A method and system for data acquisition of the whole process of halogen product processing

Abstract

The application discloses a kind of halogen products processing whole process data acquisition method and system, it is related to halogen products processing whole process data acquisition field, for solving in halogen products processing industry, optical sensor lens pollution causes the deviation of halogen soup concentration measurement difficult to find and correct, and then caused process adjustment error and product quality problem, comprising: obtaining the first concentration measurement value of halogen soup, with multiple process parameters related to the concentration change of halogen soup;Based on multiple process parameters, the second concentration calculation value of halogen soup is calculated;Compare first concentration measurement value and second concentration calculation value, identify the deviation between them, and cross validation is carried out in combination with multiple process parameters, to diagnose the progressive measurement deviation caused by optical sensor lens pollution;According to the diagnosis result, the amount of concentrated halogen material that needs to be added is output.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of halogenated product processing whole-process data acquisition, and particularly relates to a halogenated product processing whole-process data acquisition method and system. BACKGROUND

[0002] In the process of pursuing product flavor and quality stability, the halogenated product processing industry is faced with the hidden and key technical challenge of measurement deviation of halogen soup concentration caused by optical sensor lens pollution. Although the traditional existing data acquisition system can monitor various process parameters in real time, under the harsh environment inside the halogen pot body, a very thin fogging layer will gradually form on the surface of the optical sensor lens due to the condensation and adsorption of volatile substances in the halogen soup, or even a more stubborn denatured protein film.

[0003] This pollution causes the concentration data collected by the sensor to consistently show readings slightly lower than the actual value, and due to its gradualness and concealment, it is difficult to be discovered and effectively solved in time by conventional monitoring and cleaning means. As a result, process adjustments based on incorrect data, such as additional addition of concentrated halogen material, will result in actual halogen soup concentration much higher than expected, seriously affecting product flavor and quality, and even causing consumer complaints. SUMMARY

[0004] The present application provides a halogenated product processing whole-process data acquisition method for improving the display clarity and user understanding efficiency of a double-sided screen device in a professional field conversation.

[0005] In a first aspect, to solve the above technical problems, the present application provides a halogenated product processing whole-process data acquisition method, comprising: obtaining a first concentration measurement value of halogen soup, and a plurality of process parameters related to changes in halogen soup concentration;

[0006] Based on the plurality of process parameters, a second concentration calculation value of the halogen soup is calculated;

[0007] The first concentration measurement value and the second concentration calculation value are compared to identify the deviation between them, and cross-validation is performed in combination with the plurality of process parameters to diagnose the gradual measurement deviation caused by optical sensor lens pollution;

[0008] According to the diagnosis result, the amount of concentrated halogen material to be added is output.

[0009] Through this technical solution, the present application can effectively identify and diagnose the gradual measurement deviation caused by optical sensor lens pollution by cross-validation of the actual measurement value and the theoretical value calculated based on the process parameters, thereby avoiding process adjustment errors caused by incorrect data and ensuring the stability of halogenated product flavor and quality.

[0010] Further, based on the plurality of process parameters, a second concentration calculation value of the broth is calculated, including:

[0011] According to the concentration calculation formula and the plurality of process parameters, a second concentration calculation value of the broth is calculated; the plurality of process parameters include initial solute mass, total solute mass of all added halogen materials, initial broth volume, evaporation amount, and total volume of all added halogen materials.

[0012] Through the technical solution, the theoretical concentration of the broth can be more accurately calculated by introducing detailed process parameters, and a more reliable benchmark is provided for subsequent deviation diagnosis.

[0013] More specifically, in some embodiments, the concentration calculation formula is:

[0014] C_expected=(M_solute_initial+SUM(M_solute_add)) / (V_initial-E+SUM(V_add));

[0015] Wherein, C_expected is the second concentration calculation value, M_solute_initial is the initial solute mass, SUM(M_solute_add) is the total solute mass of all added halogen materials, V_initial is the initial broth volume, E is the evaporation amount, and SUM(V_add) is the total volume of all added halogen materials.

[0016] Through the technical solution, an explicit and operable concentration calculation model can be provided, ensuring the accuracy and repeatability of theoretical concentration calculation.

[0017] On this basis, the present application further proposes that the method further includes:

[0018] According to the evaporation amount formula, the evaporation amount is determined;

[0019] The evaporation amount formula is:

[0020] E=C1*P+C2*T+C3*t;

[0021] E is the evaporation amount, P is the heating power, T is the broth temperature, t is the halogen processing time, C1 is the heating power coefficient, C2 is the broth temperature coefficient, and C3 is the halogen processing time coefficient.

[0022] Through the technical solution, the evaporation amount of the broth can be more accurately estimated by considering key factors such as heating power, broth temperature, and halogen processing time, thereby improving the accuracy of theoretical concentration calculation.

[0023] On the basis described above, the present application further proposes that the method further includes:

[0024] In a case that the first concentration measurement value is less than the second concentration calculation value, a deviation between the two is greater than a deviation threshold, and the plurality of process parameters satisfy a budget condition, it is determined that the optical sensor lens is contaminated; the process parameters include a feeding amount, a brine liquid level, a brine temperature, and a brine color.

[0025] Through the technical solution, the optical sensor lens contamination can be more accurately identified by setting multiple judgment conditions, combining the measurement deviation and the comprehensive analysis of the process parameters, and avoiding misjudgment.

[0026] Preferably, the budget condition is:

[0027] The feeding amount is a preset head feeding amount, the brine liquid level is less than or equal to a preset liquid level, the brine temperature is in a preset temperature range, and the brine color is not faded.

[0028] Through the technical solution, the accuracy and reliability of the optical sensor lens contamination diagnosis can be further improved by specifying the budget condition.

[0029] In an embodiment, the method further includes:

[0030] Obtaining an average RGB value of a current brine image;

[0031] Comparing the average RGB value of the current brine image with an average RGB value under a historical normal concentration, if the current concentration reading is low, but the average RGB value of the current brine image does not significantly increase, it is determined that the brine color is not faded.

[0032] Through the technical solution, the image analysis technology can be introduced to objectively judge the brine color change, providing additional and independent verification basis for the sensor contamination diagnosis, and further enhancing the accuracy of the diagnosis.

[0033] As a technical improvement, according to the diagnosis result, the amount of concentrated brine to be added is output, including:

[0034] In a case that the diagnosis result indicates that the optical sensor lens is contaminated, the amount of concentrated brine to be added is output according to a brine formula.

[0035] Through the technical solution, after the sensor contamination is diagnosed, the amount of concentrated brine can be calculated according to the accurate theoretical concentration instead of the wrong measurement value, so as to avoid quality problems caused by wrong addition of brine.

[0036] As a further improvement, the brine formula is:

[0037] M_suggested=(C_target-C_expected)*V_current / C_concentrate;

[0038] wherein M_suggested is the amount of concentrate brine to be added, C_target is the target concentration, C_expected is the second concentration calculated value, V_current is the current volume of brine, and C_concentrate is the concentrate brine concentration.

[0039] By this technical solution, a clear brine addition calculation model can be provided to guide the production personnel to accurately adjust the brine and ensure that the brine concentration reaches the target value.

[0040] In a second aspect, the present application also discloses a brine product processing full-process data acquisition system, which comprises:

[0041] a first concentration acquisition module, configured to acquire a first concentration measurement value of the brine and a plurality of process parameters related to the change of the brine concentration;

[0042] a second concentration calculation module, configured to calculate a second concentration calculated value of the brine based on the plurality of process parameters;

[0043] a deviation diagnosis module, configured to compare the first concentration measurement value and the second concentration calculated value, identify the deviation therebetween, and cross-verify in combination with the plurality of process parameters to diagnose the gradual measurement deviation caused by the contamination of the optical sensor lens;

[0044] a result output module, configured to output the amount of concentrate brine to be added according to the diagnosis result.

[0045] By this technical solution, the present application can provide a system integrating data acquisition, theoretical calculation, deviation diagnosis and result output, realize intelligent monitoring and abnormal diagnosis of the brine concentration in the brine product processing full process, effectively solve the measurement deviation problem caused by sensor contamination, and guarantee product quality.

[0046] Advantages

[0047] The present application provides a brine product processing full-process data acquisition method, which acquires a first concentration measurement value of the brine and a plurality of process parameters related to the change of the brine concentration, and calculates a second concentration calculated value of the brine based on the process parameters. The method further compares the deviation between the first concentration measurement value and the second concentration calculated value, and cross- verifies in combination with the plurality of process parameters, to effectively diagnose the gradual measurement deviation caused by the contamination of the optical sensor lens. Finally, the amount of concentrate brine to be added is output according to the diagnosis result.

[0048] The method effectively solves the problem that in the prior art, the measurement deviation caused by the pollution of the optical sensor lens is difficult to find and correct. In the prior art, due to the complex halogen environment, the sensor lens is easily polluted, resulting in a continuous low measurement value, and the deviation is progressive and difficult to identify through a conventional alarm mechanism. Production personnel make process adjustments based on incorrect data, which increases the concentration of halogen materials, resulting in heavy product flavor and salty taste, seriously affecting product quality and consumer experience.

[0049] The present application can accurately identify the measurement deviation caused by the pollution of the sensor lens in time by introducing theoretical concentration calculation and multi-parameter cross-validation mechanism, avoiding blind trust in incorrect measurement data. When the sensor pollution is diagnosed, the system can calculate and output the correct concentration of halogen material addition amount according to the accurate theoretical concentration instead of the polluted measurement value, thereby avoiding the quality problems of halogen products caused by incorrect halogen material addition. The method not only improves the accuracy of halogen concentration control, ensures the stability of product flavor and quality, but also improves the reliability and intelligent level of the data acquisition system, and provides strong support for the fine management of halogen product processing industry. BRIEF DESCRIPTION OF DRAWINGS

[0050] Figure 1 is a halogen product processing full-process data acquisition method process schematic diagram provided by an embodiment of the present application;

[0051] Figure 2 is another halogen product processing full-process data acquisition method process schematic diagram provided by an embodiment of the present application;

[0052] Figure 3 is a halogen product processing full-process data acquisition system structure schematic diagram provided by an embodiment of the present application. DETAILED DESCRIPTION

[0053] The technical solutions in the present application will be described in detail below with reference to the accompanying drawings in the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all embodiments. The components of the present application described and shown in the drawings herein can be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present application provided in the drawings is not intended to limit the scope of the claimed application, but only represents selected embodiments of the present application. Based on the embodiments of the present application, all other embodiments obtained by those skilled in the art without creative labor are within the scope of protection of the present application.

[0054] It should be noted that similar reference numerals and letters refer to like items throughout the accompanying drawings, and once an item is defined in one drawing, it is not necessary to further define and explain it in the subsequent drawings. Meanwhile, in the description of the present application, the terms "first", "second", etc. are only used to distinguish the description, and cannot be understood as indicating or implying relative importance.

[0055] In the process of pursuing product flavor and quality stability, the braising product processing industry faces the hidden but critical technical challenge of measurement deviation of braising soup concentration caused by pollution of optical sensor lens. Although the traditional existing data acquisition system can monitor various process parameters in real time, in the harsh environment inside the braising pot, a very thin fogging layer will gradually form on the surface of the optical sensor lens due to the condensation and adsorption of volatile substances in the braising soup, or even a more stubborn denatured protein film. This pollution causes the concentration data collected by the sensor to consistently show readings slightly lower than the actual value, and due to its gradualness and concealment, it is difficult to be discovered and effectively solved in time by conventional monitoring and cleaning means. As a result, process adjustments based on incorrect data, such as additional addition of concentrated braising material, will cause the actual braising soup concentration to be much higher than expected, seriously affecting product flavor and quality, and even causing consumer complaints.

[0056] To this end, the present application proposes a braised product processing full-process data acquisition method, which aims to effectively identify and solve the above problems through innovative data processing and diagnosis mechanisms. The method comprises: obtaining a first concentration measurement value of the braising soup and a plurality of process parameters related to the change of the braising soup concentration; based on the plurality of process parameters, calculating a second concentration calculation value of the braising soup; comparing the first concentration measurement value and the second concentration calculation value to identify the deviation therebetween, and cross- verifying in combination with the plurality of process parameters to diagnose the gradual measurement deviation caused by pollution of the optical sensor lens; and outputting the amount of concentrated braising material that needs to be added according to the diagnosis result. By comparing the second concentration calculation value with the first concentration measurement value and cross-verifying in combination with multi-dimensional process parameters, the present application can effectively diagnose the hidden problem of pollution of the optical sensor lens, thereby avoiding process misjudgment and product quality problems caused by incorrect data.

[0057] In order to better understand the technical solutions proposed in the present application, it is necessary to explain some key terms involved therein.

[0058] The first concentration measurement value of the braising soup refers to the braising soup concentration data directly obtained by a physical measurement device such as an optical sensor.

[0059] The plurality of process parameters related to the change of the braising soup concentration refers to controllable or measurable factors in various production processes that affect the change of the braising soup concentration, such as initial feed amount, braising time, heating power, braising soup liquid level, and addition of braising material.

[0060] The second concentration calculated value of the brine refers to a theoretical concentration value calculated based on known process parameters and a preset physicochemical model.

[0061] The deviation refers to the difference between the first concentration measured value and the second concentration calculated value. The optical sensor lens pollution refers to the phenomenon that the optical performance of the sensor lens is degraded due to the deposition of substances in the brine on the surface of the sensor lens, thereby affecting the measurement accuracy.

[0062] The progressive measurement deviation refers to the measurement error caused by such pollution, which is slowly accumulated and increased over time. The amount of concentrated brine to be added refers to the amount of concentrated brine recommended to be added in order to make the brine concentration reach the target value according to the diagnosis result.

[0063] The method is usually implemented in a brine product processing full-process data acquisition system, which includes various sensors, data processing units, control units and human-computer interaction interfaces, etc.

[0064] The method for acquiring brine product processing full-process data provided by the embodiments of the present application will be described in detail and explained below through the following specific embodiments.

[0065] With reference to Figure 1 , the present application provides a method for acquiring brine product processing full-process data, which comprises the following steps:

[0066] S1, obtaining a first concentration measured value of the brine and a plurality of process parameters related to the concentration change of the brine.

[0067] The first concentration measured value can be obtained in real time by deploying an optical concentration sensor in the brine pot, which converts the optical signal into an electrical signal, and then converts it into a digital signal through an analog-to-digital converter and transmits it to the data acquisition system.

[0068] The plurality of process parameters related to the concentration change of the brine, such as brining time, heating power, brine temperature, brine level, and feed amount, can be collected in real time by their respective corresponding sensors (such as timers, power meters, temperature sensors, level sensors, weighing sensors, etc.). These sensors transmit data to the central data processing unit for subsequent calculation and analysis.

[0069] S2, based on the plurality of process parameters, calculating a second concentration calculated value of the brine.

[0070] As a possible implementation, the second concentration calculated value of the brine can be calculated according to a concentration calculation formula and the plurality of process parameters; the plurality of process parameters include the initial solute mass, the total solute mass of all added brine, the initial brine volume, the evaporation amount, and the total volume of all added brine.

[0071] The concentration calculation formula is:

[0072] C_expected = (M_solute_initial + SUM(M_solute_add)) / (V_initial - E + SUM(V_add));

[0073] Where C_expected is the second concentration calculation value, M_solute_initial is the initial solute mass, SUM(M_solute_add) is the total solute mass of all added brine, V_initial is the initial brine volume, E is the evaporation amount, and SUM(V_add) is the total volume of all added brine.

[0074] Specifically, C_expected represents the expected or theoretical brine concentration after considering the dynamic changes of solutes and solvents in the brine. M_solute_initial refers to the total mass of solutes present in the brine at the beginning of the brining process. SUM(M_solute_add) refers to the total mass of solutes contained in all additional brine added during the brining process. V_initial refers to the initial volume of the brine at the beginning of the brining process. E refers to the amount of water evaporation from the brine due to heating and other factors during the brining process. SUM(V_add) refers to the total volume of all additional brine added during the brining process. Through this formula, the ratio of solutes to solvents in the brine can be accurately quantified, resulting in a theoretical brine concentration.

[0075] By introducing the above explicit concentration calculation formula, the second concentration calculation value of the brine can be accurately mathematically modeled and calculated based on multiple process parameters such as the initial solute mass, the total solute mass of all added brine, the initial brine volume, the evaporation amount, and the total volume of all added brine. This calculation method based on physical and chemical principles ensures that the calculated second concentration calculation value can truly reflect the actual concentration of the brine during the dynamic change process, providing a reliable benchmark for subsequent comparison of the first concentration measurement value and the second concentration calculation value, identification of deviations, and diagnosis of optical sensor lens contamination.

[0076] Through the above technical solution, an explicit and quantifiable concentration calculation model is provided, enabling the second concentration calculation value of the brine to be accurately calculated. This greatly improves the transparency and credibility of the concentration calculation process, avoiding errors caused by ambiguous calculations or empirical judgments, thereby providing a solid data foundation for subsequent deviation identification and sensor contamination diagnosis, effectively improving the accuracy and reliability of the entire data acquisition method.

[0077] S3, compare the first concentration measurement value with the second concentration calculation value, identify the deviation between them, and cross-verify with multiple process parameters to diagnose the gradual measurement deviation caused by the optical sensor lens pollution.

[0078] The visual presentation parameters include any one or more of the following: character spacing, halo parameter, and font style.

[0079] As a possible implementation, the optical sensor lens pollution can be determined in the case that the first concentration measurement value is less than the second concentration calculation value, the deviation between them is greater than the deviation threshold, and the multiple process parameters meet the budget condition. The process parameters include the feeding amount, the brine liquid level, the brine temperature, and the brine color.

[0080] It should be noted that the budget condition is that the feeding amount is the preset first feeding amount, the brine liquid level is less than or equal to the preset liquid level, the brine temperature is in the preset temperature range, and the brine color is not faded.

[0081] The fact that the brine color is not faded can be determined by obtaining the average RGB value of the current brine image and comparing it with the average RGB value under the historical normal concentration. If the current concentration reading is low, but the average RGB value of the current brine image has not increased significantly, it indicates that the brine color has not faded.

[0082] In some preferred embodiments, the following is described by a specific example. Assume that the brine concentration monitoring system is continuously running on a certain brine product production line. At a certain time, the optical sensor measures the first concentration measurement value of 10 Brix, while the second concentration calculation value calculated according to the initial solute mass of the brine, the total solute mass of the added brine, the initial brine volume, the evaporation amount, and the total volume of the added brine is 12 Brix. At this time, the deviation between them is 2 Brix, which is greater than the preset deviation threshold of 1 Brix.

[0083] Further, the system checks the multiple process parameters:

[0084] 1. Feeding amount: the feeding amount of the current batch is completely consistent with the preset first feeding amount.

[0085] 2. Brine liquid level: the brine liquid level sensor shows that the current liquid level is at the lower limit of the preset liquid level range, but is not lower than the preset liquid level.

[0086] 3. Brine temperature: the brine temperature sensor shows that the current brine temperature is stable within the preset temperature range (e.g. 95℃±2℃).

[0087] 4. Brine color: The average RGB value of the current brine image obtained by the image recognition system is compared with the average RGB value under the historical normal concentration. It is found that although the concentration reading is low, the average RGB value does not increase significantly, indicating that the brine color has not lightened.

[0088] Since all the above process parameters meet the budget conditions, i.e. the feeding amount is the preset initial feeding amount, the brine level is less than or equal to the preset level, the brine temperature is within the preset temperature range, and the brine color has not lightened, the system determines that there is contamination on the optical sensor lens. Subsequently, the system can trigger an alarm and recommend cleaning and maintenance of the optical sensor.

[0089] Through the above technical solution, the present application can more accurately identify the gradual measurement deviation caused by contamination of the optical sensor lens. Specifically, by setting clear budget conditions, measurement deviations caused by normal process fluctuations, insufficient feeding, abnormal liquid level, or reduced actual brine concentration are excluded, thereby avoiding misdiagnosis. This makes the system more reliable and robust when judging sensor contamination, reduces unnecessary sensor cleaning or brine addition operations, and improves the automation and intelligent level of the brine product production process.

[0090] S4, output the amount of concentrated brine to be added according to the diagnosis result.

[0091] As a possible implementation, in the case where the diagnosis result indicates contamination of the optical sensor lens, the amount of concentrated brine to be added can be output according to the brine formula.

[0092] The brine formula is:

[0093] M_suggested=(C_target-C_expected)*V_current / C_concentrate;

[0094] where M_suggested is the amount of concentrated brine to be added, C_target is the target concentration, C_expected is the second concentration calculation value, V_current is the current brine volume, and C_concentrate is the concentration of concentrated brine.

[0095] Specifically, M_suggested represents the specific mass or volume of concentrated brine required to be added in order to adjust the brine concentration to the target concentration. C_target refers to the ideal concentration value that the brine should reach under normal production conditions, which is usually preset according to the type of brined product and process requirements. C_expected is the theoretical concentration of the brine calculated based on multiple process parameters, which is used to reflect the true concentration of the brine without the influence of sensor contamination. V_current represents the actual volume of the brine during the brining process, which dynamically changes due to evaporation and the addition of materials. C_concentrate refers to the concentration of the concentrated brine used itself, which is a key factor in calculating the required addition amount.

[0096] The brined product processing full-process data acquisition method proposed in the present application can effectively solve the problem of gradual measurement deviation caused by optical sensor lens contamination, which is difficult to find and solve in the prior art, by comparing the second concentration calculation value with the first concentration measurement value and cross-verifying with multiple dimensional process parameters.

[0097] Specifically, in the traditional existing brined product processing process, the concentration measurement deviation caused by optical sensor lens contamination is a hidden and gradual problem. Since the deviation is not large in the early stage and may be masked by the noise filtering mechanism or conventional fluctuation range within the system, it is difficult for production personnel and process engineers to find it through conventional data monitoring. Even if regular cleaning is performed, since the brine composition is complex, special dirt such as denatured protein film is difficult to remove by conventional cleaning agents, and instead becomes a new attachment substrate, so that the dirt layer gradually thickens and the measurement deviation continuously increases. This long-term existing systematic error eventually leads technicians to make process adjustments based on incorrect data, such as additional addition of concentrated brine, so that the actual brine concentration is much higher than expected, seriously affecting the flavor and quality of the product.

[0098] The present application breaks through the above limitations in the following ways: First, by obtaining the first concentration measurement of the brine and a plurality of process parameters related to the change of brine concentration, a basis is provided for subsequent data analysis. Second, based on these process parameters, the second concentration calculation value of the brine is calculated. This calculated value represents the theoretical concentration that should be achieved under ideal conditions according to known inputs and losses. Third, by comparing the first concentration measurement with the second concentration calculation, the deviation between the two is identified. If the first concentration measurement consistently falls below the second concentration calculation, and the deviation gradually increases, it is a preliminary indication that there may be a measurement problem. Most importantly, the present application further cross- verifies a plurality of process parameters to diagnose the gradual measurement deviation caused by optical sensor lens contamination. For example, when a low concentration measurement is found, the system will simultaneously check the amount of raw material, the brine level, the brine temperature and the brine color, etc. If these parameters are all normal, it is ruled out that the brine is actually diluted, so it can be more accurately judged that the sensor lens contamination causes the measurement deviation. Finally, according to the diagnosis result, the amount of concentrated brine to be added is output. This means that when the system confirms that the sensor contamination causes the measurement value to be low, it will not blindly suggest increasing the brine, but will output a corrected and more accurate amount of brine addition according to the actual situation, or prompt that the sensor needs to be maintained.

[0099] Compared with the closest prior art, the present application has the advantage that it can self-diagnose the health status of the sensor itself and perform deep abnormal pattern recognition on the data. The existing system usually only relies on the preset alarm threshold and lacks effective recognition ability for gradual and hidden sensor failure problems. However, by comparing the theoretical calculation value with the actual measurement value and cross-verifying a plurality of process parameters, the present application can more accurately find and diagnose the specific type of measurement deviation caused by optical sensor lens contamination. This diagnostic capability enables production personnel to timely maintain the sensor, avoids process misjudgment and product quality problems caused by false data, and thus ensures the stability of the flavor and quality of the brined products and improves the reliability and practicality of the data acquisition system.

[0100] Specifically, in order to accurately calculate the second concentration calculation value of the brine, the dynamic changes of solute and solvent (mainly water) in the brine during processing need to be considered. The concentration calculation formula refers to a mathematical expression for quantifying the ratio of solute to solvent in the brine, and its purpose is to provide a theoretical concentration expectation value by considering various physical and chemical parameters of the brine. A plurality of process parameters are key factors affecting the change of brine concentration, and their specific meanings and functions are as follows:

[0101] The initial solute mass refers to the total mass of the dissolved solute in the brine at the beginning of the brining process, which is the basis for calculating the total solute mass of the brine.

[0102] The total mass of solutes in all added halogen materials refers to the total mass of solutes contained in all additional halogen materials during the halogenation process, which directly increases the overall concentration of the halogen soup.

[0103] The initial halogen soup volume refers to the total volume of the halogen soup at the beginning of the halogenation process, which is the basis for calculating the initial concentration and subsequent volume changes.

[0104] The evaporation amount refers to the amount of water evaporation due to heating and other factors during the halogenation process. The evaporation of water reduces the total volume of the halogen soup, thereby relatively increasing the concentration of the halogen soup.

[0105] The total volume of all added halogen materials refers to the total volume of all additional halogen materials during the halogenation process, which increases the total volume of the halogen soup and thus affects the concentration of the halogen soup.

[0106] By accurately obtaining and utilizing these process parameters, a mathematical model reflecting the actual concentration changes of the halogen soup can be constructed, thereby calculating a more accurate second concentration calculation value.

[0107] In one possible design, as shown in FIG. 1, to determine the evaporation amount, the present application can further include the following steps: Figure 2

[0108] S101, determining the evaporation amount according to the evaporation amount formula.

[0109] The evaporation amount formula is:

[0110] E = C1*P + C2*T + C3*t;

[0111] E is the evaporation amount, P is the heating power, T is the halogen soup temperature, t is the halogenation time, C1 is the heating power coefficient, C2 is the halogen soup temperature coefficient, and C3 is the halogenation time coefficient.

[0112] Specifically, the evaporation amount E refers to the total amount of water loss due to heating during the halogenation process. The heating power P refers to the power of the equipment used to heat the halogen soup, which directly affects the heating intensity and evaporation rate of the halogen soup. The halogen soup temperature T refers to the actual temperature of the halogen soup during the halogenation process. The higher the temperature, the greater the kinetic energy of water molecules, and the faster the evaporation. The halogenation time t refers to the duration of the halogen soup being in a heated state or undergoing halogenation operation. The longer the time, the greater the cumulative evaporation amount. C1, C2, and C3 are the heating power coefficient, the halogen soup temperature coefficient, and the halogenation time coefficient, respectively. These coefficients are determined based on actual production experience, experimental data, or physical models, and are used to quantify the specific influence of P, T, and t on the evaporation amount E.

[0113] ​The scheme of the present application converts the calculation of the evaporation amount E from a parameter that may require empirical judgment or simple estimation into an accurate calculated value based on measurable process parameters by introducing an evaporation amount formula. The evaporation amount E is one of the key factors affecting the actual volume and solute concentration of the brine. During the processing of the brined product, the brine continuously evaporates water, resulting in a decrease in the volume of the brine and an increase in the concentration of the brine. If the estimation of the evaporation amount E is inaccurate, it will directly affect the accuracy of the second concentration calculated value C_expected. By incorporating the three process parameters related to evaporation, namely the heating power P, the brine temperature T, and the brining time t, into the calculation model and combining the corresponding coefficients C1, C2, and C3, the evaporation loss of the brine can be quantified more scientifically and accurately. Thus, when calculating the second concentration of the brine, the actual state of the brine can be more accurately reflected.

[0114] Through the above technical scheme, the determination of the evaporation amount E is no longer dependent on rough empirical judgment or estimation of a single parameter, but is calculated by a formula that comprehensively considers multiple key process parameters. This significantly improves the accuracy and reliability of the calculation of the evaporation amount E, and further enhances the precision of the second concentration calculated value C_expected of the brine. A more accurate concentration calculated value helps to more accurately identify the measurement deviation caused by the contamination of the optical sensor lens, and provides a more reliable basis for the subsequent addition of the concentrated brine amount M_suggested, thereby optimizing the quality control and production efficiency of the brined product processing process.

[0115] In some embodiments of the present application described above, it is proposed to diagnose the gradual measurement deviation caused by the contamination of the optical sensor lens by comparing the first concentration measured value with the second concentration calculated value and cross-verifying with multiple process parameters. However, in actual application, only general cross-verification may not be sufficient to accurately distinguish between the contamination of the optical sensor lens and other factors that may cause concentration deviation, such as minor fluctuations in the actual feed amount or the evaporation amount. This may lead to misdiagnosis or low diagnostic efficiency, thereby affecting the stability and accuracy of the brined product production.

[0116] In one possible design, in order to determine that the color of the brine has not faded, the following steps are included:

[0117] S201, obtaining the average RGB value of the current brine image.

[0118] Specifically, obtaining the average RGB value of the current brine image means that the digital image of the current brine is obtained by real-time shooting of the brine through an image acquisition device (such as an industrial camera). Subsequently, the digital image is analyzed using image processing technology to calculate the average values of the red (R), green (G), and blue (B) three primary color components of all pixel points in the image, thereby obtaining the average RGB value representing the color of the current brine.

[0119] S202, compare the average RGB value of the current broth image with the average RGB value under the historical normal concentration, if the current concentration reading is low, but the average RGB value of the current broth image does not significantly increase, it is determined that the broth color is not lightened.

[0120] The average RGB value under the historical normal concentration can be understood as the average RGB value obtained by collecting and calculating in the same way when the broth is in a known normal concentration state. These historical data are usually pre-stored in the database of the system as a comparison reference.

[0121] Comparing the average RGB value of the current broth image with the average RGB value under the historical normal concentration aims to quantify the difference between the current broth color and the normal state. If the current concentration reading is low, it means that the first concentration measurement value is less than the second concentration calculation value, and the deviation between them is greater than the preset deviation threshold. In this case, if the average RGB value of the broth does not significantly increase (such as the RGB value increases by less than 10%), it indicates that the color of the broth has not been significantly lightened. The broth color is not lightened, which means that the actual concentration of the solute in the broth may not have been significantly reduced, thereby excluding the possibility that the actual concentration is reduced to cause the sensor reading to be low.

[0122] The scheme of the present application provides an objective and quantitative way to evaluate the color state of the broth by introducing the average RGB value of the broth image as a judgment basis. When the optical sensor lens is contaminated, its measurement value of the broth concentration may gradually decrease, resulting in that the first concentration measurement value is lower than the second concentration calculation value. However, the actual concentration of the broth may not have been truly reduced, and its color should remain relatively stable. By obtaining the average RGB value of the current broth image and comparing it with the average RGB value under the historical normal concentration, it can be effectively verified whether the broth color is consistent with the change trend of the concentration reading. If the concentration reading is low, but the broth color is not significantly lightened (i.e. the average RGB value is not significantly increased), it strongly supports the diagnosis of optical sensor lens contamination, because it excludes the possibility of reducing the actual concentration.

[0123] Through the above technical scheme, a more accurate and reliable means can be provided to judge whether the broth color is lightened, thereby avoiding the misjudgment that may be caused by relying only on the concentration measurement value. This method significantly improves the accuracy of diagnosing optical sensor lens contamination by cross-verification of visual information and concentration data, so that the system can more effectively identify sensor failure and ensure more accurate data acquisition during the processing of the halogenated product, providing a solid foundation for subsequent halogen material addition decisions.

[0124] As shown in Figure 3 The present application also provides a halogenated product processing full-process data acquisition system. The system comprises:

[0125] a first concentration obtaining module, configured to obtain a first concentration measurement value of the brine and a plurality of process parameters related to a change in the concentration of the brine;

[0126] a second concentration calculating module, configured to calculate a second concentration calculation value of the brine based on the plurality of process parameters;

[0127] a deviation diagnosing module, configured to compare the first concentration measurement value and the second concentration calculation value, identify a deviation therebetween, and cross-verify the deviation in combination with the plurality of process parameters to diagnose a progressive measurement deviation caused by contamination of an optical sensor lens;

[0128] a result outputting module, configured to output an amount of concentrated brine to be added according to a diagnosis result.

[0129] The embodiments of the present application further provide a computer readable storage medium. All or part of the processes of the method embodiments described above can be instructed by a computer program to relevant hardware to complete, and the program can be stored in the computer readable storage medium. When the program is executed, the processes of the above-mentioned method embodiments can be included. The computer readable storage medium can be an internal storage unit of the task execution device (including the data sending end and / or the data receiving end) of any of the preceding embodiments, such as a hard disk or a memory of the task execution device. The computer readable storage medium can also be an external storage device of the terminal device, such as a plug-in hard disk, a smart media card (SMC), a secure digital (SD) card, a flash card, etc. Further, the computer readable storage medium can include both the internal storage unit of the task execution device and the external storage device. The computer readable storage medium is used to store the computer program and other programs and data required by the task execution device. The computer readable storage medium can also be used to temporarily store data that has been output or will be output.

[0130] In addition, each functional unit in each embodiment of the present application can be integrated in one processing unit, or each unit can exist physically, or two or more units can be integrated in one unit. The integrated unit can be realized in the form of hardware or in the form of a software functional unit.

[0131] The integrated unit, if implemented in the form of a software function unit and sold or used as an independent product, can be stored in a readable storage medium. Based on such understanding, the technical solutions of the embodiments of the present application essentially or say the part that contributes to the prior art or the whole or part of the technical solutions can be embodied in the form of a software product. The software product is stored in a storage medium, includes a plurality of instructions to make a device (which can be a single-chip microcomputer, a chip, etc.) or a processor execute all or part of the steps of the embodiments of the present application. The aforementioned storage medium includes: a U disk, a mobile hard disk, a ROM, a RAM, a magnetic disk or an optical disk, and various storage program codes.

[0132] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any change or replacement within the technical scope disclosed in the present application should be covered within the protection scope of the present application.

Claims

1. A method for collecting data throughout the entire process of braised food processing, characterized in that, include: Obtain the first concentration measurement value of the brine and multiple process parameters related to the change in brine concentration; Based on the aforementioned multiple process parameters, the second concentration value of the brine is calculated. The first concentration measurement value is compared with the second concentration calculation value to identify the deviation between the two, and cross-validation is performed in combination with the multiple process parameters to diagnose the progressive measurement deviation caused by optical sensor lens contamination. Based on the diagnostic results, output the amount of concentrated brine that needs to be added; The step of comparing the first concentration measurement value with the second concentration calculation value, identifying the deviation between the two, and performing cross-validation in conjunction with the multiple process parameters to diagnose progressive measurement deviations caused by optical sensor lens contamination includes: If the measured value of the first concentration is less than the calculated value of the second concentration, the deviation between the two is greater than the deviation threshold, and the multiple process parameters meet the budget conditions, the optical sensor lens is determined to be contaminated; the process parameters include the amount of feed, the level of the brine, the temperature of the brine, and the color of the brine.

2. The method for data acquisition throughout the entire process of braised food processing according to claim 1, characterized in that, Based on the aforementioned multiple process parameters, the second concentration value of the brine is calculated, including: Based on the concentration calculation formula and the multiple process parameters, the second concentration value of the brine is calculated; the multiple process parameters include the initial solute mass, the total solute mass of all added brine ingredients, the initial brine volume, the evaporation rate, and the total volume of all added brine ingredients.

3. The method for data acquisition throughout the entire process of braised food processing according to claim 2, characterized in that, The concentration calculation formula is as follows: C_expected=(M_solute_initial+SUM(M_solute_add)) / (V_initial-E+SUM(V_add)); Where C_expected is the second calculated concentration, M_solute_initial is the initial solute mass, SUM(M_solute_add) is the total solute mass of all added brine ingredients, V_initial is the initial brine volume, E is the evaporation rate, and SUM(V_add) is the total volume of all added brine ingredients.

4. The method for data acquisition throughout the entire process of braised food processing according to claim 3, characterized in that, The method further includes: The evaporation rate is determined according to the evaporation rate formula; The formula for the evaporation rate is: E = C1*P + C2*T + C3*t; E is the evaporation rate, P is the heating power, T is the brine temperature, t is the braising time, C1 is the heating power coefficient, C2 is the brine temperature coefficient, and C3 is the braising time coefficient.

5. The method for data acquisition throughout the entire process of braised food processing according to claim 1, characterized in that, The budget conditions are as follows: The amount of material fed is a preset amount, the level of the brine is less than or equal to a preset level, the temperature of the brine is within a preset temperature range, and the color of the brine has not faded.

6. The method for data acquisition throughout the entire process of braised food processing according to claim 5, characterized in that, The method further includes: Obtain the average RGB value of the current brine image; By comparing the average RGB value of the current braising liquid image with the average RGB value under historical normal concentration, if the current concentration reading is low, but the average RGB value of the current braising liquid image has not increased significantly, then it is determined that the color of the braising liquid has not faded.

7. The method for data acquisition throughout the entire process of braised food processing according to claim 1, characterized in that, The step of outputting the required amount of concentrated brine to be added based on the diagnostic results includes: If the diagnostic results indicate that the optical sensor lens is contaminated, the amount of concentrated brine to be added is output according to the brine formula.

8. The method for data acquisition throughout the entire process of braised food processing according to claim 7, characterized in that, The formula for the brine is: M_suggested=(C_target-C_expected)*V_current / C_concentrate; Where M_suggested is the amount of concentrated brine to be added, C_target is the target concentration, C_expected is the calculated value of the second concentration, V_current is the current volume of the brine, and C_concentrate is the concentration of the concentrated brine.

9. A data acquisition system for the entire process of braised food processing, characterized in that, The system includes: The first concentration acquisition module is used to acquire the first concentration measurement value of the brine and multiple process parameters related to the change in brine concentration. The second concentration calculation module is used to calculate the second concentration value of the brine based on the multiple process parameters. The deviation diagnosis module is used to compare the first concentration measurement value with the second concentration calculation value, identify the deviation between the two, and perform cross-validation in combination with the multiple process parameters to diagnose the progressive measurement deviation caused by contamination of the optical sensor lens. It is also used to determine the contamination of the optical sensor lens when the first concentration measurement value is less than the second concentration calculation value, the deviation between the two is greater than the deviation threshold, and the plurality of process parameters meet the budget conditions; the process parameters include feed amount, brine level, brine temperature, and brine color; The results output module is used to output the amount of concentrated brine that needs to be added based on the diagnostic results.

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