Loose conditioning equipment wind balance state diagnosis method and device, electronic equipment and storage medium

Abstract

The application discloses a loose moisture recovery equipment wind balance state diagnosis method and device, electronic equipment and a storage medium. The method comprises the following steps: determining a first deviation index according to first data and a first correlation model; determining a first result according to the first deviation index; and determining a second result according to the first deviation index and a second deviation index based on the first result. The method can determine the working condition of the target equipment from different dimensions by constructing the first deviation index according to the first correlation model and the first data, determine the first result according to the first deviation index, and make a secondary determination according to the first result, so as to realize the combination of overall target equipment health determination and accurate positioning of unbalanced positions, accurately identify high-cost false balance hidden by closed-loop control, locate abnormal links, guide energy-saving adjustment, and improve equipment efficiency and quality stability.

Description

Technical Field

[0001] This invention relates to the field of quality control technology for cigarette manufacturing processes, and in particular to a method, apparatus, electronic device, and storage medium for diagnosing the wind balance state of loosening and rehydration equipment. Background Technology

[0002] Loosening and rehumidifying is a crucial step in tobacco processing. Its core objective is to loosen the tobacco leaves through heating and humidification, thereby improving their processing resistance. The loosening and rehumidifying equipment utilizes a combination of hot air circulation, dehumidification, and steam systems to heat, humidify, and loosen the tobacco leaves. After the process hot air is introduced into the drum and exchanges heat and moisture with the material, a portion is returned for reuse through the circulation pipeline, while the remainder is discharged through the dehumidification system. Unlike leaf-moistening and feeding equipment, the loosening and rehumidifying equipment does not involve the application of sugars; its process media are primarily water and steam.

[0003] Existing technologies suffer from the following main shortcomings: They lack a systematic online diagnostic method for airflow balance, making it impossible to quantitatively assess the balance between recirculating air, exhaust air, and inlet hot air. They also cannot identify high-energy-consumption spurious balance states masked by closed-loop control. When the exhaust airflow is too strong, the moisture control system automatically increases the water supply to maintain outlet moisture; when hot air circulation is insufficient or cold air infiltrates, the temperature control system automatically increases the steam valve opening to maintain the process temperature inside the drum. In this situation, although the outlet quality indicators are qualified, water and steam consumption are implicitly increased, forming a "high-cost spurious balance" that traditional monitoring methods cannot identify. Summary of the Invention

[0004] This invention provides a method, device, electronic equipment, and storage medium for diagnosing the airflow balance of a loose rehumidification equipment, in order to solve the problem that loose rehumidification equipment cannot quantitatively assess the balance relationship between circulating air, exhaust air, and inlet hot air.

[0005] According to one aspect of the present invention, a method for diagnosing the wind balance state of a loosening and rehumidifying device is provided, comprising: A first deviation index is determined based on first data and a first correlation model; the first data is the collected production change data of the target equipment; the target equipment is used for high-temperature vaporization, heating and humidification of the target object; the first correlation model is used to characterize the correlation between the various changes in the first data, and the first correlation model includes a first sub-correlation model, a second sub-correlation model, and a third sub-correlation model; the first sub-correlation model is used to characterize the relationship between the output air volume and the input air volume of the target equipment under the configuration of the first preset components; the second sub-correlation model is used to characterize the correlation model between the resistance state of the dehumidification system of the target equipment and the frequency of the dehumidification fan and the opening of the dehumidification damper under the configuration of the second preset components; the third sub-correlation model is used to characterize the mapping relationship between water volume change and air volume change; the first deviation index is the transformation deviation of the first data based on the first correlation model; the first deviation index includes: air volume balance deviation rate, pressure balance deviation rate, dehumidification and water addition matching deviation rate, energy consumption cost deviation rate, and cylinder internal pressure balance deviation rate; A first result is determined based on the first deviation index; the first result is used to characterize the degree of failure of the wind power system of the target equipment. Based on the first result, a second result is determined according to the first deviation index and the second deviation index; the second deviation index is a threshold corresponding to the first deviation index that is preset; the second result is used to characterize the imbalance type of the target equipment and the imbalanced equipment.

[0006] According to another aspect of the present invention, a device for diagnosing the wind balance state of a loosening and rehumidifying equipment is provided, comprising: The first deviation index determination module is used to determine the first deviation index based on the first data and the first correlation model. The first data is the collected production change data of the target equipment. The target equipment is used for high-temperature vaporization, heating and humidification of the target object. The first correlation model is used to characterize the correlation between the various changes in the first data, and the first correlation model includes a first sub-correlation model, a second sub-correlation model, and a third sub-correlation model. The first sub-correlation model is used to characterize the relationship between the output air volume and the input air volume of the target equipment under the configuration of the first preset components. The second sub-correlation model is used to characterize the correlation model between the resistance state of the dehumidification system of the target equipment and the frequency and opening degree of the dehumidification fan and the dehumidification damper under the configuration of the second preset components. The third sub-correlation model is used to characterize the mapping relationship between water volume change and air volume change. The first deviation index is the transformation deviation of the first data based on the first correlation model. The first deviation index includes: air volume balance deviation rate, pressure balance deviation rate, dehumidification and water addition matching deviation rate, energy consumption cost deviation rate, and cylinder pressure balance deviation rate. The first result determination module is used to determine a first result based on the first deviation index; the first result is used to characterize the degree of failure of the wind power system of the target equipment. The second result determination module is used to determine a second result based on the first result according to the first deviation index and the second deviation index; the second deviation index is a threshold corresponding to the first deviation index that is preset; the second result is used to characterize the imbalance type of the target equipment and the imbalanced equipment.

[0007] According to another aspect of the present invention, an electronic device is provided, the electronic device comprising: At least one processor; and A memory communicatively connected to the at least one processor; wherein, The memory stores a computer program that can be executed by the at least one processor, which enables the at least one processor to perform the wind balance state diagnosis method for loose and damp equipment according to any embodiment of the present invention.

[0008] According to another aspect of the present invention, a computer-readable storage medium is provided, the computer-readable storage medium storing computer instructions for causing a processor to execute and implement the wind balance state diagnosis method for loose moisture recirculation equipment as described in any embodiment of the present invention.

[0009] The technical solution of this invention determines a first deviation index based on first data and a first correlation model. The determination of the first deviation index describes the operating status of the target equipment from multiple dimensions. A first result is determined based on the first deviation index, which comprehensively evaluates the operational health of the target equipment. A second result is determined based on the first result and the first and second deviation indices. If the first result is abnormal, the determination of the second result can accurately locate the imbalance state, thereby achieving precise adjustment of the target equipment. This method, by constructing a first deviation index based on a first correlation model and first data, can determine the operating status of the target equipment from different dimensions. It determines a first result based on the first deviation index and performs a secondary determination based on the first result, achieving a combination of overall target equipment health assessment and precise location of imbalance. This enables accurate identification of high-cost false balances masked by closed-loop control, locating abnormal links, guiding energy-saving adjustments, and improving equipment efficiency and quality stability.

[0010] It should be understood that the description in this section is not intended to identify key or essential features of the embodiments of the present invention, nor is it intended to limit the scope of the invention. Other features of the invention will become readily apparent from the following description. Attached Figure Description

[0011] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0012] Figure 1 A flowchart illustrating a method for diagnosing the wind balance state of a loosening and rehumidifying equipment, provided in an embodiment of the present invention; Figure 2 This is a schematic diagram of the structure of a wind balance state diagnosis system for loosening and rehumidifying equipment provided in an embodiment of the present invention; Figure 3 This is a schematic diagram of the structure of a wind balance state diagnostic device for loosening and rehumidifying equipment provided in an embodiment of the present invention; Figure 4 A schematic diagram of the electronic device used to implement the wind balance state diagnosis method for loosening and rehumidifying equipment according to embodiments of the present invention. Detailed Implementation

[0013] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. 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 should fall within the scope of protection of the present invention.

[0014] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of the invention described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover a non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0015] In one embodiment, Figure 1This is a flowchart illustrating a method for diagnosing the wind balance state of a loosening and rehumidifying equipment according to an embodiment of the present invention. This embodiment is applicable to diagnosing the operating status of loosening and rehumidifying equipment. The method can be executed by a wind balance state diagnostic device for loosening and rehumidifying equipment, which can be implemented in hardware and / or software. This device can be configured in any electronic device with network communication capabilities. Figure 1 As shown, the method includes: S110. Determine the first deviation index based on the first data and the first correlation model; the first data is the production change data of the target equipment collected; the target equipment is used for high-temperature vaporization, heating and humidification of the target object; the first correlation model is used to characterize the correlation between the various changes in the first data, and the first correlation model includes a first sub-correlation model, a second sub-correlation model and a third sub-correlation model; the first sub-correlation model is used to characterize the relationship between the output air volume and the input air volume of the target equipment under the configuration of the first preset components; the second sub-correlation model is used to characterize the correlation model between the resistance state of the dehumidification system of the target equipment and the frequency of the dehumidification fan and the opening of the dehumidification damper under the configuration of the second preset components; the third sub-correlation model is used to characterize the mapping relationship between the water volume change and the air volume change; the first deviation index is the transformation deviation of the first data based on the first correlation model; the first deviation index includes: air volume balance deviation rate, pressure balance deviation rate, dehumidification and water addition matching deviation rate, energy consumption cost deviation rate and cylinder pressure balance deviation rate.

[0016] The target object can be tobacco.

[0017] The target equipment is a drum heating and humidification device.

[0018] Furthermore, the airflow structure of the drum heating and humidifying equipment varies depending on the model and is divided into three typical configurations: Configuration A: No replenishment air damper; circulating air is drawn from the discharge hood, pressurized by a circulating fan, and directly sent back into the drum without passing through a heat exchanger; Configuration B: No replenishment air damper; circulating air is drawn from the discharge hood, heated by a heat exchanger, and then sent into the drum inlet, mixing with the inlet hot air before entering the drum; Configuration C: No replenishment air damper; no circulating air system; only inlet hot air heated by the heat exchanger enters the drum, and a dehumidifying fan exhausts the air. Specifically, data related to each sub-association model in the first association model is obtained from the first data, and the obtained data is matched with the corresponding sub-association model and calculated to obtain the first deviation index.

[0019] Furthermore, the air volume balance deviation rate step is as follows: obtain air volume change data from the first data and input it into the first sub-association model for calculation to obtain the air volume balance deviation rate.

[0020] The first sub-association model is represented as: R1=(Q in +Q new -Q out ) / Q in ×100%. Where Q in Q is the inlet hot air volume; new To replenish fresh air volume (if the equipment is not equipped with a fresh air replenishment damper, this item will be zero); Q out Q is the air volume for the drum dehumidification system. out The total exhaust air volume is calculated by subtracting the flow rates of each exhaust branch pipe from the total exhaust air volume.

[0021] Furthermore, the steps for obtaining the pressure balance deviation rate are as follows: input the first data into the second sub-association model to obtain the baseline tidal pressure. Then, calculate the pressure balance deviation rate based on the baseline tidal pressure using the pressure balance deviation rate model.

[0022] The second sub-association model can be represented as: P0 = a0' + a1' × K out The second sub-association model matches target equipment without a recirculating air system. Or, P0 = a0 + a1×K out + a2×f loop (Frequency of circulating fan) + a3×K loop The second sub-association model matches the target equipment configured in the circulating air system.

[0023] The pressure balance deviation rate model can be expressed as: R2=|P-P0| / P0×100%, where P0 is the baseline tidal pressure value.

[0024] Further, the steps for obtaining the dehumidification and water supply matching deviation rate are as follows: Construct a dehumidification and water supply matching deviation rate model based on the third sub-association relationship. Obtain the drum dehumidification air volume Q from the first data. out The water supply flow rate W is input into the tidal discharge water supply matching deviation rate model for calculation, and the tidal discharge water supply matching deviation rate is obtained.

[0025] The tidal discharge and water supply matching deviation rate model can be expressed as: R3 = (Q out / WQ out0 / W0)÷(Q out0 / W0)×100%. Where, Q out0 This represents the exhaust air volume under baseline conditions.

[0026] Furthermore, the steps for obtaining the energy consumption cost deviation rate are as follows: determine the energy consumption cost deviation rate model based on the third sub-association relationship, obtain the water flow rate and steam application amount from the first data, input the water flow rate and steam application amount into the energy consumption cost deviation rate model for calculation, and obtain the energy consumption cost deviation rate.

[0027] The energy consumption cost deviation rate model can be expressed as: R4 = α × |W-W0| / W0 + β × |S-S0| / S0. Here, α and β are weighting coefficients, and α + β = 1. W0 is the water flow rate under the baseline condition; S0 is the steam application rate under the baseline condition.

[0028] Further, the steps for obtaining the pressure balance deviation rate inside the cylinder are as follows: Obtain the corresponding data from the first data set, and then obtain the equivalent gas volume flow rate at the input end. Calculate the equivalent gas volume flow rate at the input end and the drum dehumidification airflow using the pressure balance deviation rate model inside the cylinder to obtain the pressure balance deviation rate inside the cylinder.

[0029] The pressure balance deviation rate model inside the cylinder can be expressed as: R5 = (M in -M out ) / M out ×100%. Where M out The air volume Q for drum dehumidification out M in This is the equivalent gas volume flow rate at the input end.

[0030] Among them, M in It can be represented as: M in ≈Q in +k1×S+k2×W+k3×Q loop Among them, Q loop S is the circulating air volume, S is the steam application rate, W is the water supply flow rate, and Q is the water volume. in This refers to the inlet hot air volume.

[0031] Preferably, the first association model has the ability to update online adaptively: when the system is continuously at the "excellent" level and the baseline conditions are met, the current running data is incorporated, and the baseline value and model coefficients are updated in a sliding window manner.

[0032] The above steps, the first deviation index, jointly explain the system state from five dimensions: air volume conservation, circulating air pressurization, dehumidification-water addition matching, thermodynamic vaporization expansion, and comprehensive pressure balance inside the cylinder. R1, R3, and R5 use signed deviation rates to indicate the direction of deviation. The sign comparison between R5 and R1 can trigger cross-validation, providing physical theoretical support for independent multiple diagnosis and significantly improving the engineering interpretability of the diagnosis.

[0033] S120. Determine the first result based on the first deviation index; the first result is used to characterize the degree of failure of the wind power system of the target equipment.

[0034] Specifically, the indicators in the first deviation index are normalized and weighted to obtain the fifth result. The fifth result is then compared with the preset deviation index, and the first result is determined based on the comparison result.

[0035] Wherein, the fifth result can be expressed as: B=|R1|′×w1+R2′×w2+|R3|′×w3+ R4′×w4+|R5|′×w5. Wherein, w1 to w5 are weight coefficients, satisfying w1+w2+w3+w4+w5=1.

[0036] Further, the specific acquisition steps of the first result are: when B is less than or equal to the first threshold, the first result is rated as excellent; when the first threshold < B ≤ the second threshold, the first result is rated as good; when B is greater than the second threshold, the first result is rated as poor.

[0037] S130. Determine a second result based on the first result according to the first deviation index and the second deviation index; the second deviation index is a preset threshold corresponding to the first deviation index; the second result is used to characterize the unbalance type of the target device and the unbalanced device.

[0038] Wherein, the second deviation index includes: a preset air volume balance deviation rate T1, a preset pressure balance deviation rate T2, a preset moisture exhaust and water addition matching deviation rate T3, a preset energy consumption cost deviation rate T4, and the preset in-cylinder pressure balance deviation rate includes: a positive threshold T5 and a negative threshold T6.

[0039] Further, the second deviation index is respectively determined according to the statistical quantiles of each deviation index under historical normal working conditions.

[0040] Specifically, if the first result is excellent, it indicates that the air system can operate normally; if the first result is good, it indicates that there is a deviation in the air system, and a attention prompt needs to be triggered; if the first result is poor, it indicates that there is a problem in the air system, so an alarm is issued, and further evaluation is performed according to the first deviation index, that is, the first deviation index matches the corresponding deviation index from the second deviation index for comparison, a corresponding sub-result is generated according to the comparison result, and all the obtained sub-results are integrated to obtain the second result.

[0041] Wherein, the second result includes one or more of air volume unbalance, abnormal moisture exhaust resistance, moisture exhaust-water addition matching unbalance, high-cost false balance and in-cylinder pressure unbalance.

[0042] In the above steps, the judgment of the first result and the second result realizes that each deviation index independently compares its own threshold, allows multiple alarms, avoids missed diagnosis that may be caused by the traditional single leading index judgment, and at the same time integrates the wind balance index B to provide an intuitive overall health level rating.

[0043] Further, after the second result is obtained, simultaneous early warning is performed according to different unbalance contents included in the second result. It can fully reflect the concurrent situations of air volume unbalance, abnormal moisture exhaust resistance, moisture exhaust-water addition matching unbalance, high-cost false balance and in-cylinder pressure unbalance, and avoids the limitation that traditional methods can only report a single fault.

[0044] Furthermore, after obtaining the second result, it also includes: outputting the second result and adjustment suggestions, that is, outputting the overall health level, all triggered imbalance types and corresponding adjustment suggestions to the human-computer interaction interface.

[0045] Furthermore, after obtaining the second result, the following steps are also included: when the discharge moisture content Wm or discharge temperature Tm exceeds the limit, the wind power balance diagnosis result is called first; if any deviation index exceeds the limit, the wind power balance adjustment is executed first; if all deviation indexes do not exceed the limit, other factors such as steam supply, water supply system or incoming material characteristics are prompted to be checked.

[0046] Furthermore, the above steps are achieved through a wind balance state diagnostic system for loosening and rehumidifying equipment, such as... Figure 2 As shown, the loosening and rehumidification equipment wind balance status diagnostic system includes: a data acquisition module for acquiring initial data; a benchmark model construction and storage module for establishing and storing airflow conservation benchmarks, tidal resistance benchmarks, and benchmark energy consumption status; an online diagnostic calculation module for calculating R1, R2, R3, R4, R5, and B, and assessing the overall health level; an independent diagnosis and multi-source analysis module for comparing each deviation index with its respective preset deviation index, outputting all imbalance types exceeding the standard, and locating abnormal links; and a human-computer interaction module for displaying the overall health level, diagnostic results, and adjustment suggestions.

[0047] For example, taking the WQ387 loose rehumidifier as an example, and using the three sets of operating data of configuration A as examples, the calculation process and diagnostic logic of the method of the present invention are demonstrated. The data of test 1 and test 2 are obtained by measuring the pressure of each pipeline through the newly added differential pressure sensor, and the water flow rate and steam consumption are acquired through the PLC system. The normal operating condition data are reference values ​​estimated based on the benchmark model.

[0048] The target equipment parameters are as follows: circulating fan power 7.5kW, speed 1440r / min; exhaust fan power 7.5kW, speed 2905r / min; inlet hot air fan power 5.5kW, speed 1452r / min. All three ducts are 480mm in diameter. The circulating duct is 7.8m long with no bends; the inlet hot air duct is 8.0m long with two bends; the total length of the exhaust duct is approximately 95m. The pressure sensor is installed approximately 38m from the exhaust fan outlet. The baseline drum exhaust airflow Q is... out0 The value was determined to be 2058m using a soft measurement model. 3 / h, the baseline evacuation damper opening is 27%, the baseline water flow rate W0 = 350 kg / h, and the baseline steam application rate S0 = 10.5%. The comprehensive flow coefficient of each pipeline was calibrated by actual measurement during the baseline period: inlet hot air pipe k in =120, exhaust pipe k out =55, circulating air duct k loop=135. The dehumidification system adopts a branch-merging structure: the drum inlet dehumidification pipe (approximately 9m, with one elbow) and the drum outlet dehumidification pipe (approximately 3m, without an elbow) merge at the outer end of the drum and then enter the main dehumidification pipe of the equipment. After being pressurized by the dehumidification fan, the dehumidification airflow is sent to the pressure sensor and the centralized dehumidification system. The inlet and outlet dehumidification branch pipes are close to both ends of the drum body, with the outlet branch pipe being the shortest, having the least resistance, and exhibiting the most significant diversion effect. Drum dehumidification airflow Q out The total exhaust air volume is multiplied by the penetration ratio, which is determined by calibration at the baseline period. Test 1 and Test 2 use a conservative value of 40%, while normal operating conditions use 60%.

[0049] Table 1. First data from Experiment 1 (Severe imbalance condition): Table 2. First data from Experiment 2 (Improved operating conditions): Table 3. First data obtained from normal operating condition design (based on the baseline model): The normal operating condition design data is based on the benchmark model, taking into account the physical parameters of each pipeline (pipe diameter 480mm, length and number of bends), the calibrated K value (circulation 135, inlet hot air 120, exhaust 55), and the air volume conservation relationship (Q). in ≈Q out The pressure of 1400 Pa was calculated by reverse matching with the baseline water supply flow rate (350 kg / h) and steam application rate (10.5%). The exhaust pressure was derived from the baseline exhaust air volume of 2058 m³ / h. 3 / h and drainage pipe k out =55. The relationship between air volume and pressure is P=(Q / k). 2 The forward calculation yields the following baseline tidal pressure values: 580Pa is the estimated value under normal operating conditions, while 1400Pa is the separately calculated baseline tidal pressure value used for R2 calculation. These are different concepts.

[0050] Furthermore, for the first data from Experiment 1, a fault assessment is performed. First, parameter conversion is conducted, then the first deviation index is calculated. Based on the first deviation index, the first result is determined. Based on the first result, combined with the first deviation index and the second deviation index, the second result is determined. The specific steps are as follows: Step 1: Parameter conversion: The air volume of each pipeline is converted from the measured pressure and its respective k value, as shown in Table 4, Q in =120× ≈624m 3 / h; Main exhaust air volume = 55 × ≈2379m 3 / h; Drum dehumidification air volume Qout =2379×40%≈952m 3 / h;Q loop =135× ≈1642m 3 / h.

[0051] Table 4. Conversion values ​​of the first data in Experiment 1. Step 2: Calculate the first deviation index: Wherein, R1 (airflow balance deviation rate): R1=(Q in -Q out ) / Q in ×100%=(624-952) / 624×100%≈-52.6%R1 is negative, the output air volume is greater than the input air volume, and the system tends to be negative pressure.

[0052] Wherein, R2 (pressure balance deviation rate): reference tidal discharge pressure P0 = (2058 / 55) 2 The current exhaust pressure is 1870 Pa, and the exhaust damper opening is only 8%, resulting in a significant deviation of the exhaust resistance characteristic value from the baseline. Calculations show that R²≈|1870-1400| / 1400≈33.6%.

[0053] Among them, R3 (dampness discharge-water addition matching deviation rate): the baseline dampness discharge-water addition ratio is Q. out0 / W0=2058 / 350≈5.88, the current drum dehumidification-water addition ratio is 952 / 460≈2.07. R3=(2.07-5.88) / 5.88×100%≈-64.8%, R3 is a negative value, indicating that the dehumidification capacity is seriously weak (insufficient effective through air volume), and the matching degree between the drum dehumidification air volume and the water addition volume is far below the benchmark.

[0054] Among them, R4 (energy consumption cost deviation rate): taking α=0.5, β=0.5: R4=0.5×|460-350| / 350+0.5×|12.2%-10.5%| / 10.5%=23.8%, the water flow rate increases by 31.4%, and the steam application rate increases by 16.2%. Among them, the deviation of water flow rate exceeding the preset proportion of 20% and the deviation of steam application rate not exceeding the preset proportion are judged as "simple water evaporation aggravation".

[0055] Wherein, R5 (pressure balance deviation rate inside the cylinder): take k1=1.24 (steam volume conversion), k2=124×40% (converted by adding water vaporization, 40% is the evaporation efficiency), k3=0.1 (circulating air pressure boosting coefficient). M in≈624+1.24×500+124×0.4×460+0.1×1642=624+620+22816+164=24224m 3 / h (equivalent). M out =Q out =952m 3 / h. R5=(24224-952) / 952≈+2445.8%R5 is a positive value and far exceeds the threshold, the equivalent gas volume flow rate at the input end (24224 m³ / h). 3 / h) far exceeds the output end (952m) 3 / h), indicating that the positive pressure inside the cylinder is too high. Analysis of input contribution: Water vaporization expansion term (22816 m). 3 / h) accounts for M in 94.2% of the positive pressure is the most significant factor leading to positive pressure, which perfectly matches the subtype determination of "intensified simple water evaporation" in R4. The contradiction between the negative value of R1 (tending towards negative pressure) and the positive value of R5 (actual positive pressure) verifies the diagnostic value of the input contribution analysis of R5—the combined effect of water vaporization expansion and circulating air pressurization overwhelms the negative pressure tendency caused by air volume imbalance.

[0056] Step 3: Calculate the fifth result B The R(i,max) values ​​for each indicator are: R(1,max)=500%, R(2,max)=1000%, R(3,max)=200%, R(4,max)=100%, R(5,max)=3000%. After normalization: |R1|'=52.6 / 500=0.105, R2'=33.6 / 1000=0.034, |R3|'=64.8 / 200=0.324, R4'=23.8 / 100=0.238, |R5|'=2445.8 / 3000=0.815. Let w1=0.20, w2=0.15, w3=0.15, w4=0.30, w5=0.20; B=0.20×0.105+0.15×0.034+0.15×0.324+0.30×0.238+0.20×0.815=0.309.

[0057] Step 4: Overall health level assessment, i.e., determining the primary result. The first threshold is set to 0.25, and the second threshold is set to 0.40. B=0.309, which is between the first and second thresholds, and is rated as "good". This triggers a notification and initiates the second result determination.

[0058] Step 5: Determine the second result The second deviation index is defined as follows: T1 = 30% (determined by |R1|), T2 = 50%, T3 = 30% (determined by |R3|), T4 = 15%, T5 = 100%, and T6 = -100%. Each deviation index in the first deviation index is compared with its corresponding index in the second deviation index, and corresponding sub-results are generated based on the comparison results. See Table 5 for an example.

[0059] Table 5 Comparison results of Experiment 1 Furthermore, as shown in Table 5, the contradiction between a negative value for R1 (tending towards negative pressure) and a positive value for R5 (actual positive pressure) can be resolved by analyzing the contribution of R5 input: water vaporization and expansion account for 94.2% of the total input, overriding the negative pressure tendency caused by airflow imbalance. The inconsistency in the signs of R5 and R1 triggers cross-validation, verifying the necessity of R5 as a comprehensive pressure diagnostic indicator, thus forming a complete diagnostic closed loop.

[0060] The comprehensive diagnostic conclusion, i.e., the second result, is that the wind power system suffers from four concurrent imbalances: air volume imbalance (excessive output), moisture discharge-water supply mismatch (weak moisture discharge), high-cost false balance (exacerbated simple water evaporation), and excessive positive pressure inside the cylinder. The normal R2 indicates that the high moisture discharge pressure is mainly due to inherent pipeline resistance and centralized system back pressure, rather than a malfunction in the unit's moisture discharge system.

[0061] Further, the following adjustment recommendations are made for the target equipment: Primary measure – reduce the frequency of the exhaust fan to gradually reduce the exhaust air volume back to near the baseline. Secondary measures – increase the opening of the circulating damper to increase the circulating air volume and improve the pressure state and through-flow volume within the cylinder. Simultaneously, appropriately reduce the water supply flow rate to minimize the contribution of vapor expansion to the positive pressure within the cylinder.

[0062] Furthermore, for the first data of Experiment 2, a fault assessment is performed. First, parameter conversion is performed, then the first deviation index is calculated. Based on the first deviation index, the first result is determined. Based on the first result, combined with the first deviation index and the second deviation index, the second result is determined. The specific steps are as follows:

[0063] Step 1: Parameter conversion: The air volume of each pipeline is converted from the measured pressure and its respective k value, as shown in Table 6, Q in =120× ≈849m 3 / h; Main exhaust air volume = 55 × ≈2109m 3 / h; Drum dehumidification air volume Q out =2109×40%≈844m 3 / h;Q loop =135× ≈2323m 3 / h.

[0064] Table 6. Conversion values ​​for Experiment 2 Step 2: Calculate the first deviation index: Wherein, R1 (airflow balance deviation rate): R1=(Q in -Q out ) / Q in ×100%=(849-844) / 849×100%≈+0.6%. R1 is close to zero and positive, the input air volume is slightly greater than the output air volume, the air volume is basically conserved, and there is a slight positive pressure. Compared with Experiment 1, R1 improved from -52.6% (output too large) to +0.6% (basically balanced), an improvement of 53.2 percentage points.

[0065] Among them, R2 (pressure balance deviation rate): the evacuation pressure changed from 1870 to 1470 Pa, R2≈|1470-1400| / 1400≈5.0%, which is significantly lower than that of Experiment 1 (33.6%), and the evacuation pressure is close to the benchmark level.

[0066] Among them, R3 (dampness removal-water addition matching deviation rate): the current drum dampness removal-water addition ratio is 844 / 350≈2.41, and the benchmark is 5.88. R3=(2.41-5.88) / 5.88×100%≈-59.0% R3 is still negative, and the dampness removal capacity is still weak, but it is an improvement compared to Experiment 1 (-64.8%).

[0067] Wherein, R4 (energy consumption cost deviation rate): take α=0.5, β=0.5: R4=0.5×|350-350| / 350+0.5×|10.5%-10.5%| / 10.5%=0%.

[0068] Wherein, R5 (pressure balance deviation rate inside the cylinder): take k1=1.24 (steam volume conversion), k2=124×40% (converted by adding water vaporization, 40% is the evaporation efficiency), k3=0.1 (circulating air pressure boosting coefficient). M in ≈849+1.24×420+124×0.4×350+0.1×2323=18962m 3 / h (equivalent). M out =Q out =844m 3 / h. R5=(18962-844) / 844≈+2147.6%. R5 is still positive and far exceeds the threshold, but it is about 12% lower than that of Experiment 1 (+2445.8%), which is consistent with the trend of the pressure inside the cylinder improving from a significantly positive pressure to a slightly positive pressure.

[0069] Step 3: Calculate the fifth result B The R(i,max) values ​​for each indicator are: R(1,max)=500%, R(2,max)=1000%, R(3,max)=200%, R(4,max)=100%, R(5,max)=3000%. After normalization: |R1|'=0.6 / 500=0.001, R2'=5.0 / 1000=0.005, |R3|'=59.0 / 200=0.295, R4'=0, |R5|'=2147.6 / 3000=0.716. Let w1 = 0.20, w2 = 0.15, w3 = 0.15, w4 = 0.30, w5 = 0.20; then B = 0.20 × 0.001 + 0.15 × 0.005 + 0.15 × 0.295 + 0.30 × 0 + 0.20 × 0.716 = 0.188 Step 4: Overall health level assessment, i.e., determining the primary result. The first threshold is set at 0.25, and the second threshold is set at 0.40. B = 0.188 ≤ 0.25, and is rated as "excellent".

[0070] Step 5: Determine the second result Each independent threshold is set as follows: T1=30% (determined by |R1|), T2=50%, T3=30% (determined by |R3|), T4=15%, T5=100%, T6=-100%. Each deviation index in the first deviation index is compared with its corresponding index in the second deviation index, and corresponding sub-results are generated based on the comparison results. See Table 7.

[0071] Table 7 Comparison results of Experiment 2 Furthermore, as shown in Table 7, R4 has returned to normal, and the high-cost spurious balance has been eliminated. R1 and R2 have both returned to normal. Although R3 still exceeds the standard, it has significantly improved compared to Experiment 1. R5 is positive and has decreased by about 12% compared to Experiment 1, consistent with the direction of improvement in the internal pressure state. The signs of R1 (slightly positive pressure) and R5 (excessive positive pressure) are consistent, making the diagnostic conclusion self-consistent.

[0072] Furthermore, for the first data under normal operating conditions, fault assessment is performed. First, parameter conversion is conducted, then the first deviation index is calculated. Based on the first deviation index, the first result is determined. Based on the first result, combined with the first deviation index and the second deviation index, the second result is determined. The specific steps are as follows: Step 1: Parameter conversion: The air volume of each pipeline is converted from the measured pressure and its respective k value, as shown in Table 8, Q in =120× ≈1859m 3 / h; Main exhaust air volume = 55 × ≈2058m 3 / h; Drum dehumidification air volume Q out =2058×60%≈1235m 3 / h;Q loop =135× ≈2002m 3 / h.

[0073] Table 8. Conversion values ​​under normal operating conditions Step 2: Calculate the first deviation index: Wherein, R1 (airflow balance deviation rate): R1=(Q in -Q out ) / Q in ×100%=(1859-1235) / 1859×100%≈+33.6%. R1 is a positive value, input air volume (1859m³ / s). 3 / h) is greater than the output air volume (1235m) 3 / h), the system tends towards positive pressure. |R1| slightly exceeds the 30% threshold, reflecting that the branch pipe diversion effect still exists under normal operating conditions, but the deviation is small.

[0074] R2 (pressure balance deviation rate): The drainage pressure is 1400 Pa, and the reference pressure is also 1400 Pa. R2≈0%, which is within the normal range. Under normal operating conditions, the drainage pressure is completely consistent with the reference pressure, and the drainage resistance is normal.

[0075] Among them, R3 (dampness exhaust-water filling matching deviation rate): The current drum dampness exhaust-water filling ratio is 1235 / 350≈3.53, and the benchmark is 5.88. R3=(3.53-5.88) / 5.88×100%≈-40.0%. R3 is a negative value, exceeding the 30% threshold. Under normal operating conditions, the R3 exceeding the standard is due to the setting of the penetration ratio (60%)—even if the exhaust main duct air volume has reached the benchmark value of 2058m³. 3 / h, due to the branch pipe diversion effect, the effective through air volume is only 60% of that of the main pipe, resulting in the drum dehumidification-water addition ratio being lower than the benchmark.

[0076] Wherein, R4 (energy consumption cost deviation rate): take α=0.5, β=0.5: R4=0.5×|350-350| / 350+0.5×|10.5%-10.5%| / 10.5%=0%.

[0077] Wherein, R5 (pressure balance deviation rate inside the cylinder): take k1=1.24 (steam volume conversion), k2=124×40% (converted by adding water vaporization, 40% is the evaporation efficiency), k3=0.1 (circulating air pressure boosting coefficient). M in≈1859+1.24×420+124×0.4×350+0.1×2002=19940m 3 / h (equivalent). M out =Q out =1235m 3 / h.

[0078] R5 = (19940-1235) / 1235 ≈ +1514.6%. R5 is positive and far exceeds the threshold, reflecting the normal physical characteristic that the water vaporization expansion term dominates at the input end (17360 / 19940 = 87.1%). The pressure inside the cylinder is slightly negative, but R5 shows a high positive pressure. This difference stems from R5 only considering the balance between the total gas mass at the input end and the effective through-flow volume, without separately accounting for the contribution of slight negative pressure factors such as air leakage at the seals. Under normal operating conditions, this difference is an inherent background characteristic of the system and does not affect the effectiveness of R5 as a comprehensive pressure diagnostic indicator.

[0079] Step 3: Calculate the fifth result B The R(i,max) values ​​for each indicator are: R(1,max)=500%, R(2,max)=1000%, R(3,max)=200%, R(4,max)=100%, R(5,max)=3000%. After normalization: |R1|'=33.6 / 500=0.067, R2'=0 / 1000=0, |R3|'=40.0 / 200=0.2, R4'=0, |R5|'=1514.6 / 3000=0.505. Let w1=0.20, w2=0.15, w3=0.15, w4=0.30, w5=0.20: then B=0.20×0.067+0.15×0+0.15×0.2+0.30×0+0.20×0.505=0.144.

[0080] Step 4: Overall health level assessment, i.e., determining the primary result. The first threshold is set at 0.25, and the second threshold is set at 0.40. B = 0.144 ≤ 0.25, which is rated as "excellent". The B value under normal operating conditions (0.144) is lower than that in Experiment 2 (0.188), which is consistent with the expectation that the overall health level under normal operating conditions is better than that in Experiment 2.

[0081] Step 5: Determine the second result Each independent threshold is set as follows: T1=30% (determined by |R1|), T2=50%, T3=30% (determined by |R3|), T4=15%, T5=100%, T6=-100%. Each deviation index in the first deviation index is compared with its corresponding index in the second deviation index, and corresponding sub-results are generated based on the comparison results. See Table 9.

[0082] Table 9 Comparison Results under Normal Operating Conditions Furthermore, as shown in Table 9, R2 returns to normal under normal operating conditions, indicating that the desiccation resistance is normal. |R1| is slightly out of range (33.6% vs 30%), R3 exceeds the range due to the system setting of the penetration ratio (60%), and R5's positive value reflects the normal physical characteristics of water vaporization and expansion. These non-faulty background characteristics can automatically adapt after the baseline model has fully learned, and the alarm thresholds of each indicator will be automatically calibrated to a reasonable level.

[0083] Further, a comprehensive comparison of the three operating conditions is shown in Table 10. The table shows that in Experiment 1, the signs of R1 (-52.6%, indicating a high output and negative pressure) and R5 (+2445.8%, actual positive pressure) are inconsistent, triggering cross-validation. Analysis of the input contribution of R5 shows that water vaporization and expansion account for 94.2% of the total input, overriding the negative pressure tendency caused by airflow imbalance. This discrepancy verifies the necessity of R5 as a comprehensive pressure diagnostic indicator, forming the core of the diagnostic closed-loop of this method. R2 is normal (33.6%, not exceeding the 50% threshold), indicating that the high exhaust pressure mainly stems from inherent pipeline resistance and centralized system back pressure. B=0.309 is rated as "Good". In Experiment 2, R1 improved from -52.6% to +0.6% (basically balanced), both indicating positive pressure with consistent signs, making the diagnostic conclusion self-consistent. R2 and R4 have both returned to normal. B=0.188 is rated as "Excellent". Under normal operating conditions: B=0.144, rated as "Excellent," indicating an overall health level superior to Experiment 2. R2 recovered to 0%, perfectly consistent with the baseline. |R1| slightly exceeded the threshold (33.6%), R3 exceeded the standard due to the penetration ratio setting, and the positive value of R5 reflects the normal physical characteristics of water vaporization and expansion. The fifth result shows a decreasing trend of B from Experiment 1 (0.309, Good) → Experiment 2 (0.188, Excellent) → Normal operating conditions (0.144, Excellent), consistent with the improvement direction of the internal pressure state from significantly positive pressure → slightly positive pressure → slightly negative pressure. The sign characteristics of R1 and R3 make the deviation direction clearly identifiable. The contradiction in the signs of R1 and R5 in Experiment 1 and the explanation of the input contribution analysis of R5 fully demonstrate the core value of the "diagnostic closed loop" of this invention.

[0084] Table 10: Comprehensive Comparison Results of Three Working Conditions Optionally, at least one method for determining the first association model includes steps A1-A4: Step A1: Determine the second data; the second data is the production change data of the target equipment at historical moments collected under the first condition; the second data includes: air volume change data, water volume change data, temperature data and dehumidification data; the first condition includes: the discharge moisture content is within the first preset range, the discharge temperature is within the second preset range and the drum inlet hot air temperature is within the third preset range.

[0085] Specifically, the system retrieves production change data from a pre-set database showing that the target device met the first condition at a given historical moment.

[0086] Furthermore, if the target device has an internal pressure or differential pressure sensor, the second data also includes: the internal pressure state parameter P.

[0087] The air volume change data includes: air volume-related parameters for each pipeline and damper opening parameters. The air volume-related parameters for each pipeline include at least: hot air volume-related parameters at the drum inlet and dehumidification air volume-related parameters for the drum exhaust. The damper opening parameters include at least: dehumidification damper opening and recirculation damper opening.

[0088] Furthermore, if the target equipment is equipped with a fresh air damper, the air volume change data also includes: fresh air volume related parameters and damper opening parameters.

[0089] Among them, the water volume change data includes at least: steam application rate parameter S, water flow rate parameter W, and discharge moisture content parameter Wm.

[0090] The temperature data includes at least: discharge temperature parameter Tm, and drum inlet hot air temperature parameter T. air。

[0091] The exhaust data includes at least: exhaust fan frequency and exhaust damper opening. Furthermore, the exhaust data may also include: recirculation fan frequency and recirculation damper opening.

[0092] Furthermore, if the target equipment does not have conventional equipment such as an internal pressure sensor and a pipeline differential pressure sensor, the second data that can be obtained includes: circulating fan frequency, exhaust fan frequency, exhaust damper opening, circulating damper opening, steam valve opening (or steam flow rate), water supply flow rate, discharge moisture content Wm, discharge temperature Tm, and drum inlet hot air temperature T. air All the above data is from the original equipment and requires no additional hardware. The airflow data was obtained through the construction and calibration of a soft sensing model.

[0093] Furthermore, the steps for constructing the soft measurement model are as follows: obtain the fan operating frequency (Hz) and the corresponding damper opening (%). Construct a mapping model based on the fan operating frequency (Hz) and the corresponding damper opening; calibrate the mapping model and perform online calculations to obtain air volume data.

[0094] Furthermore, the specific steps for constructing the mapping model are as follows: Under the condition that the pipeline resistance characteristics remain unchanged, the air volume is approximately proportional to the fan frequency and has a non-linear relationship with the damper opening. Therefore, a mapping model Q=g(f,K) is established with the fan frequency and damper opening as inputs and the corresponding pipeline air volume as output. This mapping model can be achieved through function fitting, table lookup interpolation, or regression equations.

[0095] Further, the specific steps for calibrating and online calculating the mapping model are as follows: During the baseline operating period after equipment commissioning, multiple steady-state operating points are selected, and the air volume of each pipeline is calibrated by short-term field measurements using a portable anemometer. The calibration data is then fitted using the least squares method to obtain the specific parameters of the mapping model. Simultaneously, the rationality of the calculated results is verified using nameplate parameters such as the fan's rated power and rated speed. Since the k-value of each pipeline differs due to variations in resistance characteristics such as pipe diameter, length, and number of bends, separate calibrations are required. After calibration, the system reads the current fan frequency and damper opening, and calculates the current air volume in real time using the mapping model.

[0096] Furthermore, the main exhaust air volume can be calculated using the exhaust fan frequency and exhaust damper opening as described above; the drum exhaust air volume Q out The volume of the main exhaust air is obtained by multiplying the penetration ratio, and the inlet hot air volume is calculated from the exhaust air volume of the drum based on the air volume conservation relationship.

[0097] Furthermore, if the target equipment is equipped with the following additional sensors: differential pressure sensors are installed in the hot air duct at the drum inlet, the outlet duct of the dehumidifying fan, and the straight section of the circulating air duct; and a micro differential pressure sensor is installed inside the drum between the drum inlet hood and the outlet hood, then the airflow data acquisition method is as follows: the airflow data is obtained using the calibrated differential pressure-airflow relationship Q = k × Direct conversion is used, where k is the comprehensive flow coefficient, determined by actual measurement and calibration with an anemometer during the baseline period. The k value varies for each pipeline due to differences in resistance characteristics such as pipeline diameter, length, and number of bends.

[0098] For example, the exhaust pressure sensor is installed approximately 38 meters from the exhaust fan outlet, located in the middle section of an exhaust pipeline approximately 95 meters long. The pressure measured by the exhaust pressure sensor is the residual static pressure after the total pressure at the fan outlet has overcome the friction loss along the upstream pipeline, and is also affected by the back pressure from the downstream approximately 57-meter pipeline and the centralized exhaust system. The overall flow coefficient is selected as k. out =55 reflects the overall resistance characteristics of the pipe section from the fan outlet through the 38-meter pipeline to the sensor section.

[0099] For example, taking the WQ387 loosening and rehumidifying machine as an example, the diameter of all three air ducts is 480mm. According to actual measurements and calibration during the baseline period: the circulating air duct (7.8m in length, without bends) kloop =135; Inlet hot air duct (8.0m long, two bends) k in =120; Drainage pipe (pressure sensor installed 38m from the fan, total pipe length 95m) k out =55. The circulating air duct is the shortest and has no bends, resulting in the highest k-value; the two bends in the inlet hot air duct reduce the k-value by approximately 11%; the exhaust duct is approximately 4.9 times the length of the circulating air duct, significantly increasing friction loss along the path, and its k-value is approximately 41% of that of the circulating air duct. The two methods of obtaining air volume data do not affect the calculation formulas and diagnostic logic of any deviation indicators in this method, but only affect the accuracy of the raw data.

[0100] Step A2: Determine the first sub-association model based on the air volume change data.

[0101] The first preset component is a fresh air damper.

[0102] Specifically, first, it is determined whether the target device has a first preset component. If it does, the first sub-association model is constructed using the first construction method; if it does not, the first sub-association model is constructed using the second construction method.

[0103] Furthermore, the implementation steps of the first construction method are as follows: construct the conservation relationship between the output air volume and the input air volume of the target device, that is, the hot air volume input at the inlet of the target device is the input, and the dehumidification air volume output at the outlet of the target device is the output. The two are approximately equal, so the first sub-association model is: hot air volume = dehumidification air volume.

[0104] Furthermore, the implementation steps of the second construction method are as follows: construct the conservation relationship between the output air volume, input air volume and replenishment fresh air volume of the target device, that is, the first sub-association model is: hot air volume + replenishment fresh air volume = exhaust air volume.

[0105] Among them, the dehumidification air volume is the dehumidification air volume of the main drum pipe minus the flow rate of each dehumidification branch pipe.

[0106] It consists of hot air inlet, water vapor generated by water vaporization, and water vapor injected by steam application.

[0107] Furthermore, the construction of the first sub-association model is not affected by the circulating air system, that is, the circulating air is an internal circulation loop of the system, and the circulating air volume does not participate in the inflow and outflow balance of the system boundary, regardless of whether the equipment is equipped with a circulating air system.

[0108] Step A3: Determine the second sub-correlation model based on the tidal data.

[0109] The second preset component is a circulating air system.

[0110] Specifically, taking the opening degree K of the exhaust damper as an example. out Circulating fan frequency floop Circulating air damper opening K loop Using the measured pressure P from the exhaust pressure sensor as the dependent variable, a multiple linear regression equation is established using the least squares method. The regression coefficients a0, a1, a2, and a3 are obtained through fitting, thus yielding the second sub-correlation model. Alternatively, the exhaust damper opening K can be used as the independent variable. out Using the measured pressure P from the tidal pressure sensor as the dependent variable, a multiple linear regression equation is established using the least squares method. The specific values ​​of the regression coefficients a0' and a1' are obtained through fitting, thus yielding the second sub-correlation model.

[0111] Furthermore, the second sub-association model can be expressed as: P0 = a0' + a1' × K out The second sub-association model matches target equipment without a recirculating air system. Or, P0 = a0 + a1×K out + a2×f loop (Frequency of circulating fan) + a3×K loop The second sub-association model matches the target equipment configured in the circulating air system.

[0112] Furthermore, the second sub-correlation model is constructed based on the following: the magnitude of the dehumidification negative pressure directly affects the flow direction and velocity distribution of the airflow inside the drum. Excessive dehumidification negative pressure will result in the hot air staying in the drum for too short a time and reduced thermal efficiency; while insufficient dehumidification negative pressure will result in the moisture not being discharged in time and will affect the loosening effect.

[0113] Step A4: Determine the third sub-association model based on water volume change data.

[0114] The water volume change data includes: the baseline value of water flow rate W0 and the baseline value of steam application rate S0 under the baseline conditions.

[0115] Specifically, by fitting data through regression analysis, the mapping relationship between water volume change data and air volume related parameters of each pipeline is obtained through fitting, which serves as the third sub-association model.

[0116] The first, second, and third sub-association models described above possess online adaptive update capabilities. When the system is rated "excellent" for a certain period of time and all baseline conditions are met, the data for that period is automatically included in the historical database, and each baseline value, normalized upper limit value R(i,max), and independent threshold is updated using a sliding window method. This mechanism can automatically follow the gradual changes in production conditions, ensuring the long-term accuracy and reliability of diagnostic conclusions.

[0117] Optionally, a first deviation index is determined based on the first data and the first correlation model, including steps B1-B4: Step B1: Determine the air volume balance deviation rate based on the first data and the first sub-correlation model.

[0118] Specifically, the first data is input into the first sub-association model for calculation to obtain the air volume balance deviation rate.

[0119] The first sub-association model is represented as: R1=(Q in +Q new -Q out ) / Q in ×100%. Where Q in Q is the inlet hot air volume; new To replenish fresh air volume (if the equipment is not equipped with a fresh air replenishment damper, this item will be zero); Q out Q is the air volume for the drum dehumidification process. out The total exhaust air volume is calculated by subtracting the flow rates of each exhaust branch pipe from the total exhaust air volume.

[0120] Step B2: Determine the pressure balance deviation rate based on the first data and the second sub-correlation model.

[0121] Specifically, the first data is input into the second sub-correlation model to obtain the baseline tidal pressure. Based on the baseline tidal pressure, the pressure balance deviation rate is calculated using the pressure balance deviation rate model to obtain the pressure balance deviation rate.

[0122] Furthermore, the pressure balance deviation rate model can be expressed as: R2=|P-P0| / P0×100%, where P0 is the benchmark evacuation pressure value given by the pressure benchmark inside the cylinder. This benchmark evacuation pressure value is determined by the opening of the evacuation damper, the frequency of the circulating fan, and the opening of the circulating damper, or it can be determined solely by the opening of the evacuation damper. The distinction is made based on whether a circulating air system exists.

[0123] Furthermore, the above steps are based on the presence of an internal pressure sensor in the target equipment to obtain the pressure balance deviation rate. If the target equipment does not have an internal pressure sensor: R2 is determined by the frequency of the exhaust fan, the opening of the exhaust damper, and the frequency of the circulating fan and the opening of the circulating damper (if present), that is, indirectly calculated based on the deviation between the characteristic value of the exhaust resistance and the reference characteristic value.

[0124] Furthermore, input items related to the circulating fan frequency and circulating damper opening in configuration C:R2 are automatically removed, and only the exhaust side parameters are determined; in independent diagnosis, when R2 exceeds the standard, it is judged as "exhaust resistance imbalance". R5's M in In the calculation of the circulating air booster term k3×Q loop Automatically reset to zero.

[0125] Furthermore, the changes in R1 and R2 are detected in real time. Based on the detected slow deviation trend, early warnings can be given before the pipeline becomes severely blocked, guiding preventive cleaning and maintenance, and avoiding false diagnostics and equipment performance degradation caused by K value drift.

[0126] Step B3: Determine the tidal discharge and water supply matching deviation rate and energy consumption cost deviation rate based on the first data and the third sub-correlation model.

[0127] Among them, the energy consumption cost deviation rate represents the weighted degree of deviation of water flow rate and steam application amount from the benchmark.

[0128] Specifically, a desiccation and water supply matching deviation rate model is constructed based on the third sub-relationship. The drum desiccation air volume Q is obtained from the first data. out The water supply flow rate W is input into the tidal discharge and water supply matching deviation rate model for calculation, resulting in the tidal discharge and water supply matching deviation rate. Based on the third sub-correlation relationship, the energy consumption cost deviation rate model is determined. The water supply flow rate and steam application rate are obtained from the first data set and input into the energy consumption cost deviation rate model for calculation, resulting in the energy consumption cost deviation rate.

[0129] Furthermore, the tidal discharge and water supply matching deviation rate model can be expressed as: R3 = (Q out / WQ out0 / W0)÷(Q out0 / W0)×100%. Where, Q out0 This represents the exhaust air volume under baseline conditions.

[0130] R3>0 indicates that the drainage capacity is relatively strong (or the water added is too little), and R3<0 indicates that the drainage capacity is relatively weak (or the water added is too much).

[0131] Furthermore, the energy consumption cost deviation rate model can be expressed as: R4 = α × |W - W0| / W0 + β × |S - S0| / S0. Where α and β are weighting coefficients, and α + β = 1. Where W0 is the water flow rate under the baseline condition; S0 is the steam application rate under the baseline condition.

[0132] Furthermore, the energy consumption cost deviation rate is set based on the following physical mechanism: excessive exhaust will lead to two concurrent energy consumption costs: on the one hand, excessive exhaust air volume carries away a large amount of heat, and in order to maintain the process temperature inside the drum (circulating air temperature or outlet material temperature), the temperature control system will be forced to increase the steam application rate (S increases); on the other hand, excessive exhaust accelerates moisture evaporation, and in order to maintain the outlet moisture setpoint, the moisture PID will be forced to increase the water supply flow rate (W increases). Regardless of whether the direct target of steam control is the circulating air temperature or the outlet material temperature, increased heat loss will inevitably lead to increased steam consumption. The degree of deviation of the two benchmarks is weighted and combined to form the R4 index.

[0133] The above steps reveal the dual high-cost false equilibrium mechanism of "excessive dehumidification → heat loss → increased steam in temperature control system → increased steam consumption" and "excessive dehumidification → excessive water evaporation → increased water addition due to water PID → increased water consumption". It can also distinguish three subtypes based on the deviation combination of water addition flow rate and steam application amount, providing a clear diagnostic basis and classification control direction for energy saving and consumption reduction.

[0134] Step B4: Determine the pressure balance deviation rate inside the cylinder based on the first data.

[0135] Among them, the pressure balance deviation rate inside the drum reflects the comprehensive deviation index of the pressure state inside the drum, and characterizes the balance degree of gas mass flow rate between the input end and the output end. In other words, it comprehensively reflects the dynamic balance relationship between the input of four dimensions: inlet hot air, steam injection, water vaporization and circulating air pressurization, and effective dehumidification output.

[0136] Specifically, the circulating air volume, steam application rate, water flow rate, and inlet hot air volume are obtained from the first data set. Based on the obtained data, the equivalent gas volumetric flow rate at the input end is determined. The equivalent gas volumetric flow rate at the input end and the drum exhaust air volume are then calculated using the drum pressure balance deviation rate model to obtain the drum pressure balance deviation rate.

[0137] Furthermore, the pressure balance deviation rate model inside the cylinder can be expressed as: R5 = (M in -M out ) / M out ×100%. Where M out The air volume Q for drum dehumidification out M in This is the equivalent gas volume flow rate at the input end.

[0138] Among them, M in It can be represented as: M in ≈Q in +k1×S+k2×W+k3×Q loop Among them, Q loop S is the circulating air volume, S is the steam application rate, W is the water supply flow rate, and Q is the water volume. in This refers to the inlet hot air volume.

[0139] Furthermore, R5>0 indicates that the input end is greater than the output end, and gas accumulates inside the cylinder, tending towards positive pressure; R5<0 indicates that the output end is greater than the input end, and gas is discharged from the cylinder, tending towards negative pressure.

[0140] Furthermore, the pressure balance deviation rate R5 inside the cylinder forms a diagnostic closed loop of "cause → effect" with the air volume balance deviation rate R1. When the signs of R1 and R5 are inconsistent (e.g., R1<0 indicates negative pressure, but R5>0 indicates positive pressure), it indicates that the direction of air volume imbalance is overwhelmed by the thermodynamic factors in the energy consumption cost deviation rate R4 and the circulating air pressurization effect of the pressure balance deviation rate R2. The system automatically triggers cross-validation and makes a comprehensive judgment by combining the input contribution analysis of R5.

[0141] The above steps, the pressure balance deviation rate R5 inside the drum, is a dynamic balance between the input of four dimensions—inlet hot air, steam injection, water vaporization, and circulating air pressurization—and the effective dehumidification output. It directly characterizes the pressure state inside the drum, filling the technical gap that traditional diagnostic methods cannot directly assess the pressure balance inside the drum.

[0142] Optionally, the pressure balance deviation rate is determined based on the first data and the second sub-correlation model, including steps C1-C3: Step C1: Determine the third result based on the presence of preset sensors in the target device.

[0143] The preset sensor is a moisture pressure sensor.

[0144] Specifically, the system determines whether a preset sensor is configured in the target device. If it is, the third result is that a preset sensor exists; if it is not, the third result is that a preset sensor does not exist.

[0145] Step C2: If the third result indicates the existence of a preset sensor, then determine the reference evaporation pressure value based on the first data, and determine the pressure balance deviation rate based on the reference evaporation pressure value.

[0146] Specifically, if the third result indicates the existence of a preset sensor, the opening degree of the exhaust damper, the frequency of the circulating fan, and the opening degree of the circulating damper are obtained from the first data and input into the second sub-association model to obtain the baseline exhaust pressure value. The baseline exhaust pressure is then calculated using the pressure balance deviation rate model to obtain the pressure balance deviation rate.

[0147] Furthermore, if the target device includes a second preset component, then the second sub-association model is P0 = a0 + a1×K. out + a2×f loop (Frequency of circulating fan) + a3×K loop If the target device does not contain the second preset component, then the second sub-association model is P0 = a0' + a1' × K. out .

[0148] Step C3: If the third result is that there is no preset sensor, then determine the pressure balance deviation rate based on the fourth result and the first data; the fourth result is used to characterize whether the target device has a second preset component.

[0149] Specifically, if the third result indicates that there is no preset sensor and the target device does not have a second preset component, the pressure balance deviation rate is indirectly calculated by the deviations of the exhaust fan frequency, exhaust damper opening, and circulating fan frequency and circulating damper opening (if present) from their respective reference values. If the third result indicates that there is no preset sensor and the target device has a second preset component, the pressure balance deviation rate is directly calculated by the deviation between the real-time pressure inside the cylinder and the reference pressure.

[0150] Furthermore, the calculation method can employ a ratio method (such as using the ratio of the exhaust fan frequency to the exhaust damper opening as the resistance characteristic value), a difference method, or a regression modeling method based on baseline data. Regardless of the specific calculation method used, the core is to compare the current resistance characteristic value with the baseline characteristic value, using the deviation rate as the value of R². The specific calculation method can be determined based on the equipment characteristics and site conditions during the baseline period.

[0151] Optionally, a first result is determined based on a first deviation index, including steps D1-D3: Step D1: Normalize the first deviation index to obtain the third deviation index.

[0152] Specifically, the indicators included in the first deviation index are normalized using different normalization methods to obtain the third deviation index.

[0153] The third deviation index includes: normalized air volume balance deviation rate |R1|′, normalized pressure balance deviation rate R2′, normalized humidification and water supply matching deviation rate |R3|′, normalized energy consumption cost deviation rate R4′, and normalized cylinder internal pressure balance deviation rate |R5|′.

[0154] Furthermore, |R1|′=|R1| / |R(1,max)|; R2′=R2 / R(2,max); |R3|′=|R3| / |R(3,max)|; R4′=R4 / R(4,max); |R5|′=|R5| / |R(5,max)|. Here, R(i,max) represents the reasonable upper limit value observed during historical normal operation for each deviation indicator, used to map each indicator to the same scale.

[0155] Step D2: Weight the third deviation index to obtain the fifth result.

[0156] Specifically, the weights are matched to the indicators included in the third deviation index, and then weighted to obtain the fifth result.

[0157] Further, the fifth result may be expressed as: B=|R1|′×w1+R2′×w2+|R3|′×w3+ R4′×w4+|R5|′×w5. Where w1 to w5 are weight coefficients, satisfying w1+w2+w3+w4+w5=1. Preferably, the value range of each weight coefficient is 0.10 to 0.35.

[0158] Step D3: Determine the first result according to the fifth result and a preset deviation index.

[0159] Specifically, the fifth result is compared with the preset deviation index, that is, when B ≤ the first threshold, the first result is assessed as excellent; when the first threshold < B ≤ the second threshold, the first result is assessed as good, and a attention prompt is triggered; when B > the second threshold, the first result is assessed as poor, an alarm is triggered and a second result evaluation is started.

[0160] Optionally, the determining the second result based on the first result according to the first deviation index and the second deviation index by the deviation index includes steps E1 to E5: Step E1: Determine a first sub-result according to an air volume balance deviation rate and a preset air volume balance deviation rate; the first sub-result is used to characterize the degree and direction of air volume imbalance of the target device.

[0161] Specifically, the air volume balance deviation rate is compared with the preset air volume balance deviation rate. If the absolute value of the air volume balance deviation rate is greater than the preset air volume balance deviation rate, the sign of the air volume balance deviation rate is judged. If the air volume balance deviation rate is positive, it is indicated that the input air volume is too large and tends to positive pressure; if the air volume balance deviation rate is negative, it is indicated that the output air volume is too large and tends to negative pressure, then the first sub-result is air volume imbalance, and air leakage, damper abnormality or measurement fault is located. If the absolute value of the air volume balance deviation rate is less than the preset air volume balance deviation rate, the first sub-result is that the air volume is normal.

[0162] For example, if |R1|>T1, it is determined as "air volume imbalance", and the imbalance direction is further determined according to the sign of R1: R1>0 indicates that the input air volume is too large and tends to positive pressure; R1<0 indicates that the output air volume is too large and tends to negative pressure, then the first sub-result is air volume imbalance, and air leakage, damper abnormality or measurement fault is located.

[0163] Step E2: Determine a second sub-result according to a pressure balance deviation rate and a preset pressure balance deviation rate; the second sub-result is used to characterize the dehumidification resistance imbalance degree or the circulating air dehumidification coordination imbalance degree of the target device.

[0164] Specifically, the pressure balance deviation rate is compared with the preset pressure balance deviation rate. If the pressure balance deviation rate is less than the preset pressure balance deviation rate, the second sub-result is that the moisture exhaust resistance is normal or the circulation air-moisture exhaust coordination is normal. If the pressure balance deviation rate is greater than the preset pressure balance deviation rate, the second sub-result is that the moisture exhaust resistance is unbalanced or the circulation air-moisture exhaust coordination is unbalanced.

[0165] As an example, if R2>T2, the second sub-result is determined as "moisture exhaust resistance unbalanced" (when there is no circulating air system) or "circulation air-moisture exhaust coordination unbalanced" (when there is a circulating air system), and abnormalities of the moisture exhaust damper, moisture exhaust pipeline, moisture exhaust fan, circulating air damper or circulating fan are analyzed. If R2<T2, the second sub-result is that the moisture exhaust resistance is normal or the circulation air-moisture exhaust coordination is normal.

[0166] Step E3: Determine a third sub-result according to the moisture exhaust and water addition matching deviation rate and the preset moisture exhaust and water addition matching deviation rate; the third sub-result is used to characterize the unbalance degree and unbalance direction of the moisture exhaust and water addition matching of the target equipment.

[0167] Specifically, the moisture exhaust and water addition matching deviation rate is compared with the preset moisture exhaust and water addition matching deviation rate. If the absolute value of the moisture exhaust and water addition matching deviation rate is greater than the preset moisture exhaust and water addition matching deviation rate, it is determined that the moisture exhaust-water addition matching is unbalanced, and the sign of the moisture exhaust and water addition matching deviation rate is further judged. If the moisture exhaust and water addition matching deviation rate is greater than 0, the third sub-result is that the moisture exhaust is on the strong side or the water addition is on the low side; if the moisture exhaust and water addition matching deviation rate is less than 0, the third sub-result is that the moisture exhaust is on the weak side or the water addition is on the high side. If the absolute value of the moisture exhaust and water addition matching deviation rate is less than the preset moisture exhaust and water addition matching deviation rate, the third sub-result is that the moisture exhaust-water addition matching is normal.

[0168] As an example, if |R3|>T3, it is determined that the moisture exhaust-water addition matching is unbalanced, and the deviation direction is further determined according to the sign of R3: if R3>0, the third sub-result is that the moisture exhaust-water addition matching is unbalanced, and the moisture exhaust is on the strong side or the water addition is on the low side; if R3<0, the third sub-result is that the moisture exhaust-water addition matching is unbalanced, and the moisture exhaust is on the weak side or the water addition is on the high side. If |R3|<T3, the third sub-result is that the moisture exhaust-water addition matching is normal.

[0169] Step E4: Determine a fourth sub-result according to the energy consumption cost deviation rate and the preset energy consumption cost deviation rate; the fourth sub-result is used to characterize the deviation degree of the water addition flow rate and the steam application amount.

[0170] Specifically, the energy consumption cost deviation rate is compared with a preset energy consumption cost deviation rate. If the energy consumption cost deviation rate is greater than the preset energy consumption cost deviation rate, a first ratio and a second ratio are obtained. The first ratio is compared with a first preset ratio, and the second ratio is compared with a second preset ratio at the same time. If the first ratio is greater than the first preset ratio and the second ratio is greater than the second preset ratio, the fourth sub-result is a double high-cost false balance. If the first ratio is greater than the first preset ratio and the second ratio is less than or equal to the second preset ratio, the fourth sub-result is intensified water evaporation. If the first ratio is less than or equal to the first preset ratio and the second ratio is greater than the second preset ratio, the fourth sub-result is intensified heat loss.

[0171] As an example, if R4>T4 and it is determined as "high-cost false balance", the first ratio |W-W0| / W0 and the second ratio |S-S0| / S0 are obtained. The first ratio is compared with the first preset ratio, and the second ratio is compared with the second preset ratio at the same time. If both |W-W0| / W0 and |S-S0| / S0 exceed their respective preset ratios, it is determined as a double high-cost false balance, and it is recommended to reduce the frequency of the moisture exhaust fan or close the moisture exhaust damper, while appropriately increasing the circulating air volume; when only |W-W0| / W0 exceeds the preset ratio, it is determined as simple intensified water evaporation, and it is recommended to appropriately reduce the moisture exhaust capacity; when only |S-S0| / S0 exceeds the preset ratio, it is determined as intensified heat loss, and it is recommended to check the sealing state of the circulating air system or equipment.

[0172] Step E5: Determine a fifth sub-result according to the in-cylinder pressure balance deviation rate and a preset in-cylinder pressure balance deviation rate; the fifth sub-result is used to characterize the internal pressure state of the drum.

[0173] Wherein, the preset in-cylinder pressure balance deviation rate includes a positive threshold T5 and a negative threshold T6.

[0174] Specifically, the in-cylinder pressure balance deviation rate is compared with the preset in-cylinder pressure balance deviation rate. If the in-cylinder pressure balance deviation rate is greater than the positive threshold, the fifth sub-result is that the positive pressure in the cylinder is too high; if the in-cylinder pressure balance deviation rate is less than the negative threshold, the fifth sub-result is that the negative pressure in the cylinder is too high.

[0175] As an example, if R5>T5, it is determined that "the positive pressure in the cylinder is too high", and the input end contribution is analyzed in combination with R4: if the water adding flow W is significantly higher than the reference value, it indicates that the positive pressure is mainly caused by water adding vaporization and expansion, and it is recommended to appropriately reduce the water adding flow; if the steam application amount S is significantly higher than the reference value, it indicates that the positive pressure is mainly caused by steam injection, and it is recommended to check the steam system; if both are normal, it indicates that the positive pressure may be caused by excessive circulating air pressurization or insufficient moisture exhaust capacity, and it is recommended to check the R2 related links. If R5<T6 (negative threshold), it is determined that "the negative pressure in the cylinder is too high", and it is recommended to close the moisture exhaust damper or reduce the frequency of the moisture exhaust fan.

[0176] Optionally, a fourth sub-result is determined based on the energy consumption cost deviation rate and the preset energy consumption cost deviation rate, including steps F1-F4: Step F1: If the energy consumption cost deviation rate is greater than the preset energy consumption cost deviation rate, then obtain the first ratio and the second ratio; the first ratio is used to characterize the degree of deviation of water addition; the second ratio is used to characterize the degree of deviation of steam application.

[0177] The first ratio is determined based on the water flow rate W and the water flow rate W0 under the baseline conditions. The first ratio can be expressed as: |W-W0| / W0.

[0178] The second ratio is based on the steam application amount S and the steam application amount S0|S-S0| / S0 under the reference condition.

[0179] Furthermore, the preset ratios of the first ratio |W-W0| / W0 and the second ratio |S-S0| / S0 can be set according to actual process requirements, with a preferred range of 20% to 30%.

[0180] Specifically, if the energy consumption cost deviation rate is greater than the preset energy consumption cost deviation rate, i.e., R4>T4, it is determined to be a high-cost false balance, and the first ratio and the second ratio are obtained.

[0181] Among them, the high-cost false balance is the situation where the target equipment uses steam and water volume far exceeding the process requirements to maintain the apparent standard of the outlet, forming a continuous false balance. This directly causes a significant increase in the energy consumption of steam, water, and hot air fans, while also resulting in increased raw material losses and auxiliary material losses in subsequent processes, leading to a significant increase in the overall cost of silk production.

[0182] Step F2: If the first ratio is greater than the first preset ratio and the second ratio is greater than the second preset ratio, then the fourth sub-result is a double high-cost false balance.

[0183] The first layer of false balance is caused by excessive humidification of the target equipment's pre-processed loose and damp tobacco leaves to mask defects and control deficiencies. The second layer of false balance occurs when the target equipment produces tobacco leaves with uneven moisture content and excessive surface free water. Subsequent equipment then uses additional steam, hot air, and dehumidification energy to compensate for these defects, creating a second layer of false balance. Both stages rely on excessive consumption of process media to maintain instrument compliance, resulting in a double amplification of steam, water, electricity, raw material, and auxiliary material costs.

[0184] Specifically, the first ratio is compared with the first preset ratio, and the second ratio is compared with the second preset ratio. If the first ratio is greater than the first preset ratio and the second ratio is greater than the second preset ratio, then the fourth sub-result is a double high-cost false balance.

[0185] Furthermore, if the fourth sub-result is a double high-cost false equilibrium, it is recommended to reduce the frequency of the exhaust fan or close the exhaust damper, while appropriately increasing the circulating air volume.

[0186] Step F3: If the first ratio is greater than the first preset ratio and the second ratio is less than or equal to the second preset ratio, then the fourth sub-result is increased water evaporation.

[0187] Specifically, the first ratio is compared with the first preset ratio, and the second ratio is compared with the second preset ratio. If the first ratio is greater than the first preset ratio and the second ratio is less than or equal to the second preset ratio, then the fourth sub-result is that water evaporation is aggravated.

[0188] Furthermore, if the fourth result indicates increased moisture evaporation, it is recommended to appropriately reduce the dehumidification capacity.

[0189] Step F4: If the first ratio is less than or equal to the first preset ratio, and the second ratio is greater than the second preset ratio, then the fourth sub-result is increased heat loss.

[0190] Specifically, the first ratio is compared with the first preset ratio, and the second ratio is compared with the second preset ratio. If the first ratio is less than or equal to the first preset ratio, and the second ratio is greater than the second preset ratio, then the fourth sub-result is that heat loss is aggravated.

[0191] Furthermore, if the fourth sub-result is increased heat loss, it is recommended to check the circulating air system or the sealing condition of the equipment.

[0192] The technical solution of this embodiment determines a first deviation index based on first data and a first correlation model. The determination of the first deviation index describes the operating status of the target equipment from multiple dimensions. A first result is determined based on the first deviation index, which comprehensively evaluates the operational health status of the target equipment. A second result is determined based on the first result and the first and second deviation indices. If the first result is abnormal, the determination of the second result can accurately locate the imbalance state, thereby achieving precise adjustment of the target equipment. This method, by constructing a first deviation index based on a first correlation model and first data, can determine the operating status of the target equipment from different dimensions. It determines a first result based on the first deviation index and performs a second determination based on the first result, achieving a combination of overall target equipment health assessment and precise location of imbalance. This enables accurate identification of high-cost false balances masked by closed-loop control, locating abnormal links, guiding energy-saving adjustments, and improving equipment efficiency and quality stability.

[0193] Figure 3This is a schematic diagram of a wind balance state diagnostic device for loosening and rehumidifying equipment provided in an embodiment of the present invention. This embodiment is applicable to diagnosing the operating condition of loosening and rehumidifying equipment. The wind balance state diagnostic device can be implemented in hardware and / or software, and can be configured in any electronic device with network communication capabilities. Figure 3 As shown, the device includes: a first deviation index determination module 210, a first result determination module 220, and a second result determination module 230, wherein: First Deviation Index Determination Module 210: Used to determine the first deviation index based on first data and a first correlation model; the first data is the collected production change data of the target equipment; the target equipment is used for high-temperature vaporization, heating and humidification of the target object; the first correlation model is used to characterize the correlation between the various changes in the first data, and the first correlation model includes a first sub-correlation model, a second sub-correlation model and a third sub-correlation model; the first sub-correlation model is used to characterize the relationship between the output air volume and the input air volume of the target equipment under the configuration of the first preset components; the second sub-correlation model is used to characterize the correlation model between the resistance state of the dehumidification system of the target equipment and the frequency of the dehumidification fan and the opening of the dehumidification damper under the configuration of the second preset components; the third sub-correlation model is used to characterize the mapping relationship between water volume change and air volume change; the first deviation index is the transformation deviation of the first data based on the first correlation model; the first deviation index includes: air volume balance deviation rate, pressure balance deviation rate, dehumidification and water addition matching deviation rate, energy consumption cost deviation rate and cylinder pressure balance deviation rate; First Result Determination Module 220: Used to determine a first result based on a first deviation index; the first result is used to characterize the degree of failure of the target equipment's wind power system. Second result determination module 230: used to determine a second result based on the first result according to the first deviation index and the second deviation index; the second deviation index is a threshold corresponding to the first deviation index set in advance; the second result is used to characterize the imbalance type of the target equipment and the imbalanced equipment.

[0194] Optionally, the first deviation index determination module 210 includes: Second data determination unit: used to determine second data; the second data is the production change data of the target equipment at a historical moment collected under the first condition; the second data includes: air volume change data, water volume change data, temperature data and dehumidification data; the first condition includes: the discharge moisture content is within a first preset range, the discharge temperature is within a second preset range and the drum inlet hot air temperature is within a third preset range. First Sub-Association Model Determination Unit: Used to determine the first sub-association model based on air volume change data; Second Sub-Association Model Determination Unit: Used to determine the second sub-association model based on tidal data; Third Sub-Association Model Determination Unit: Used to determine the third sub-association model based on water volume change data.

[0195] Optionally, the first deviation index determination module 210 includes: Airflow balance deviation rate determination unit: used to determine the airflow balance deviation rate based on the first data and the first sub-association model; Pressure balance deviation rate determination unit: used to determine the pressure balance deviation rate based on the first data and the second sub-correlation model; Deviation rate determination unit: used to determine the tide discharge and water supply matching deviation rate and energy consumption cost deviation rate based on the first data and the third sub-correlation model; Internal pressure balance deviation rate determination unit: used to determine the internal pressure balance deviation rate based on the first data.

[0196] Optional, the pressure balance deviation rate determination unit includes: Third Result Determination Subunit: Used to determine the third result based on the presence of preset sensors in the target device; Pressure balance deviation rate determination subunit: If the third result is that a preset sensor exists, then determine the reference evacuation pressure value based on the first data, and determine the pressure balance deviation rate based on the reference evacuation pressure value; Pressure balance deviation rate determination subunit: if the third result is that there is no preset sensor, then the pressure balance deviation rate is determined based on the fourth result and the first data; the fourth result is used to characterize whether the target device has a second preset component.

[0197] Optionally, the first result determination module 220 includes: The third deviation index determination unit is used to normalize the first deviation index to obtain the third deviation index. The fifth result determination unit is used to perform a weighted summation of the third deviation index to obtain the fifth result. First Result Determination Unit: Used to determine the first result by comparing the fifth result with the preset deviation index.

[0198] Optionally, the second result determination module 230 includes: First sub-result determination unit: used to determine the first sub-result based on the air volume balance deviation rate and the preset air volume balance deviation rate; the first sub-result is used to characterize the degree and direction of air volume imbalance of the target equipment; Second sub-result determination unit: used to determine the second sub-result based on the pressure balance deviation rate and the preset pressure balance deviation rate; the second sub-result is used to characterize the degree of imbalance of the exhaust resistance of the target equipment or the degree of imbalance of the coordinated exhaust of the circulating air. The third sub-result determination unit is used to determine the third sub-result based on the tidal discharge and water supply matching deviation rate and the preset tidal discharge and water supply matching deviation rate; the third sub-result is used to characterize the degree and direction of the tidal discharge and water supply matching imbalance of the target equipment. The fourth sub-result determination unit is used to determine the fourth sub-result based on the energy consumption cost deviation rate and the preset energy consumption cost deviation rate; the fourth sub-result is used to characterize the degree of deviation of water flow rate and steam application rate; The fifth sub-result determination unit is used to determine the fifth sub-result based on the cylinder pressure balance deviation rate and the preset cylinder pressure balance deviation rate; the fifth sub-result is used to characterize the pressure state inside the drum.

[0199] Optionally, the fourth sub-result determination unit is specifically used for: If the energy consumption cost deviation rate is greater than the preset energy consumption cost deviation rate, then a first ratio and a second ratio are obtained; the first ratio is used to characterize the degree of deviation in water addition; the second ratio is used to characterize the degree of deviation in steam application. If the first ratio is greater than the first preset ratio and the second ratio is greater than the second preset ratio, then the fourth sub-result is a double high-cost false balance; If the first ratio is greater than the first preset ratio, and the second ratio is less than or equal to the second preset ratio, then the fourth sub-result is increased water evaporation. If the first ratio is less than or equal to the first preset ratio, and the second ratio is greater than the second preset ratio, then the fourth sub-result is increased heat loss.

[0200] The wind balance state diagnostic device for loose and damp equipment provided in this embodiment of the invention can execute the wind balance state diagnostic method for loose and damp equipment provided in any of the above embodiments of the invention. It has the corresponding functions and beneficial effects of executing the wind balance state diagnostic method for loose and damp equipment. For detailed process, please refer to the relevant operations of the wind balance state diagnostic method for loose and damp equipment in the foregoing embodiments.

[0201] In one embodiment, Figure 4 This is a structural block diagram of an electronic device provided in an embodiment of the present invention, such as... Figure 4 The diagram illustrates a schematic representation of an electronic device 10 that can be used to implement embodiments of the present invention. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processors, cellular phones, smartphones, wearable devices (e.g., helmets, glasses, watches, etc.), and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely illustrative and are not intended to limit the implementation of the invention described and / or claimed herein.

[0202] like Figure 4 As shown, the electronic device 10 includes at least one processor 11 and a memory, such as a read-only memory (ROM) 12, a random access memory (RAM) 13, etc., which is communicatively connected to the at least one processor 11. The memory stores a computer program that can be executed by the at least one processor 11, and the computer program is executed by the at least one processor 11 to enable the at least one processor 11 to perform the method provided by the present invention.

[0203] The processor 11 can perform various appropriate actions and processes based on a computer program stored in the read-only memory (ROM) 12 or a computer program loaded from the storage unit 18 into the random access memory (RAM) 13. The RAM 13 can also store various programs and data required for the operation of the electronic device 10. The processor 11, ROM 12, and RAM 13 are interconnected via a bus 14. An input / output (I / O) interface 15 is also connected to the bus 14.

[0204] Multiple components in electronic device 10 are connected to I / O interface 15, including: input unit 16, such as keyboard, mouse, etc.; output unit 17, such as various types of displays, speakers, etc.; storage unit 18, such as disk, optical disk, etc.; and communication unit 19, such as network card, modem, wireless transceiver, etc. Communication unit 19 allows electronic device 10 to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks.

[0205] Processor 11 can be a variety of general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of processor 11 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special-purpose artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, digital signal processors (DSPs), and any suitable processor, controller, microcontroller, etc. Processor 11 performs the various methods and processes described above, such as the methods provided in this invention.

[0206] In some embodiments, the methods provided herein may be implemented as a computer program tangibly contained in a computer-readable storage medium, such as storage unit 18. In some embodiments, part or all of the computer program may be loaded and / or installed on electronic device 10 via ROM 12 and / or communication unit 19. When the computer program is loaded into RAM 13 and executed by processor 11, one or more steps of the methods described above may be performed. Alternatively, in other embodiments, processor 11 may be configured to execute the methods by any other suitable means (e.g., by means of firmware).

[0207] Various embodiments of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), application-specific standard parts (ASSPs), systems-on-chip (SoCs), complex programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments may include implementations in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which may be a dedicated or general-purpose programmable processor, capable of receiving data and instructions from a storage system, at least one input device, and at least one output device, and transmitting data and instructions to the storage system, the at least one input device, and the at least one output device.

[0208] Computer programs used to implement the methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, such that when executed by the processor, the computer programs cause the functions / operations specified in the flowcharts and / or block diagrams to be performed. The computer programs may be executed entirely on a machine, partially on a machine, or as a standalone software package, partially on a machine and partially on a remote machine, or entirely on a remote machine or server.

[0209] In the context of this invention, a computer-readable storage medium stores computer instructions that are used to cause a processor to execute and implement the method provided by this invention.

[0210] The present invention also provides a computer program product comprising a computer program that, when executed by a processor, implements the method provided according to embodiments of the present invention. A computer-readable storage medium may be a tangible medium that may contain or store a computer program for use by or in conjunction with an instruction execution system, apparatus, or device. The computer-readable storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination thereof. Alternatively, the computer-readable storage medium may be a machine-readable signal medium. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof.

[0211] To provide interaction with a user, the systems and techniques described herein can be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user provides input to the electronic device. Other types of devices can also be used to provide interaction with the user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including sound input, voice input, or tactile input).

[0212] The systems and technologies described herein can be implemented in computing systems that include backend components (e.g., as data servers), or middleware components (e.g., application servers), or frontend components (e.g., user computers with graphical user interfaces or web browsers through which users can interact with implementations of the systems and technologies described herein), or any combination of such backend, middleware, or frontend components. The components of the system can be interconnected via digital data communication of any form or medium (e.g., communication networks). Examples of communication networks include local area networks (LANs), wide area networks (WANs), blockchain networks, and the Internet.

[0213] A computing system can include clients and servers. Clients and servers are generally located far apart and typically interact through communication networks. The client-server relationship is created by computer programs running on the respective computers and having a client-server relationship with each other. The server can be a cloud server, also known as a cloud computing server or cloud host, which is a hosting product within the cloud computing service system to address the shortcomings of traditional physical hosts and VPS services, such as high management difficulty and weak business scalability.

[0214] This invention also provides a computer program product, including a computer program that, when executed by a processor, can implement the methods provided in any embodiment of this application.

[0215] In the implementation of the computer program product, computer program code for performing the operations of this application can be written in one or more programming languages ​​or a combination thereof. Programming languages ​​include object-oriented programming languages ​​such as Java, Smalltalk, and C++, as well as conventional procedural programming languages ​​such as C or similar languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving remote computers, the remote computer can be connected to the user's computer via any type of network—including a local area network (LAN) or a wide area network (WAN)—or can be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0216] It should be understood that the various forms of processes shown above can be used, with steps reordered, added, or deleted. For example, the steps described in this invention can be executed in parallel, sequentially, or in different orders, as long as the desired result of the technical solution of this invention can be achieved, and this is not limited herein.

[0217] The specific embodiments described above do not constitute a limitation on the scope of protection of this invention. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this invention should be included within the scope of protection of this invention.

Claims

1. A method for diagnosing the wind balance state of a loosening and rehumidifying device, characterized in that, include: The first deviation index is determined based on the first data and the first correlation model; the first data is the production change data of the target equipment collected. The target device is used for high-temperature vaporization, heating, and humidification of the target object; the first correlation model is used to characterize the correlation between the various changes in the first data, and the first correlation model includes a first sub-correlation model, a second sub-correlation model, and a third sub-correlation model; the first sub-correlation model is used to characterize the relationship between the output air volume and the input air volume of the target device under the configuration of the first preset component; the second sub-correlation model is used to characterize the correlation model between the resistance state of the dehumidification system of the target device and the frequency of the dehumidification fan and the opening of the dehumidification damper under the configuration of the second preset component; the third sub-correlation model is used to characterize the mapping relationship between water volume changes and air volume changes; The first deviation index is the transformation deviation of the first data based on the first correlation model; the first deviation index includes: air volume balance deviation rate, pressure balance deviation rate, tidal discharge and water supply matching deviation rate, energy consumption cost deviation rate and cylinder pressure balance deviation rate. A first result is determined based on the first deviation index; the first result is used to characterize the degree of failure of the wind power system of the target equipment. Based on the first result, a second result is determined according to the first deviation index and the second deviation index; the second deviation index is a threshold corresponding to the first deviation index that is preset; the second result is used to characterize the imbalance type of the target equipment and the imbalanced equipment.

2. The method according to claim 1, characterized in that, At least one method for determining the first association model includes: The second data is determined; the second data is the production change data of the target equipment at a historical moment collected under the first condition; the second data includes: air volume change data, water volume change data, temperature data and dehumidification data; the first condition includes: the discharge moisture content is within a first preset range, the discharge temperature is within a second preset range and the drum inlet hot air temperature is within a third preset range; The first sub-association model is determined based on the air volume change data; The second sub-association model is determined based on the tidal data; The third sub-association model is determined based on the water volume change data.

3. The method according to claim 1, characterized in that, The step of determining the first deviation index based on the first data and the first correlation model includes: The air volume balance deviation rate is determined based on the first data and the first sub-correlation model; The pressure balance deviation rate is determined based on the first data and the second sub-correlation model. Based on the first data and the third sub-correlation model, determine the tidal discharge and water supply matching deviation rate and the energy consumption cost deviation rate; The pressure balance deviation rate inside the cylinder is determined based on the first data.

4. The method according to claim 3, characterized in that, The step of determining the pressure balance deviation rate based on the first data and the second sub-correlation model includes: The third result is determined based on the presence of preset sensors in the target device; If the third result indicates the existence of a preset sensor, then the reference hygroscopic pressure value is determined based on the first data, and the pressure balance deviation rate is determined based on the reference hygroscopic pressure value. If the third result indicates that there is no preset sensor, then the pressure balance deviation rate is determined based on the fourth result and the first data; the fourth result is used to characterize whether the target device has a second preset component.

5. The method according to claim 1, characterized in that, Determining the first result based on the first deviation index includes: The first deviation index is normalized to obtain the third deviation index; The fifth result is obtained by weighted summation of the third deviation index; The fifth result is compared with the preset deviation index to determine the first result.

6. The method according to claim 1, characterized in that, The step of determining the second result based on the first result and according to the first deviation index and the second deviation index includes: The first sub-result is determined based on the air volume balance deviation rate and the preset air volume balance deviation rate; the first sub-result is used to characterize the degree and direction of air volume imbalance of the target equipment. The second sub-result is determined based on the pressure balance deviation rate and the preset pressure balance deviation rate; the second sub-result is used to characterize the degree of imbalance in the exhaust resistance or the degree of imbalance in the coordinated exhaust of the circulating air of the target equipment. The third sub-result is determined based on the tidal discharge and water supply matching deviation rate and the preset tidal discharge and water supply matching deviation rate; the third sub-result is used to characterize the degree and direction of the tidal discharge and water supply matching imbalance of the target equipment. The fourth sub-result is determined based on the energy consumption cost deviation rate and the preset energy consumption cost deviation rate; the fourth sub-result is used to characterize the degree of deviation of water flow rate and steam application amount. The fifth sub-result is determined based on the pressure balance deviation rate inside the cylinder and the preset pressure balance deviation rate inside the cylinder; the fifth sub-result is used to characterize the pressure state inside the cylinder.

7. The method according to claim 6, characterized in that, The determination of the fourth sub-result based on the energy consumption cost deviation rate and the preset energy consumption cost deviation rate includes: If the energy consumption cost deviation rate is greater than the preset energy consumption cost deviation rate, then a first ratio and a second ratio are obtained; the first ratio is used to characterize the degree of deviation in water addition; the second ratio is used to characterize the degree of deviation in steam application. If the first ratio is greater than the first preset ratio and the second ratio is greater than the second preset ratio, then the fourth sub-result is a double high-cost false balance; If the first ratio is greater than the first preset ratio, and the second ratio is less than or equal to the second preset ratio, then the fourth sub-result is increased water evaporation. If the first ratio is less than or equal to the first preset ratio, and the second ratio is greater than the second preset ratio, then the fourth sub-result is increased heat loss.

8. A device for diagnosing the wind balance state of a loosening and rehumidifying equipment, characterized in that, include: The first deviation index determination module is used to determine the first deviation index based on first data and a first correlation model; the first data is the collected production change data of the target equipment; the target equipment is used for high-temperature vaporization, heating and humidification of the target object; the first correlation model is used to characterize the correlation between the various changes in the first data, and the first correlation model includes a first sub-correlation model, a second sub-correlation model and a third sub-correlation model; the first sub-correlation model is used to characterize the relationship between the output air volume and the input air volume of the target equipment under the configuration of the first preset components; the second sub-correlation model is used to characterize the correlation model between the resistance state of the dehumidification system of the target equipment and the frequency and opening degree of the dehumidification fan and the dehumidification damper under the configuration of the second preset components; the third sub-correlation model is used to characterize the mapping relationship between water volume change and air volume change; The first deviation index is the transformation deviation of the first data based on the first correlation model; the first deviation index includes: air volume balance deviation rate, pressure balance deviation rate, tidal discharge and water supply matching deviation rate, energy consumption cost deviation rate and cylinder pressure balance deviation rate. The first result determination module is used to determine a first result based on the first deviation index; the first result is used to characterize the degree of failure of the wind power system of the target equipment. The second result determination module is used to determine a second result based on the first result according to the first deviation index and the second deviation index; the second deviation index is a threshold corresponding to the first deviation index that is preset; the second result is used to characterize the imbalance type of the target equipment and the imbalanced equipment.

9. An electronic device, characterized in that, The electronic device includes: At least one processor; and A memory communicatively connected to the at least one processor; wherein, The memory stores a computer program that can be executed by the at least one processor, the computer program being executed by the at least one processor to enable the at least one processor to perform the method for diagnosing the wind balance state of a loose, damping device as described in any one of claims 1-7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions that cause a processor to execute the method for diagnosing the wind balance state of a loose, re-humidified device as described in any one of claims 1-7.

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