Chicken cake production process

Abstract

The invention relates to the technical field of food production, in particular to a production process of a small chicken cake. The method comprises the steps of fermentation degree control, cake crust crispness optimization formula generation, cake stuffing viscosity adjustment parameter determination, texture synergy parameter calculation, production efficiency improvement scheme making, stable production parameter optimization, quality stability evaluation, long-term production optimization strategy generation and the like. The fermentation process of the fermented pastry flour can be optimized, and the texture characteristics of the pastry crust and the pastry stuffing can be coordinated, so that the collaborative forming of the crisp pastry crust and the soft pastry stuffing can be realized.

Description

Technical Field

[0001] The invention relates to the technical field of food production, and in particular to a chicken biscuit production process. Background Art

[0002] As an important branch of food processing, fermented pastry making occupies a key position in the global food industry because it can give pastries a unique flavor and taste. Traditional fermented pastry making processes improve the quality of dough through microbial fermentation, meeting consumers' dual needs for health and deliciousness. However, existing methods have significant limitations in process optimization and quality control. Many traditional processes rely on manual experience and lack standardization, resulting in unstable product quality and low production efficiency. In addition, in modern industrial production, a single fermentation process makes it difficult to take into account both the crispiness of the crust and the softness of the filling, which limits the quality consistency of large-scale production.

[0003] In the production of fermented pastries, the core challenge lies in the coordinated optimization of the crust and filling. The degree of fermentation of fermented pastry flour directly affects the texture and flavor of the crust, but excessive or insufficient fermentation will result in the crust being insufficiently tough or too loose, making it difficult to effectively cover the filling. In the production of the crust, the mixing ratio and process conditions of pastry flour, maltose and edible oil need to be precisely controlled, otherwise the crispness and forming effect of the crust will be affected due to uneven oil distribution or sugar crystallization. The processing of glutinous rice flour for the filling requires a balance between viscosity and softness. Excessive viscosity will make the filling too firm and affect the taste, while insufficient softness will make it difficult to form a good texture contrast with the crust. These factors are closely related. The control of the degree of fermentation determines the feasibility of the mixing process, and the stability of the mixing process further affects the texture matching of the filling and the crust, ultimately determining the overall quality of the pastry.

[0004] Therefore, how to optimize the fermentation process of fermented pastry flour and coordinate the texture characteristics of the crust and filling to achieve the coordinated forming of crispy crust and soft filling has become a key issue in improving the quality and production efficiency of fermented pastries. Summary of the Invention

[0005] The purpose of the present invention is to provide a chicken biscuit production process in view of the above-mentioned deficiencies in the prior art.

[0006] The purpose of the present invention is achieved through the following technical solution: A chicken biscuit production process comprises the following steps: S1. Obtain fermentation degree data of fermented pastry flour under different fermentation time, temperature and humidity conditions, monitor carbon dioxide release and pH changes during the fermentation process through sensors, analyze the relationship between fermentation degree and crust texture parameters, and determine the fermentation degree control threshold; S2. According to the fermentation degree control threshold, an initial mixing ratio of pastry flour, maltose, and edible oil is obtained, and by simulating the oil distribution and sugar crystallization state during the crust forming process, crust texture parameters are calculated to obtain an optimized crust crispness formula; S3. Based on the crust crispness optimization formula, obtain the viscosity and softness data of glutinous rice flour at different moisture contents and processing temperatures, establish a functional relationship between viscosity and softness, and determine the viscosity adjustment parameters of the filling; S4. If the viscosity adjustment parameter of the filling meets the preset softness threshold, the mechanical properties of the filling and the crust during the forming process are measured, and the texture synergy parameters of the crust and the filling are calculated to obtain the synergistic forming conditions. S5. Based on the texture synergy parameters, obtain operating data of the fermentation, mixing, and molding equipment on the production line, analyze the correlation between the equipment parameters and product quality, and determine a plan to improve production efficiency; S6. If the equipment parameter deviation in the production efficiency improvement plan exceeds the preset threshold, adjust the fermentation temperature, mixing speed and molding pressure to predict quality stability and obtain stable production parameters; S7. Based on the stable production parameters, obtain data on the crispness of the crust and the softness of the filling during batch production, analyze the quality fluctuation trend, and determine whether the quality stability meets the preset standards; S8. If the quality stability meets the preset standards, analyze the historical data of fermentation degree, texture parameters and production efficiency, optimize the process flow, and obtain a long-term production optimization strategy.

[0007] The present invention is further configured as follows: the fermentation degree control threshold includes crust toughness evaluation, looseness detection and fermentation uniformity analysis; the crust crispness optimization formula includes oil distribution uniformity, sugar crystal particle size and dough extensibility optimization; the filling viscosity adjustment parameters include filling viscosity range and softness matching; the texture synergy parameters are specifically the mechanical matching and molding stability between the crust and filling; the production efficiency improvement plan includes a fermentation time shortening plan, mixing process optimization configuration and molding equipment adjustment strategy; the stable production parameters include fermentation temperature and humidity curve, mixing speed dynamic adjustment and molding pressure distribution; the quality fluctuation trend analysis includes crust crispness consistency evaluation and filling softness stability detection; the long-term production optimization strategy includes fermentation process improvement plan, mixing process standardization design and molding equipment intelligent upgrade.

[0008] The present invention is further configured to obtain fermentation degree data of fermented pastry flour under different fermentation time, temperature and humidity conditions, monitor the carbon dioxide release and pH value changes during the fermentation process through a sensor, analyze the relationship between the fermentation degree and the crust texture parameters, and determine the fermentation degree control threshold value in the following steps: S101. Based on the fermentation environmental conditions of fermented pastry flour, multi-sensor fusion technology is used to collect real-time data on carbon dioxide release and pH value changes during the fermentation process, and environmental interference is eliminated through data calibration to generate a fermentation degree monitoring data set; S102, based on the fermentation degree monitoring data set and combined with the crust texture parameters, experimentally analyzing the effect of the fermentation degree on the toughness, looseness, and ductility of the crust, and generating a correlation model between the fermentation degree and the crust texture; S103. Based on the correlation model between the fermentation degree and the crust texture, evaluation criteria for crust toughness, looseness, and fermentation uniformity are set, the rationality of the fermentation degree is verified through comparative experiments, and a fermentation degree control threshold is generated.

[0009] The present invention is further configured to obtain an initial mixing ratio of pastry flour, maltose, and edible oil according to the fermentation degree control threshold, calculate crust texture parameters by simulating the oil distribution and sugar crystallization state during the crust forming process, and obtain the crust crispness optimization formula in the following steps: S201. Based on the fermentation degree control threshold, set the initial mixing ratio of pastry flour, maltose, and cooking oil, obtain the oil distribution and sugar crystallization state of the crust under different ratios through experiments, and generate a mixing ratio experimental data set; S202. Based on the mixing ratio experimental data set, simulate the uniformity of fat distribution and the change of sugar crystal particle size during the crust forming process, analyze their effects on the crispiness of the crust, and generate crust texture parameter analysis results; S203. Based on the analysis results of the crust texture parameters, optimize the mixing ratio of pastry flour, maltose, and edible oil, verify the effect of improving the crispness of the crust through experiments, and generate a crust crispness optimization formula.

[0010] The present invention is further configured to obtain viscosity and softness data of glutinous rice flour at different moisture contents and processing temperatures based on the crust crispness optimization formula, establish a functional relationship between viscosity and softness, and determine the viscosity adjustment parameters of the filling in the following steps: S301, based on the crust crispness optimization formula, setting the moisture content and processing temperature range of the glutinous rice flour, obtaining the stickiness and softness data of the filling under different conditions through experiments, and generating a stickiness and softness experimental data set; S302: Based on the viscosity and softness experimental data set, analyze the effects of moisture content and processing temperature on the viscosity and softness of the filling, establish a functional relationship between viscosity and softness, and generate filling texture characteristic analysis results; S303. Based on the analysis results of the filling texture characteristics, set the cake filling viscosity adjustment parameters, verify the matching of the filling softness and the cake crust texture through experiments, and generate the cake filling viscosity adjustment parameters.

[0011] The present invention is further configured such that if the viscosity adjustment parameter of the filling meets a preset softness threshold, the mechanical properties of the filling and the crust during the forming process are measured, and the texture synergy parameters of the crust and the filling are calculated. The steps of obtaining the synergistic forming conditions are specifically as follows: S401: Based on the filling viscosity adjustment parameter, a filling softness threshold is set, and mechanical properties of the filling and crust during the forming process are measured experimentally to generate a crust and filling mechanical property dataset. S402: Analyze the mechanical matching degree of the crust and filling based on the mechanical property data set of the crust and filling, calculate the stress distribution and deformation characteristics during the molding process, and generate texture synergy parameters; S403. Based on the texture synergy parameters, the molding conditions of the crust and the filling are optimized, the molding stability is verified through experiments, and the synergistic molding conditions are generated.

[0012] The present invention is further configured to obtain the operating data of the fermentation, mixing and molding equipment on the production line based on the texture synergy parameters, analyze the correlation between the equipment parameters and product quality, and determine the production efficiency improvement plan in the following steps: S501. Based on the texture synergy parameters, collect operating data of the fermentation, mixing, and molding equipment on the production line, analyze the impact of the equipment parameters on product quality, and generate equipment operation data analysis results; S502: Based on the analysis results of the equipment operation data, a fermentation time reduction plan, a mixing process optimization configuration, and a molding equipment adjustment strategy are set; the effect of equipment parameter adjustment on production efficiency is verified through experiments, and a production efficiency improvement plan is generated; S503: Based on the production efficiency improvement plan, optimize the overall operating parameters of the production line, verify the feasibility of the plan through experiments, and generate optimized operating parameters of the production line.

[0013] The present invention is further configured such that if the equipment parameter deviation in the production efficiency improvement plan exceeds a preset threshold, the fermentation temperature, mixing speed, and molding pressure are adjusted to predict quality stability. The steps for obtaining stable production parameters are specifically as follows: S601. Based on the production efficiency improvement plan, set equipment parameter deviation thresholds, experimentally monitor the effects of changes in fermentation temperature, mixing speed, and molding pressure on product quality, and generate equipment parameter deviation analysis results. S602: Based on the equipment parameter deviation analysis results, adjust the fermentation temperature, mixing speed, and molding pressure, predict quality stability, and generate stable production parameters; S603: Based on the stable production parameters, optimize the equipment operation mode of the production line, verify the quality stability through experiments, and generate a stable production parameter optimization plan.

[0014] The present invention is further configured to obtain data on the crispness of the crust and the softness of the filling in batch production based on the stable production parameters, analyze the quality fluctuation trend, and determine whether the quality stability meets the preset standards. Specifically, the steps are: S701. Based on the stable production parameters, collect data on the crispness of the crust and the softness of the filling during batch production, analyze the quality fluctuation trend, and generate a quality fluctuation trend analysis result; S702: Based on the quality fluctuation trend analysis results, set a crust crispness consistency evaluation standard and a filling softness stability detection index, verify the quality stability through experiments, and generate a quality stability evaluation result; S703. Based on the quality stability evaluation results, optimize the key control points of the production process, verify through experiments whether the quality stability meets the preset standards, and generate a quality stability optimization plan.

[0015] The present invention is further configured to analyze historical data of fermentation degree, texture parameters and production efficiency, optimize the process flow, and obtain a long-term production optimization strategy if the quality stability meets the preset standards. Specifically, the steps are as follows: S801. Based on the quality stability optimization plan, analyze historical data of fermentation degree, texture parameters, and production efficiency to generate historical data analysis results; S802: Based on the historical data analysis results, optimize the fermentation process, mixing process, and operation mode of the molding equipment to generate a process optimization plan; S803. Based on the process optimization plan, formulate a long-term production optimization strategy, verify the sustainability of the strategy through experiments, and generate an implementation plan for the long-term production optimization strategy.

[0016] Beneficial effects of the present invention: The present invention can optimize the fermentation process of fermented pastry flour and coordinate the texture characteristics of crust and filling through the steps of controlling the degree of fermentation, generating a crispy crust optimization formula, determining filling viscosity adjustment parameters, calculating texture synergy parameters, formulating a production efficiency improvement plan, optimizing stable production parameters, evaluating quality stability, and generating a long-term production optimization strategy, so as to achieve the coordinated forming of crispy crust and soft filling. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] The invention is further described with reference to the accompanying drawings, but the embodiments in the accompanying drawings do not constitute any limitation to the invention. A person skilled in the art can obtain other drawings based on the following drawings without making any creative effort.

[0018] Figure 1 It is a process flow chart of the present invention. DETAILED DESCRIPTION

[0019] The present invention is further described with reference to the following examples.

[0020] Depend on Figure 1 As can be seen, the chicken biscuit production process described in this embodiment includes steps such as controlling the fermentation level, generating a recipe for optimizing the crispiness of the biscuit crust, determining parameters for adjusting the viscosity of the filling, calculating parameters for textural synergy, developing a production efficiency improvement plan, optimizing stable production parameters, assessing quality stability, and generating a long-term production optimization strategy. These steps are interconnected and progressive, forming a complete chicken biscuit production process system.

[0021] The first step in the implementation process was to obtain fermentation data for yeasted pastry flour under different fermentation times, temperatures, and humidity conditions. Sensors were then used to monitor carbon dioxide release and pH changes to analyze the relationship between fermentation and crust textural parameters, thereby determining the fermentation control threshold. The specific steps were as follows: Based on the fermentation environment of yeasted pastry flour, multi-sensor fusion technology was used to collect real-time data on carbon dioxide release and pH changes during the fermentation process. Data calibration was performed to eliminate environmental interference, generating a fermentation monitoring dataset. The experimental design included three different fermentation times (6 hours, 8 hours, and 10 hours), three different fermentation temperatures (25°C, 30°C, and 35°C), and three different humidity conditions (60%, 70%, and 80%), totaling 27 experimental conditions. Based on the experimental results, the CO2 release curve and pH change over time were recorded for each condition. Subsequently, combined with the crust textural parameters, the effect of fermentation on crust toughness, looseness, and ductility was experimentally analyzed, generating a correlation model between fermentation and crust texture. The model formula is: F = aT + bH + cC + dP, where F represents the comprehensive fermentation score, T represents the fermentation time, H represents the fermentation humidity, C represents the carbon dioxide release, and P represents the pH variation. a, b, c, and d are weighting coefficients, derived through experimental fitting. Ultimately, evaluation criteria for crust toughness, looseness, and fermentation uniformity were established. The rationality of the fermentation level was verified through comparative experiments, and a fermentation level control threshold was generated.

[0022] Next, the initial mixing ratio of pastry flour, maltose, and cooking oil is determined based on the fermentation control threshold. By simulating the fat distribution and sugar crystallization during the crust forming process, crust textural parameters are calculated, resulting in an optimized crust crispness formula. The specific implementation is as follows: Based on the fermentation control threshold, the initial mixing ratio of pastry flour, maltose, and cooking oil is set—for example, 60% pastry flour, 20% maltose, and 20% cooking oil. Experiments are conducted to determine the fat distribution and sugar crystallization in the crust at different ratios, generating an experimental dataset for the mixing ratios. The experiments revealed that excessively high maltose content increases the sugar crystal size, resulting in a hard crust; whereas excessively high cooking oil content results in uneven fat distribution, affecting the crust's crispness. Therefore, the uniformity of fat distribution and changes in sugar crystal size during the crust forming process are further simulated to analyze their impact on the crust's crispness and generate crust textural parameter analysis results. The formula for evaluating crust crispness is S=eU+fG+hE, where S represents the crust crispness score, U represents the oil distribution uniformity index, G represents the sugar crystal size, and E represents the dough extensibility. e, f, and h are weighting coefficients, derived through experimental fitting. Finally, the mixing ratio of pastry flour, maltose, and cooking oil was optimized. The effect of improving crust crispness was experimentally verified, resulting in an optimized crust recipe. For example, the ratio of pastry flour was adjusted to 65%, maltose to 18%, and cooking oil to 17%.

[0023] After optimizing the crispiness of the pastry crust, the researchers used this recipe to obtain data on the viscosity and softness of glutinous rice flour at different moisture contents and processing temperatures. A functional relationship between viscosity and softness was established to determine the viscosity adjustment parameters for the pastry filling. The specific implementation was as follows: Based on the optimized pastry crust recipe, the moisture content of the glutinous rice flour was set between 30% and 50%, and the processing temperature range was 80°C to 120°C. Experimental data on the viscosity and softness of the filling under these conditions were obtained, generating a dataset for viscosity and softness. The experimental results showed that low moisture content resulted in higher viscosity but lower softness, while high processing temperature increased softness but decreased viscosity. Therefore, the researchers further analyzed the effects of moisture content and processing temperature on the viscosity and softness of the filling, established a functional relationship between viscosity and softness, and generated filling texture analysis results. The functional relationship is V=kM+lT+m, where V represents the viscosity of the filling, M represents the moisture content, and T represents the processing temperature. k, l, and m are constants derived through experimental fitting. Finally, the viscosity adjustment parameters of the filling are set, and the matching of the filling softness and the crust structure is verified through experiments to generate the viscosity adjustment parameters of the filling, such as the moisture content is set to 40% and the processing temperature is set to 100°C.

[0024] If the viscosity adjustment parameter of the filling meets the preset softness threshold, the mechanical properties of the filling and the crust during the molding process are measured, the texture synergy parameters of the crust and the filling are calculated, and the synergistic molding conditions are obtained. The specific implementation is as follows: Based on the viscosity adjustment parameter of the filling, the softness threshold of the filling is set, for example, the softness score is above 80 points, and the mechanical properties of the filling and the crust during the molding process are measured experimentally to generate a data set of mechanical properties of the crust and the filling. In the experiment, a mechanical tester is used to measure the stress-strain curves of the crust and the filling respectively, analyze their mechanical matching, calculate the stress distribution and deformation characteristics during the molding process, and generate texture synergy parameters. The texture synergy formula is C=nR+oD+pS, where C represents the texture synergy score, R represents the mechanical matching degree between the crust and the filling, D represents the uniformity of stress distribution, S represents the molding stability, and n, o, and p are weight coefficients, which are obtained through experimental fitting. Finally, the molding conditions of the crust and filling were optimized, the molding stability was verified through experiments, and collaborative molding conditions were generated, such as setting the molding pressure to 0.5 MPa and the molding time to 10 seconds.

[0025] Based on texture synergy parameters, operational data from the fermentation, mixing, and molding equipment on the production line is collected. The correlation between equipment parameters and product quality is analyzed to determine a plan to improve production efficiency. The specific implementation is as follows: Based on texture synergy parameters, operational data from the fermentation, mixing, and molding equipment on the production line, such as fermentation time, mixing speed, and molding pressure, is collected. The impact of equipment parameters on product quality is analyzed, and the results of the equipment operation data analysis are generated. Experiments have found that excessively long fermentation times can reduce the toughness of the crust, excessively fast mixing speeds can lead to uneven oil distribution, and insufficient molding pressure can lead to an unstable bond between the crust and filling. Therefore, a fermentation time reduction plan, optimized mixing process configuration, and molding equipment adjustment strategies are established. The effect of equipment parameter adjustments on production efficiency is experimentally verified, and a production efficiency improvement plan is generated. For example, the fermentation time can be shortened from 10 hours to 8 hours, the mixing speed can be adjusted from 100 rpm to 120 rpm, and the molding pressure can be adjusted from 0.4 MPa to 0.5 MPa.

[0026] If the equipment parameter deviation in the production efficiency improvement plan exceeds a preset threshold, the fermentation temperature, mixing speed, and molding pressure are adjusted to predict quality stability and generate stable production parameters. The specific implementation is as follows: Based on the production efficiency improvement plan, equipment parameter deviation thresholds are set, such as a fermentation temperature deviation of no more than ±2°C, a mixing speed deviation of no more than ±10 rpm, and a molding pressure deviation of no more than ±0.1 MPa. Experiments are conducted to monitor the impact of changes in fermentation temperature, mixing speed, and molding pressure on product quality, generating equipment parameter deviation analysis results. The experiments found that increasing the fermentation temperature to 37°C significantly reduced the toughness of the crust; reducing the mixing speed to 110 rpm affected the uniformity of the fat distribution; and reducing the molding pressure to 0.45 MPa reduced the stability of the crust-filling bond. Therefore, the fermentation temperature, mixing speed, and molding pressure are adjusted to predict quality stability and generate stable production parameters. For example, the fermentation temperature is adjusted to 35°C, the mixing speed is adjusted to 115 rpm, and the molding pressure is adjusted to 0.48 MPa. Finally, the equipment operating mode of the production line is optimized, and quality stability is verified through experiments to generate a stable production parameter optimization plan.

[0027] According to the stable production parameters, the crispness of the crust and the softness of the filling are obtained in the batch production, the quality fluctuation trend is analyzed, and it is determined whether the quality stability meets the preset standards. The specific implementation is as follows: Based on the stable production parameters, the crispness of the crust and the softness of the filling are collected in the batch production, such as the crispness score of the crust and the softness score of the filling for 10 consecutive batches, the quality fluctuation trend is analyzed, and the quality fluctuation trend analysis results are generated. It was found in the experiment that the crispness score of the crust fluctuated between 85 and 90 points, and the softness score of the filling fluctuated between 80 and 85 points. Therefore, the consistency evaluation standard of the crust crispness and the stability detection index of the filling softness are set, and the quality stability is verified through experiments to generate the quality stability evaluation results. For example, the consistency score of the crust crispness is set to above 88 points, and the stability score of the filling softness is set to above 83 points. Finally, the key control points of the production process are optimized, and the quality stability is verified through experiments to generate the quality stability optimization plan.

[0028] If quality stability meets the preset standards, historical data on fermentation level, texture parameters, and production efficiency are analyzed to optimize the process flow and develop a long-term production optimization strategy. The specific implementation is as follows: Based on the quality stability optimization plan, historical data on fermentation level, texture parameters, and production efficiency are analyzed, including fermentation time, crust crispness scores, filling softness scores, and production efficiency data from the past year. Historical data analysis results are generated. Experiments have shown that crust toughness is optimal when the fermentation time is controlled at around 8 hours; consumer satisfaction is highest when the crust crispness score remains above 88 points; and a 10% increase in production efficiency leads to significant profit growth. Therefore, the fermentation process, mixing steps, and molding equipment operating modes are optimized to generate a process optimization plan. For example, the fermentation time is fixed at 8 hours, the mixing speed is fixed at 115 rpm, and the molding pressure is fixed at 0.48 MPa. Finally, a long-term production optimization strategy is developed, the sustainability of the strategy is verified through experiments, and an implementation plan for the long-term production optimization strategy is generated. For example, an intelligent equipment monitoring system is introduced to monitor fermentation temperature, mixing speed, and molding pressure in real time to ensure production stability and efficiency.

[0029] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit the scope of protection of the present invention. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the essence and scope of the technical solutions of the present invention.

Claims

1. A chicken biscuit production process, characterized by: The following steps are involved: S1. Obtain fermentation degree data of fermented pastry flour under different fermentation time, temperature and humidity conditions, monitor carbon dioxide release and pH changes during the fermentation process through sensors, analyze the relationship between fermentation degree and crust texture parameters, and determine the fermentation degree control threshold; S2. According to the fermentation degree control threshold, an initial mixing ratio of pastry flour, maltose, and edible oil is obtained, and by simulating the oil distribution and sugar crystallization state during the crust forming process, crust texture parameters are calculated to obtain an optimized crust crispness formula; S3. Based on the crust crispness optimization formula, obtain the viscosity and softness data of glutinous rice flour at different moisture contents and processing temperatures, establish a functional relationship between viscosity and softness, and determine the viscosity adjustment parameters of the filling; S4. If the viscosity adjustment parameter of the filling meets the preset softness threshold, the mechanical properties of the filling and the crust during the forming process are measured, and the texture synergy parameters of the crust and the filling are calculated to obtain the synergistic forming conditions. S5. Based on the texture synergy parameters, obtain operating data of the fermentation, mixing, and molding equipment on the production line, analyze the correlation between the equipment parameters and product quality, and determine a plan to improve production efficiency; S6. If the equipment parameter deviation in the production efficiency improvement plan exceeds the preset threshold, adjust the fermentation temperature, mixing speed and molding pressure to predict quality stability and obtain stable production parameters; S7. Based on the stable production parameters, obtain data on the crispness of the crust and the softness of the filling during batch production, analyze the quality fluctuation trend, and determine whether the quality stability meets the preset standards; S8. If the quality stability meets the preset standards, analyze the historical data of fermentation degree, texture parameters and production efficiency, optimize the process flow, and obtain a long-term production optimization strategy.

2. The chicken biscuit production process according to claim 1, characterized in that: The fermentation degree control threshold includes crust toughness assessment, looseness detection and fermentation uniformity analysis; the crust crispness optimization formula includes oil distribution uniformity, sugar crystal particle size and dough extensibility optimization; the filling viscosity adjustment parameters include filling viscosity range and softness matching; the texture synergy parameters are specifically the mechanical matching and molding stability between crust and filling; the production efficiency improvement plan includes fermentation time shortening plan, mixing process optimization configuration and molding equipment adjustment strategy; the stable production parameters include fermentation temperature and humidity curve, mixing speed dynamic adjustment and molding pressure distribution; the quality fluctuation trend analysis includes crust crispness consistency assessment and filling softness stability detection; the long-term production optimization strategy includes fermentation process improvement plan, mixing process standardization design and molding equipment intelligent upgrade.

3. The chicken biscuit production process according to claim 1, characterized in that: The fermentation degree data of yeast pastry flour under different fermentation time, temperature and humidity conditions were obtained. The carbon dioxide release and pH value changes during the fermentation process were monitored by sensors. The relationship between the fermentation degree and the crust texture parameters was analyzed. The specific steps to determine the fermentation degree control threshold were as follows: S101. Based on the fermentation environmental conditions of fermented pastry flour, multi-sensor fusion technology is used to collect real-time data on carbon dioxide release and pH value changes during the fermentation process, and environmental interference is eliminated through data calibration to generate a fermentation degree monitoring data set; S102, based on the fermentation degree monitoring data set and combined with the crust texture parameters, experimentally analyzing the effect of the fermentation degree on the toughness, looseness, and ductility of the crust, and generating a correlation model between the fermentation degree and the crust texture; S103. Based on the correlation model between the fermentation degree and the crust texture, evaluation criteria for crust toughness, looseness, and fermentation uniformity are set, the rationality of the fermentation degree is verified through comparative experiments, and a fermentation degree control threshold is generated.

4. The chicken biscuit production process according to claim 1, characterized in that: The steps of obtaining the initial mixing ratio of pastry flour, maltose, and edible oil according to the fermentation degree control threshold, calculating the crust texture parameters by simulating the oil distribution and sugar crystallization state during the crust forming process, and obtaining the crust crispness optimization formula are as follows: S201. Based on the fermentation degree control threshold, set the initial mixing ratio of pastry flour, maltose, and cooking oil, obtain the oil distribution and sugar crystallization state of the crust under different ratios through experiments, and generate a mixing ratio experimental data set; S202. Based on the mixing ratio experimental data set, simulate the uniformity of fat distribution and the change of sugar crystal particle size during the crust forming process, analyze their effects on the crispiness of the crust, and generate crust texture parameter analysis results; S203. Based on the analysis results of the crust texture parameters, optimize the mixing ratio of pastry flour, maltose, and edible oil, verify the effect of improving the crispness of the crust through experiments, and generate a crust crispness optimization formula.

5. The chicken biscuit production process according to claim 1, characterized in that: Based on the crispy optimization formula of the pastry crust, the viscosity and softness data of glutinous rice flour at different moisture contents and processing temperatures are obtained, a functional relationship between viscosity and softness is established, and the steps of determining the viscosity adjustment parameters of the pastry filling are specifically as follows: S301, based on the crust crispness optimization formula, setting the moisture content and processing temperature range of the glutinous rice flour, obtaining the stickiness and softness data of the filling under different conditions through experiments, and generating a stickiness and softness experimental data set; S302: Based on the viscosity and softness experimental data set, analyze the effects of moisture content and processing temperature on the viscosity and softness of the filling, establish a functional relationship between viscosity and softness, and generate filling texture characteristic analysis results; S303. Based on the analysis results of the filling texture characteristics, set the cake filling viscosity adjustment parameters, verify the matching of the filling softness and the cake crust texture through experiments, and generate the cake filling viscosity adjustment parameters.

6. The chicken biscuit production process according to claim 1, characterized in that: If the viscosity adjustment parameter of the filling meets the preset softness threshold, the mechanical properties of the filling and the crust during the molding process are measured, and the texture synergy parameters of the crust and the filling are calculated. The specific steps for obtaining the synergistic molding conditions are as follows: S401: Based on the filling viscosity adjustment parameter, a filling softness threshold is set, and mechanical properties of the filling and crust during the forming process are measured experimentally to generate a crust and filling mechanical property dataset. S402: Analyze the mechanical matching degree of the crust and filling based on the mechanical property data set of the crust and filling, calculate the stress distribution and deformation characteristics during the molding process, and generate texture synergy parameters; S403. Based on the texture synergy parameters, the molding conditions of the crust and the filling are optimized, the molding stability is verified through experiments, and the synergistic molding conditions are generated.

7. The chicken biscuit production process according to claim 1, characterized in that: Based on the texture synergy parameters, the operating data of the fermentation, mixing, and molding equipment on the production line is obtained, the correlation between the equipment parameters and product quality is analyzed, and the steps to determine the production efficiency improvement plan are as follows: S501. Based on the texture synergy parameters, collect operating data of the fermentation, mixing, and molding equipment on the production line, analyze the impact of the equipment parameters on product quality, and generate equipment operation data analysis results; S502: Based on the analysis results of the equipment operation data, a fermentation time reduction plan, a mixing process optimization configuration, and a molding equipment adjustment strategy are set; the effect of equipment parameter adjustment on production efficiency is verified through experiments, and a production efficiency improvement plan is generated; S503: Based on the production efficiency improvement plan, optimize the overall operating parameters of the production line, verify the feasibility of the plan through experiments, and generate optimized operating parameters of the production line.

8. The chicken biscuit production process according to claim 1, characterized in that: If the equipment parameter deviation in the production efficiency improvement plan exceeds the preset threshold, the fermentation temperature, mixing speed, and molding pressure are adjusted to predict quality stability. The specific steps to obtain stable production parameters are as follows: S601. Based on the production efficiency improvement plan, set equipment parameter deviation thresholds, experimentally monitor the effects of changes in fermentation temperature, mixing speed, and molding pressure on product quality, and generate equipment parameter deviation analysis results. S602: Based on the equipment parameter deviation analysis results, adjust the fermentation temperature, mixing speed, and molding pressure, predict quality stability, and generate stable production parameters; S603: Based on the stable production parameters, optimize the equipment operation mode of the production line, verify the quality stability through experiments, and generate a stable production parameter optimization plan.

9. The chicken biscuit production process according to claim 1, characterized in that: The steps for obtaining the crust crispness and filling softness data during batch production based on the stable production parameters, analyzing the quality fluctuation trend, and determining whether the quality stability meets the preset standards are as follows: S701. Based on the stable production parameters, collect data on the crispness of the crust and the softness of the filling during batch production, analyze the quality fluctuation trend, and generate a quality fluctuation trend analysis result; S702: Based on the quality fluctuation trend analysis results, set a crust crispness consistency evaluation standard and a filling softness stability detection index, verify the quality stability through experiments, and generate a quality stability evaluation result; S703. Based on the quality stability evaluation results, optimize the key control points of the production process, verify through experiments whether the quality stability meets the preset standards, and generate a quality stability optimization plan.

10. The chicken biscuit production process according to claim 1, characterized in that: If the quality stability meets the preset standards, the historical data of fermentation degree, texture parameters and production efficiency are analyzed to optimize the process flow and obtain the long-term production optimization strategy. The specific steps are as follows: S801. Based on the quality stability optimization plan, analyze historical data of fermentation degree, texture parameters, and production efficiency to generate historical data analysis results; S802: Based on the historical data analysis results, optimize the fermentation process, mixing process, and operating mode of the molding equipment to generate a process optimization plan; S803. Based on the process optimization plan, formulate a long-term production optimization strategy, verify the sustainability of the strategy through experiments, and generate an implementation plan for the long-term production optimization strategy.

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