters of the air conditioning unit is obtained; based on the response information data of the current target operating parameters and the current target operating parameters, it is determined whether each air conditioning unit has generated an abnormal response, and an early warning information is sent to the management terminal.

[0067] Specifically, the response information data includes the current target operating parameter response adjustment time and the actual operating parameter value;

[0068] Through the above technical solution, this embodiment first divides the pharmaceutical cleanroom into several clean area units according to the zoning rules; it enables independent and detailed environmental monitoring and management for areas with different functions and cleanliness requirements; this division method avoids the situation where local environmental problems may be overlooked in traditional overall monitoring; then, environmental monitoring modules are set up in each clean area unit to obtain the environmental information data of the corresponding clean area unit; then, by analyzing the environmental information data of each clean area unit over a preset time period, the environmental risk index of each clean area unit is obtained; then, based on the analysis of the environmental risk index of each clean area unit, the current target operating parameters of the air conditioning units in each clean area unit are dynamically adjusted; finally, by setting up an air conditioning unit monitoring module on each air conditioning unit, the response information data of the current target operating parameters of the air conditioning unit is obtained; based on the analysis of the response information data and the current target operating parameters, it is determined whether each air conditioning unit has generated an abnormal response, and an early warning information is sent to the management terminal; this enables managers to promptly detect abnormalities in the air conditioning system, avoid environmental deterioration in the clean area due to abnormal response of the air conditioning system, and ensure the continuity and stability of pharmaceutical production.

[0069] In one embodiment of the present invention, the environmental monitoring module includes several environmental monitoring groups; each environmental monitoring group is located at a different preset horizontal height in the clean area unit; each environmental monitoring group includes several environmental acquisition units, which are evenly arranged in the clean area unit according to preset horizontal height and preset interval distance;

[0070] Because environmental conditions can vary at different altitudes—for example, in pharmaceutical cleanrooms, areas near the ground may generate more particulate matter due to personnel activity and material handling; while higher locations may experience different temperature and humidity distributions due to ventilation systems, and the diffusion of chemical pollutants and microorganisms also changes with altitude—this embodiment, by setting up environmental monitoring groups at different preset altitudes, can acquire environmental information data from multiple height levels. Compared to setting up environmental monitoring units only at the return air vents, this embodiment can more comprehensively and accurately reflect the environmental conditions of the entire cleanroom unit. Based on this, this embodiment can provide accurate data for adjusting the operating parameters of the air conditioning units, enabling them to operate according to more reasonable current target operating parameters. Furthermore, by acquiring response information data of the current target operating parameters of the air conditioning units and comparing it with the current target operating parameters, it can more accurately determine whether the air conditioning units are operating normally according to preset requirements, obtaining a more reliable response level. Then, based on the response level, it can determine whether each air conditioning unit has generated an abnormal response and send early warning information to the management terminal, enabling managers to promptly detect abnormalities in the air conditioning system, preventing environmental deterioration in the cleanroom due to abnormal air conditioning system responses, and effectively ensuring the continuity and stability of pharmaceutical production.

[0071] In one embodiment of the present invention, the process of obtaining the environmental risk index of each clean area unit in step S3 is as follows:

[0072] S31: Obtain the target cleanliness level for each clean area unit;

[0073] Specifically, the target cleanliness level of each clean area unit is a preset value, which is set according to experience based on the function of the area.

[0074] S32: Analyze the ambient temperature collected by each environmental acquisition unit in each clean area unit within a preset time period past the current time, and obtain the temperature risk index of the location of each environmental acquisition unit in each clean area unit.

[0075] Specifically, the temperature risk index is used to reflect the degree of influence of the temperature at the location of each environmental sampling unit in each clean area unit on the spread of pollutants;

[0076] S33: Analyze the ambient humidity collected by each environmental acquisition unit in each clean area unit within a preset time period past the current time, and obtain the humidity risk index of the location of each environmental acquisition unit in each clean area unit.

[0077] Specifically, the humidity risk index is used to reflect the degree of influence of humidity at the location of each environmental sampling unit in each clean area unit on the spread of pollutants.

[0078] S34: Analyze the types of pollutants collected by each environmental collection unit in each clean area unit within a preset time period in the past, and obtain the pollution risk index of the location of each environmental collection unit in each clean area unit.

[0079] Specifically, the pollution risk index is used to reflect the degree of pollution exceeding the standard at the location of each environmental sampling unit in each clean area unit;

[0080] S35: Based on the temperature risk index, humidity risk index, and pollution risk index of each environmental acquisition unit in each clean area unit, a comprehensive analysis is conducted to obtain the environmental risk index of each clean area unit; specifically, the environmental risk index is used to reflect the overall pollution risk level within each clean area unit.

[0081] Through the above technical solution, this embodiment first obtains the target cleanliness level of each clean area unit, and then correlates the subsequently calculated environmental risk index with the target cleanliness level, making the risk assessment more targeted and purposeful. It clarifies the risk status faced by different functional areas in meeting their specific cleanliness requirements, thus providing a basis for taking targeted measures to ensure cleanliness and ensuring that the environmental quality of each clean area unit meets the standards and requirements of pharmaceutical production. When calculating the environmental risk index, the impact of three key environmental factors—temperature, humidity, and pollutant concentration—on pollutant propagation is considered. Temperature, humidity, and pollutant concentration are interrelated in pharmaceutical cleanrooms and have a significant impact on environmental quality. For example, excessively high or low temperatures and humidity may promote the growth and reproduction of microorganisms and accelerate the spread of pollutants. By calculating the temperature risk index, humidity risk index, and pollution risk index separately, and then comprehensively deriving the environmental risk index, the overall pollution risk level within each clean area unit can be more accurately reflected, avoiding risk assessment bias caused by considering a single factor, thereby providing strong support for ensuring that the clean area reaches the target cleanliness level.

[0082] As one embodiment of the present invention, in step S32, formula one is used:

[0083]

[0084] Calculate the temperature risk index T at the location of the nth environmental acquisition unit in the mth clean area unit. mn ;

[0085] Where f(X) is the first judgment function, f(X) = X when X > 0; f(X) = 0 when X ≤ 0; M is the total number of clean area units, m ∈ M; t n The current time is Δt; the preset duration is Δt; N is the total number of environmental data acquisition units, n∈N; T mns(t) represents the temperature change curve over time at the location of the nth environmental acquisition unit in the mth clean area unit; ρ is the preset ambient temperature; mT A preset adjustment coefficient for the temperature error of the target cleanliness level of the m-th clean area unit; W T This is the preset temperature tolerance value;

[0086] Formula 1 Explanation: ρ mT W T The allowable temperature error value for the target cleanliness level of the m-th clean area unit; This is the cumulative value of the difference between the actual temperature of the m-th clean area unit and the preset ambient temperature within a preset time period, which exceeds the allowable temperature error value of the target cleanliness level of the m-th clean area unit. It is used to reflect the degree of beneficial influence of the actual temperature of the m-th clean area unit on the reproduction and spread of pollutants. The larger the value, the greater the favorable influence of the actual temperature of the m-th clean area unit and the n-th environmental acquisition unit on the reproduction and spread of pollutants, and therefore the greater the pollution risk. The greater the temperature risk index of the location of the m-th clean area unit and the n-th environmental acquisition unit.

[0087] Through the above technical solution, this embodiment considers the temperature change curve over time to comprehensively reflect the dynamic impact; by calculating the cumulative value of the difference between the actual temperature and the preset temperature exceeding the allowable error value, it clarifies the degree to which the actual temperature is favorable for the reproduction and spread of pollutants; at the same time, it introduces a preset adjustment coefficient for the temperature error of the target cleanliness level to meet the needs of different areas; Formula 1 enables the temperature risk index to accurately reflect the impact of temperature at each location on the spread of pollution, providing a reliable basis for subsequent comprehensive assessment of environmental risks, dynamic adjustment of air conditioning parameters, and accurate fault early warning, thus ensuring the pharmaceutical production environment;

[0088] It should be noted that the preset duration Δt, preset ambient temperature T0, preset temperature error adjustment coefficients for each target cleanliness level, and preset temperature allowable error value W are all specified. T These are preset values, set based on empirical fitting, and will not be detailed here.

[0089] As one embodiment of the present invention, in step S33, formula two is used:

[0090]

[0091] Calculate the humidity risk index S at the location of the nth environmental acquisition unit in the mth clean area unit. mn ;

[0092] Among them, S mns(t) represents the humidity change curve over time at the location of the nth environmental acquisition unit in the mth clean area unit; S0 represents the preset environmental humidity; ρ mS A preset adjustment coefficient for humidity error of the target cleanliness level of the m-th clean area unit; W T This is the preset humidity tolerance value;

[0093] Formula 2 Explanation: ρ mS S T The allowable humidity error value for the target cleanliness level of the m-th clean area unit; The value is the cumulative value of the difference between the actual humidity of the m-th clean area unit and the preset environmental humidity within a preset time period, which exceeds the allowable error value of humidity for the target cleanliness level of the m-th clean area unit. It is used to reflect the degree of beneficial influence of the actual humidity of the m-th clean area unit on the reproduction and spread of pollutants. The larger the value, the greater the beneficial influence of the actual humidity of the m-th clean area unit and the n-th environmental collection unit on the reproduction and spread of pollutants, and therefore the greater the pollution risk. The greater the humidity risk index of the location of the m-th clean area unit and the n-th environmental collection unit.

[0094] Through the above technical solutions, this embodiment considers the humidity change curve over time to comprehensively reflect the dynamic impact; by calculating the cumulative value of the difference between the actual humidity and the preset humidity exceeding the allowable error value, it clarifies the degree to which the actual humidity is favorable for the reproduction and spread of pollutants; at the same time, it introduces a preset adjustment coefficient for the humidity error of the target cleanliness level to meet the needs of different areas; Formula 2 enables the humidity risk index to accurately reflect the impact of humidity at each location on the spread of pollution, providing a reliable basis for subsequent comprehensive assessment of environmental risks, dynamic adjustment of air conditioning parameters, and accurate fault early warning, thus ensuring the pharmaceutical production environment;

[0095] It should be noted that the preset ambient humidity S0, the preset adjustment coefficient for humidity error of each target cleanliness level, and the preset humidity allowable error value W are all specified. T These are preset values, set based on empirical fitting, and will not be detailed here.

[0096] As one embodiment of the present invention, in step S34, formula three is used:

[0097]

[0098] Calculate the pollution risk index R of the location of the nth environmental acquisition unit in the mth clean area unit. mn ;

[0099] Where K is the total number of pollutant types, k∈K; R mnks(t) represents the curve of the real-time value of the k-th pollutant type at the location of the nth environmental acquisition unit in the m-th clean area unit over time; ρ mk R is the preset adjustment coefficient for the k-th contaminant type of the target cleanliness level of the m-th clean area unit; k0 This is the preset concentration value for the k-th pollutant type;

[0100] Formula 3 Explanation: ρ mk R k0 The permissible error value for the kth contaminant type of the target cleanliness level of the m-th clean area unit; This is the cumulative value of the real-time value of each pollutant type at the current location within a preset time period, exceeding the preset concentration value of each pollutant type. It is used to reflect the degree of pollutant exceedance in the nth environmental collection unit of the mth clean area unit. The larger the value, the greater the degree of pollutant exceedance in the nth environmental sampling unit of the mth clean area unit. The pollution risk index R of the location of the nth environmental sampling unit in the mth clean area unit is... mn The larger;

[0101] Through the above technical solution, this embodiment considers the time-varying curves of each pollutant type, and calculates the cumulative value of each pollutant type's real-time value exceeding the preset concentration value of each pollutant type to clarify the degree of pollution exceeding the standard; Formula 3 enables the pollution risk index to accurately reflect the degree of pollutant exceeding the standard at each location, providing a reliable basis for subsequent comprehensive assessment of environmental risks, dynamic adjustment of air conditioning parameters and accurate fault early warning, and ensuring the pharmaceutical production environment;

[0102] It should be noted that the preset adjustment coefficients and preset concentration values ​​for each pollutant type at each target cleanliness level are preset values, set based on empirical fitting, and will not be detailed here.

[0103] As one embodiment of the present invention, in step S34, formula four is used:

[0104]

[0105] Calculate the environmental risk index U of the m-th clean area unit. m ;

[0106] Wherein, γ1 is the first weighting coefficient; γ2 is the second weighting coefficient; γ3 is the third weighting coefficient; C1 is the first preset constant; C2 is the second preset constant; and C3 is the third preset constant.

[0107] Through the above technical solution, this embodiment uses Formula 4 to comprehensively reflect the environmental risk of the m-th clean area unit based on three aspects: the temperature risk index, humidity risk index, and pollutant risk index of the location of each environmental collection unit in each clean area unit. Formula 4 provides a key basis for subsequent comprehensive assessment of environmental risk, which is conducive to dynamically adjusting air conditioning parameters, accurately predicting faults, effectively ensuring that the pharmaceutical production environment is stable and meets standards, avoiding bias in considering a single factor, and enhancing the scientificity and reliability of risk assessment.

[0108] It should be noted that the first weighting coefficient γ1, the second weighting coefficient γ2, the third weighting coefficient γ3, the first preset constant C1, the second preset constant C2, and the third preset constant C3 are preset values, set based on empirical fitting, and will not be described in detail here.

[0109] In one embodiment of the present invention, the current target operating parameter includes the target air volume per unit time, as defined by Formula 5:

[0110]

[0111] Calculate the target air volume F per unit time for the air conditioning unit corresponding to the m-th clean area unit. m0 ;

[0112] Where F0 is the preset air volume; C0 is the preset air volume constant; To round x up;

[0113] Using the above technical solution, the environmental risk index U of the m-th clean area unit is... m The larger the value, the higher the risk of pollutant breeding and spread in the area; this can be achieved by increasing the target air volume F per unit time of the air conditioning unit. m0 It can accelerate airflow, more effectively dilute and remove pollutants, reduce their concentration in the area, and better regulate the temperature and humidity of the area, keeping them within a suitable range, thereby inhibiting the spread of pollutants and ensuring a clean environment.

[0114] It should be noted that the preset air volume F0 and the preset air constant C0 are preset values, set based on empirical fitting, and will not be described in detail here.

[0115] As one embodiment of the present invention, the process for determining whether the air conditioning unit of the m-th clean area unit is abnormal is as follows:

[0116] First, let's use formula six:

[0117]

[0118] Calculate the response exponent X of the current target working parameters. t ;

[0119] Among them, t s F represents the current target operating parameter response adjustment time of the air conditioning unit in the m-th clean area unit; ms (t) represents the curve showing the change in the actual air output of the air conditioning unit of the m-th clean area unit over time; t ms+1 ρ represents the time after the current time when the target airflow of the air conditioning unit corresponding to the m-th clean area unit changes per unit time; t ρ is the first weighting coefficient; F This is the second weighting coefficient; C t C is the first preset constant; F This is the second preset constant;

[0120] The response index X of the current target working parameter t Compare with preset thresholds [D1, D2];

[0121] When 0≤X t When <D1, the air conditioning unit of the m-th clean area unit responds normally;

[0122] When D1≤X t When <D2, the air conditioning unit of the m-th clean area unit responds with a slight abnormality, and a slight abnormality warning is issued;

[0123] When D2≤X t When the m-th clean area unit's air conditioning unit responds to a severe anomaly, a severe anomaly warning is issued;

[0124] Formula 6 Explanation: t ms -t n Let X be the time difference between the current target operating parameter response adjustment time of the m-th clean area unit's air conditioning unit and the current time. The larger the time difference, the slower the response of the m-th clean area unit's air conditioning unit, indicating that the air conditioning unit may have a response fault; the response index X of the current target operating parameter. t The larger; The average supply air volume between the current target operating parameter response adjustment time of the m-th clean area unit's air conditioning unit and the time when the target air volume per unit time of the m-th clean area unit's corresponding air conditioning unit changes after the current time, and the target air volume per unit time F of the m-th clean area unit's corresponding air conditioning unit at the current time. m0 The absolute value of the difference reflects the air volume response deviation of the air conditioning unit corresponding to the m-th clean area unit; The larger the value, the greater the deviation in the airflow response of the air conditioning unit corresponding to the m-th clean area unit, indicating a possible airflow malfunction in the air conditioning unit; the response index X of the current target operating parameter. t The larger;

[0125] Through the above technical solution, this embodiment calculates the response index by comprehensively considering factors such as response adjustment time and air volume response deviation, which can comprehensively and accurately evaluate the performance of the air conditioning unit, provide a strong basis for fault diagnosis and maintenance, effectively improve the stability and reliability of the air conditioning unit operation, and ensure the environmental quality of the clean area; this solution can also detect abnormal response of the air conditioning unit in a timely manner, whether it is a minor abnormality or a serious abnormality, and can quickly issue an early warning, so as to facilitate timely handling by staff and avoid the fault from expanding and affecting the clean area environment;

[0126] It should be noted that the current target operating parameter response adjustment time t of each clean area unit's air conditioning unit is... s The method of acquisition is based on existing technology and will not be detailed here;

[0127] It should be noted that the first weighting coefficient ρ t Weighting coefficient ρ (number two) F Preset constant C t Preset constant C (number 2) F The preset thresholds [D1, D2] are preset values, set based on empirical fitting, and will not be described in detail here.

[0128] The foregoing has provided a detailed description of one embodiment of the present invention, but this description is merely a preferred embodiment and should not be construed as limiting the scope of the invention. All equivalent variations and modifications made within the scope of the claims of this invention should still fall within the patent coverage of this invention.

Claims

1. A method for early warning of central air conditioning system failures in pharmaceutical cleanrooms based on multi-source data, characterized in that, The early warning method includes the following steps: S1: Divide the pharmaceutical cleanroom into several clean area units according to the zoning rules; S2: An environmental monitoring module is set up in each clean area unit. The environmental monitoring module is used to acquire environmental information data of the corresponding clean area unit. S3: By analyzing the environmental information data of each clean area unit over a preset time period in the past, the environmental risk index of each clean area unit is obtained; S4: Analyze the environmental risk index of each clean area unit and dynamically adjust the current target operating parameters of the air conditioning units in each clean area unit. S5: By setting up an air conditioning unit monitoring module on each air conditioning unit, the response information data of the current target operating parameters of the air conditioning unit is obtained; based on the response information data of the current target operating parameters and the current target operating parameters, it is determined whether each air conditioning unit has generated an abnormal response, and an early warning information is sent to the management terminal.

2. The method for early warning of central air conditioning failure in pharmaceutical cleanrooms based on multi-source data as described in claim 1, characterized in that, The environmental monitoring module includes several environmental monitoring groups; each environmental monitoring group is located at a different preset horizontal height in the clean area unit; each environmental monitoring group includes several environmental acquisition units, which are evenly arranged in the clean area unit according to preset horizontal heights and preset intervals.

3. The method for early warning of central air conditioning failure in pharmaceutical cleanrooms based on multi-source data according to claim 2, characterized in that, Environmental information data includes ambient temperature, ambient humidity, and the concentration of various pollutant types.

4. The method for early warning of central air conditioning failure in pharmaceutical cleanrooms based on multi-source data according to claim 3, characterized in that, In step S3, the process of obtaining the environmental risk index of each clean area unit is as follows: S31: Obtain the target cleanliness level 1 for each clean area unit; S32: Analyze the ambient temperature collected by each environmental acquisition unit in each clean area unit within a preset time period past the current time, and obtain the temperature risk index of the location of each environmental acquisition unit in each clean area unit. S33: Analyze the ambient humidity collected by each environmental acquisition unit in each clean area unit within a preset time period past the current time, and obtain the humidity risk index of the location of each environmental acquisition unit in each clean area unit. S34: Analyze the types of pollutants collected by each environmental collection unit in each clean area unit within a preset time period in the past, and obtain the pollution risk index of the location of each environmental collection unit in each clean area unit. S35: Based on a comprehensive analysis of the temperature risk index, humidity risk index, and pollution risk index of each environmental acquisition unit in each clean area unit, the environmental risk index of each clean area unit is obtained.

5. The method for early warning of central air conditioning failure in pharmaceutical cleanrooms based on multi-source data according to claim 4, characterized in that, In step S32, according to formula one: Calculate the temperature risk index T at the location of the nth environmental acquisition unit in the mth clean area unit. mn ; Where f(X) is the first judgment function, f(X) = X when X > 0; f(X) = 0 when X ≤ 0; M is the total number of clean area units, m ∈ M; t n The current time is Δt; the preset duration is Δt; N is the total number of environmental data acquisition units, n∈N; T mns (t) represents the temperature change curve over time at the location of the nth environmental acquisition unit in the mth clean area unit; ρ is the preset ambient temperature; mT A preset adjustment coefficient for the temperature error of the target cleanliness level of the m-th clean area unit; W T This is the preset temperature tolerance value.

6. The method for early warning of central air conditioning failure in pharmaceutical cleanrooms based on multi-source data according to claim 5, characterized in that, In step S33, according to formula two: Calculate the humidity risk index S at the location of the nth environmental acquisition unit in the mth clean area unit. mn ; Among them, S mns (t) represents the humidity change curve over time at the location of the nth environmental acquisition unit in the mth clean area unit; S0 represents the preset environmental humidity; ρ mS A preset adjustment coefficient for humidity error of the target cleanliness level of the m-th clean area unit; W T This is the preset humidity tolerance value.

7. The method for early warning of central air conditioning failure in pharmaceutical cleanrooms based on multi-source data as described in claim 6, characterized in that, In step S34, according to formula three: Calculate the pollution risk index R of the location of the nth environmental acquisition unit in the mth clean area unit. mn ; Where K is the total number of pollutant types, k∈K; R mnks (t) represents the curve of the real-time value of the k-th pollutant type at the location of the nth environmental acquisition unit in the m-th clean area unit over time; ρ mk R is the preset adjustment coefficient for the k-th contaminant type of the target cleanliness level of the m-th clean area unit; k0 This is the preset concentration value for the kth pollutant type.

8. The method for early warning of central air conditioning failure in pharmaceutical cleanrooms based on multi-source data according to claim 7, characterized in that, In step S34, according to formula four: Calculate the environmental risk index U of the m-th clean area unit. m ; Wherein, γ1 is the first weighting coefficient; γ2 is the second weighting coefficient; γ3 is the third weighting coefficient; C1 is the first preset constant; C2 is the second preset constant; and C3 is the third preset constant.

9. The method for early warning of central air conditioning failure in pharmaceutical cleanrooms based on multi-source data as described in claim 8, characterized in that, Through formula five: Calculate the target air volume F per unit time for the air conditioning unit corresponding to the m-th clean area unit. m0 ; Where F0 is the preset air volume; C0 is the preset air volume constant; This is to round X up.

10. The method for early warning of central air conditioning failure in pharmaceutical cleanrooms based on multi-source data according to claim 9, characterized in that, The process for determining whether the air conditioning unit of the m-th clean area unit is abnormal is as follows: First, let's use formula six: Calculate the response exponent X of the current target working parameters. t ; Among them, t s F represents the current target operating parameter response adjustment time of the air conditioning unit in the m-th clean area unit; ms (t) represents the curve showing the change in the actual air output of the air conditioning unit of the m-th clean area unit over time; t ms+1 ρ represents the time after the current time when the target airflow of the air conditioning unit corresponding to the m-th clean area unit changes per unit time; t ρ is the first weighting coefficient; F c is the second weighting coefficient; t C is the first preset constant; F This is the second preset constant; The response index X of the current target working parameter t Compare with preset thresholds [D1, D2]; When 0 ≤ X t <When < D1, the air conditioning unit of the m-th clean area unit responds normally; When D1≤X t When <D2, the air conditioning unit of the m-th clean area unit responds with a slight abnormality; When D2≤X t At that time, the air conditioning unit of the m-th clean area unit responded with a serious anomaly.

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