Control system for producing biomass graphene quantum dots based on agricultural wastes

Abstract

The invention discloses a control system for producing biomass graphene quantum dots based on agricultural wastes, which comprises an information acquisition unit, a pretreatment unit, a comprehensive treatment unit and a condition control unit, the method comprises the following steps: firstly, acquiring and regularly monitoring preparation environment information of a production preparation environment for producing biomass graphene quantum dots from agricultural wastes through an information acquisition unit, and correcting and cleaning the acquired preparation environment information through a pretreatment unit to realize accuracy and reliability of the information; the preprocessed preparation environment information is analyzed through a comprehensive processing unit, a comprehensive evaluation coefficient is generated in combination with a preparation state coefficient and a control management coefficient, then different regulation and control signals and prompt signals are generated for regulation and control management, and meanwhile regulation is conducted according to a control management scheme; and automatic optimization of the control management scheme is realized through feedback circulation.

Description

Technical Field

[0001] The invention relates to the technical field of graphene quantum dots, and in particular to a control system for producing biomass graphene quantum dots based on agricultural waste. Background Art

[0002] Graphene quantum dots (GQDs) are a new type of carbon nanomaterial with many advantages such as simple synthesis, good water solubility and good biocompatibility. Due to these excellent properties, GQDs are easily absorbed by plants and can be used in real-time imaging of plant systems and potential agricultural fertilizers. In the prior art, however, the growth and influence mechanism of zero-dimensional graphene quantum dots as regulators on crops have not been explored; generally, different laser pulses are used to monitor the production and preparation environment of biomass graphene quantum dots and uniformly process the crops produced to improve the photosynthesis of crops. During the monitoring process, the difference in data may cause inaccurate monitoring, thereby affecting the final effect; In view of the above technical defects, a solution is now proposed. Summary of the invention

[0003] The purpose of the present invention is to propose a control system based on the production of biomass graphene quantum dots from agricultural waste, which solves the problem of unclear generation and influence mechanism of crops in the control environment of biomass graphene quantum dots, and realizes a control management scheme with high accuracy, strong pertinence and automatic optimization.

[0004] In order to achieve the above-mentioned purpose, the present invention adopts the following technical scheme: a control system for producing biomass graphene quantum dots based on agricultural waste, comprising an information collection unit, a preprocessing unit, a comprehensive processing unit and a condition control unit, and the information collection unit, the preprocessing unit, the comprehensive processing unit and the condition control unit are communicatively connected; First, a preliminary control and management plan is set up for the production and preparation environment of biomass graphene quantum dots produced from agricultural waste, and the preparation environment information A of the preparation environment is collected through the information collection unit, and a collection cycle is set to perform regular collection and detection of the preparation environment information A; The preprocessing unit performs data correction and cleaning operations on the collected preparation environment information A, then outputs the preprocessed preparation environment information B, and sends it to the comprehensive processing unit; The comprehensive processing unit analyzes the preparation environment information B, obtains the comprehensive evaluation coefficient and generates corresponding control signals and prompt signals to the condition control unit; wherein the comprehensive processing unit includes a preparation analysis module, a control evaluation module and a comprehensive analysis module; The preparation state coefficient is generated through the preparation analysis module, the control management coefficient is generated through the control evaluation module, and the comprehensive analysis module generates a comprehensive evaluation coefficient by combining the preparation state coefficient and the control management coefficient, and generates a control signal and a prompt signal; After receiving the control signal and the prompt signal, the condition control unit adjusts and refreshes the control management plan accordingly, generates and feeds back the control instruction to the information collection unit, and repeats the above operation to realize the automatic optimization of the control management of the preparation environment of biomass graphene quantum dots produced from agricultural waste.

[0005] Furthermore, the specific working process of the information collection unit collecting the preparation environment information A of the preparation environment is as follows: Step A1, establishing a three-dimensional spatial model of the production preparation environment, dividing the preparation environment into N areas, marking any area as Ni, and collecting and obtaining corresponding preparation environment information A in the area Ni after using the first laser pulse, the second laser pulse and the third laser pulse; Step A2, setting a collection period T1, regularly collecting and monitoring the preparation environment information A, the preparation environment information A including the particle size, ID / IG ratio and oxygen content of the biomass graphene quantum dots; Among them, the morphology of biomass graphene quantum dots is set to have nf kinds of crystal phase structures, any crystal phase structure is marked as j, and the particle size of the crystal phase structure j is marked as cj; the functional surface groups are set to have ns kinds, any functional surface group is marked as r, and the oxygen content is marked as Hr; and the ID / IG ratio is marked as V.

[0006] Furthermore, the specific process of the preprocessing unit is: Step B1, construct information preprocessing model: Step B1-1, setting and marking a set of preparation environment information A Qa={particle size C, ID / IG ratio V, oxygen content H}; Step B1-11, input any element in the set Qa, set the measurement period T2, and the number of acquisitions within the measurement period T2 is nc; then obtain the values ​​of nc elements within the measurement period, and establish a two-dimensional curve graph Sc with the acquisition period T1 as the horizontal coordinate and the value of the current element as the vertical coordinate; at the same time, preset the threshold curve graph Se of the two-dimensional curve graph Sc in the coordinate system, and then calculate the distance value jl of the corresponding point of the current element between the two-dimensional curve graph Sc and the threshold curve graph Se; Step B1-12, set the difference range ±wc%, when the distance value jl corresponding to any element exceeds the set difference range ±wc%, obtain the coordinate points of the element on the two-dimensional curve graph Sc and the threshold curve graph Se, and mark them as coordinate point zb1 and coordinate point zb2 respectively; then calculate the average value of the ordinates of coordinate point zb1 and coordinate point zb2 to obtain the corrected coordinate JZj, and replace the value of coordinate point zb1 with the corrected coordinate JZj; Step B2, applying the information preprocessing model: substituting the elements in the set Qa of the preparation environment information A into the information preprocessing model in sequence, correcting and cleaning the preparation environment information A, and then outputting the preparation environment information B.

[0007] Furthermore, the specific working process of generating the preparation state coefficient through the preparation analysis module is as follows: Step C1, constructing a preliminary analysis model for the preparation status: marking the input information as a set Qb, where there are M0 elements in the set Qb, marking the two-dimensional change curve corresponding to any element as Sr, and then obtaining the data Cm of the end point of the curve; and assigning a corresponding weight coefficient to the data Cm to obtain the corresponding influence coefficient; Step C2, applying the preliminary analysis model of the preparation state: after the preprocessing operation, a two-dimensional curve graph of the preparation environment information B of N areas in the production preparation environment is obtained; and then the preparation environment information B using the first laser pulse, the second laser pulse and the third laser pulse are sequentially substituted into the preliminary analysis model of the preparation state to obtain the corresponding particle size terminal influence coefficient, ID / IG ratio terminal influence coefficient and oxygen content terminal influence coefficient respectively; Step C3, assigning corresponding normalization coefficients to the particle size terminal influence coefficient, ID / IG ratio terminal influence coefficient and oxygen content terminal influence coefficient in the Ni region, and establishing a formula to obtain the regional state coefficient QYi of the region Ni; Then, the average value of the N regional state coefficients QYi is calculated. , and then calculate the variance to obtain the coefficient of difference Cy, and compare the coefficient of difference Cy with the mean value Combined, the preparation state coefficient F1 of the production preparation environment is obtained.

[0008] Furthermore, the specific working process of generating the control management coefficient through the control evaluation module is as follows: Step D1: Construct a preliminary analysis model for management evaluation: Establish a change curve of the preparation state coefficient F1-time T, preset a standard range of the preparation state coefficient F1, and when the preparation state coefficient F1 exceeds the standard range, mark the time node that exceeds the value as an abnormal time point; set the historical period Tx, extract two time series from the historical period Tx, and calculate and generate the abnormal curve time period, and then sort them according to the time series and mark them as T1, T2, T3, ..., Tn; where the time series represents the dynamic change amount, and n is the number of abnormal times; The abnormal curve time period is divided into equal parts and marked as dm, where dm is a dynamic value. When it has a remainder, it is dm+1, and dm is a positive integer; When it is dm, the corresponding abnormal curve time period is set, and the corresponding abnormal factor wr is calculated by setting the formula; When it is the abnormal curve time period corresponding to dm+1, set the formula to calculate the corresponding abnormal factor wr; Step D2: Obtain the abnormal factors of all abnormal curve time periods through the preliminary analysis model of management capability, and then obtain the average value of all abnormal factors. , by taking the outlier factor wr and its mean Combined with the above, the control management coefficient F2 of the production and preparation environment is obtained.

[0009] Furthermore, the specific working process of generating corresponding control signals and prompt signals is as follows: Step E1, firstly combine the preparation state coefficient F1 and the control management coefficient F2 of the production preparation environment to generate a comprehensive evaluation coefficient PG; Step E2, generate a control signal through the comprehensive evaluation coefficient PG: A standard interval Z1 of the preset comprehensive evaluation coefficient PG is used for comparative analysis. When the comprehensive evaluation coefficient PG exceeds the standard interval Z1, a control signal is generated; Step E3, set up the depth analysis model again and generate the corresponding prompt signal: Obtain comprehensive evaluation coefficients PG under the first laser pulse, the second laser pulse, and the third laser pulse, mark them as PG1, PG2, and PG3 respectively, and then construct a three-dimensional feature change space with PG1, PG2, and PG3 as the X-axis and time T as the Y-axis; At the same time, a standard threshold space of the three-dimensional feature change space is preset in the coordinate system, and then the three-dimensional feature change space and the standard threshold space are compared and analyzed, and the horizontal and vertical screenshots thereof are obtained and compared to generate the total overlapping area cmj and the total non-overlapping area fmj, and a formula is set to obtain the passive control factor B0, and then the proportion values ​​of the passive control factor B0 in the three-dimensional coordinate system are obtained, and the proportion values ​​under the first laser pulse, the second laser pulse and the third laser pulse are marked as the first proportion, the second proportion and the third proportion, respectively, and then the thresholds of the first proportion, the second proportion and the third proportion are set respectively, and when the proportion value exceeds the preset threshold value, different prompt signals are generated; Step E4, after receiving the corresponding prompt signal, the control management plan is adjusted, and then the production preparation environment is regulated and managed through the control signal.

[0010] In summary, due to the adoption of the above technical solution, the beneficial effects of the present invention are: The information collection unit is used to collect and regularly monitor the preparation environment information of the production environment of biomass graphene quantum dots produced from agricultural waste. The preprocessing unit is used to correct and clean the collected preparation environment information to achieve accuracy and reliability of the information. The preprocessed preparation environment information is then analyzed by the comprehensive processing unit, and a comprehensive evaluation coefficient is generated by combining the preparation state coefficient and the control management coefficient, thereby generating different control signals and prompt signals for control management. At the same time, adjustments are made to the control management plan, and automatic optimization of the control management plan is achieved through a feedback loop. BRIEF DESCRIPTION OF THE DRAWINGS

[0011] Figure 1 A module schematic diagram of the present invention is shown. DETAILED DESCRIPTION

[0012] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0013] Example like Figure 1 As shown, the control system for producing biomass graphene quantum dots based on agricultural waste includes an information acquisition unit, a preprocessing unit, a comprehensive processing unit and a condition control unit, and the information acquisition unit, the preprocessing unit, the comprehensive processing unit and the condition control unit are communicatively connected; The specific working steps are as follows: S1, first set up and implement a preliminary control management plan for the production environment of biomass graphene quantum dots produced from agricultural waste, collect the preparation environment information A of the preparation environment through the information collection unit, set a collection cycle, and perform regular collection and detection of the preparation environment information A; The specific working process of the information collection unit collecting the preparation environment information A of the preparation environment is as follows: Step A1, establishing a three-dimensional spatial model of the production preparation environment, dividing the preparation environment into N areas, marking any area as Ni, and collecting and obtaining corresponding preparation environment information A in the area Ni after using the first laser pulse, the second laser pulse and the third laser pulse; Step A2, setting a collection period T1, regularly collecting and monitoring the preparation environment information A, the preparation environment information A including the particle size, ID / IG ratio and oxygen content of the biomass graphene quantum dots; Among them, the morphology of biomass graphene quantum dots is set to have nf kinds of crystal phase structures, any crystal phase structure is marked as j, and the particle size of the crystal phase structure j is marked as cj; the functional surface groups are set to have ns kinds, any functional surface group is marked as r, and the oxygen content is marked as Hr; the ID / IG ratio is also marked as V; it should be noted that this system is connected to a transmission electron microscope (TEM), an X-ray diffraction spectrometer (XRD), an X-ray photoelectron spectrometer (XPS), a Fourier transform infrared spectrometer (FTIR), a Raman spectrometer (Raman) and an ultraviolet spectrophotometer (UV), and the above devices can be used to obtain the morphology, crystal phase structure, elemental composition and surface functional groups and other physical properties of biomass graphene quantum dots, and then the influence of different laser pulses on the particle size, dispersibility and hydrophilicity of the prepared biomass graphene quantum dots can be explored; and the above devices are not drawn in the figure; S2, the preprocessing unit performs data correction and cleaning operations on the collected preparation environment information A, then outputs the preprocessed preparation environment information B, and sends it to the comprehensive processing unit; Among them, the specific process of the preprocessing unit is: Step B1, construct information preprocessing model: Step B1-1, setting and marking a set of preparation environment information A Qa={particle size C, ID / IG ratio V, oxygen content H}; Step B1-11, input any element in the set Qa, set the measurement period T2, and the number of acquisitions within the measurement period T2 is nc; then obtain the values ​​of nc elements within the measurement period, and establish a two-dimensional curve graph Sc with the acquisition period T1 as the horizontal coordinate and the value of the current element as the vertical coordinate; at the same time, preset the threshold curve graph Se of the two-dimensional curve graph Sc in the coordinate system, and then calculate the distance value jl of the corresponding point of the current element between the two-dimensional curve graph Sc and the threshold curve graph Se; Step B1-12, set the difference range ±wc%, when the distance value jl corresponding to any element exceeds the set difference range ±wc%, obtain the coordinate points of the element on the two-dimensional curve graph Sc and the threshold curve graph Se, and mark them as coordinate point zb1 and coordinate point zb2 respectively; then calculate the average value of the ordinates of coordinate point zb1 and coordinate point zb2 to obtain the corrected coordinate JZj, and replace the value of coordinate point zb1 with the corrected coordinate JZj; Step B2, applying the information preprocessing model: substituting the elements in the set Qa of the preparation environment information A into the information preprocessing model in sequence, correcting and cleaning the preparation environment information A, and then outputting the preparation environment information B; S3, the comprehensive processing unit analyzes the preparation environment information B, obtains the comprehensive evaluation coefficient and generates corresponding control signals and prompt signals to the condition control unit; wherein the comprehensive processing unit includes a preparation analysis module, a control evaluation module and a comprehensive analysis module; The preparation state coefficient is generated through the preparation analysis module, the control management coefficient is generated through the control evaluation module, and the comprehensive analysis module generates a comprehensive evaluation coefficient by combining the preparation state coefficient and the control management coefficient, and generates a control signal and a prompt signal; S3-1, the specific working process of generating the preparation state coefficient through the preparation analysis module is as follows: Step C1, constructing a preliminary analysis model for the preparation status: marking the input information as a set Qb, where there are M0 elements in the set Qb, marking the two-dimensional change curve corresponding to any element as Sr, and then obtaining the data Cm of the end point of the curve; and assigning a corresponding weight coefficient to the data Cm to obtain the corresponding influence coefficient; Step C2, applying the preliminary analysis model of the preparation state: after the preprocessing operation, a two-dimensional curve graph of the preparation environment information B of N areas in the production preparation environment is obtained; and then the preparation environment information B using the first laser pulse, the second laser pulse and the third laser pulse are sequentially substituted into the preliminary analysis model of the preparation state to obtain the corresponding particle size terminal influence coefficient, ID / IG ratio terminal influence coefficient and oxygen content terminal influence coefficient respectively; Step C2-1: through the change curve of the particle size C of the nf seed crystal phase structures in the current area, mark the change curve of the particle size of any morphology as S1, obtain the data Cm1 of the end point of the curve S1, and then obtain nf end data values ​​of the curve through the nf seed crystal phase structures; By assigning corresponding weight coefficient μ1 to nf data values ​​at the end of the curve, and u1 is a natural number greater than 0, and then performing cumulative calculation, the particle size end influence coefficient Y1 is obtained: ; Step C2-2: through the variation curve of oxygen content H of ns kinds of functional surface groups r in the current region, mark the variation curve of oxygen content H of any functional surface group as S2, obtain the data Cm2 of the end point of curve S2, and then obtain ns end data values ​​of the curve through ns kinds of crystal phase structures; By assigning corresponding weight coefficient μ2 to ns data values ​​of the last segment of the curve, and u2 is a natural number greater than 0, and then performing cumulative calculation, the terminal influence coefficient Y2 of the oxygen content is obtained: ; Step C2-3, obtaining the data value Vm of the end point of the curve S3 through the change curve S3 of the ID / IG ratio V in the current area; Step C3, assigning corresponding normalization coefficients to the particle size terminal influence coefficient, ID / IG ratio terminal influence coefficient and oxygen content terminal influence coefficient in the Ni region, and establishing a formula: Obtain the regional state coefficient QYi of region Ni; wherein β1, β2 and β3 are normalized coefficients of the particle size terminal influence coefficient, the oxygen content terminal influence coefficient and the ID / IG ratio terminal influence coefficient, respectively, and β1, β2 and β3 are all greater than 0; it should be noted that the larger the particle size terminal influence coefficient, the oxygen content terminal influence coefficient and the ID / IG ratio terminal influence coefficient, the larger the corresponding regional state coefficient QYi; Then, the average value of the N regional state coefficients QYi is calculated. , and then calculate the variance to obtain the coefficient of difference Cy, and compare the coefficient of difference Cy with the mean value Combined, the preparation state coefficient F1 of the production preparation environment is obtained: It should be noted that the coefficient of variation Cy represents the preparation difference of N areas in the entire production preparation environment. The larger the value, the greater the preparation difference and the average value. The larger the gap is, the worse the overall production and preparation environment is. S3-2, the specific working process of generating the control management coefficient through the control evaluation module is: Step D1: Construct a preliminary analysis model for management evaluation: Establish a change curve of the preparation state coefficient F1-time T, preset a standard range of the preparation state coefficient F1, and when the preparation state coefficient F1 exceeds the standard range, mark the time node that exceeds the value as an abnormal time point; set the historical period Tx, extract two time series from the historical period Tx, and calculate and generate the abnormal curve time period, and then sort them according to the time series and mark them as T1, T2, T3, ..., Tn; where the time series represents the dynamic change amount, and n is the number of abnormal times; The abnormal curve time period is divided equally and marked as dm, and dm is a dynamic value. When it has a remainder, it is dm+1, and dm is a positive integer; there are Xs preset abnormal curve time periods, and any abnormal curve time period is marked as pds, and the peak point of the abnormal curve time period pds is marked as Ps (x1, y1), and the valley point of the abnormal curve time period pds is marked as Gs (x2, y2); When it is dm, the corresponding abnormal curve time period is set as follows: Calculate and obtain the corresponding abnormal factor wr; When it is the abnormal curve time period corresponding to dm+1, set the formula: Calculate the corresponding abnormal factor wr; where T i is the abnormal curve time period, and its value is the difference between the last time node and the initial time node of the time period; Step D2: Obtain the abnormal factors of all abnormal curve time periods through the preliminary analysis model of management capability, and then obtain the average value of all abnormal factors. , by taking the outlier factor wr and its mean Combined with the above, the control management coefficient F2 of the production and preparation environment is obtained: ; S3-3, the specific working process of generating corresponding control signals and prompt signals is as follows: Step E1, first combine the preparation state coefficient F1 and the control management coefficient F2 of the production preparation environment to generate a comprehensive evaluation coefficient PG: ; Wherein, λ1 and λ2 are weight coefficients of the preparation state coefficient F1 and the control management coefficient F2, respectively, and both λ1 and λ2 are greater than 0; It should be noted that the smaller the preparation state coefficient F1 and the control management coefficient F2, the larger the corresponding comprehensive evaluation coefficient PG; Step E2, generate a control signal through the comprehensive evaluation coefficient PG: A standard interval Z1 of the preset comprehensive evaluation coefficient PG is used for comparative analysis. When the comprehensive evaluation coefficient PG exceeds the standard interval Z1, a control signal is generated; Step E3, set up the depth analysis model again and generate the corresponding prompt signal: Obtain comprehensive evaluation coefficients PG under the first laser pulse, the second laser pulse, and the third laser pulse, mark them as PG1, PG2, and PG3 respectively, and then construct a three-dimensional feature change space with PG1, PG2, and PG3 as the X-axis and time T as the Y-axis; At the same time, the standard threshold space of the three-dimensional feature change space is preset in the coordinate system, and then the three-dimensional feature change space and the standard threshold space are compared and analyzed, and their horizontal and vertical screenshots are obtained and compared to generate the total overlap area cmj and the total non-overlap area fmj, and the formula is set: Obtain the passive control factor B0, and then obtain the proportion of the passive control factor B0 in the three-dimensional coordinate system, respectively mark the proportion values ​​under the first laser pulse, the second laser pulse and the third laser pulse as the first proportion, the second proportion and the third proportion, respectively, and then set the thresholds of the first proportion, the second proportion and the third proportion, and compare the proportion values ​​with the preset thresholds: When the first proportion exceeds a preset first proportion threshold, a first prompt signal is generated; When the second proportion exceeds a preset second proportion threshold, a second prompt signal is generated; When the third proportion exceeds a preset third proportion threshold, a third prompt signal is generated; Step E4, adjusting the control management plan after receiving the corresponding prompt signal, and then regulating and managing the production preparation environment through the control signal; S4, after the condition control unit receives the control signal and the prompt signal, it adjusts and refreshes the control management plan accordingly, generates and feeds back the control instruction to the information collection unit, and repeats the above operation to achieve automatic optimization of the control management of the preparation environment of biomass graphene quantum dots produced from agricultural waste.

[0014] In summary of the above technical scheme: the present invention includes an information collection unit, a preprocessing unit, an integrated processing unit and a condition control unit. The information collection unit is first used to collect and regularly monitor the preparation environment information of the production and preparation environment of biomass graphene quantum dots produced from agricultural waste, and the collected preparation environment information is corrected and cleaned by the preprocessing unit to achieve accuracy and reliability of the information; the preprocessing unit is then used to analyze the preprocessed preparation environment information, and a comprehensive evaluation coefficient is generated by combining the preparation state coefficient and the control management coefficient, and then different control signals and prompt signals are generated for control management, and adjustments are made to the control management scheme, and then automatic optimization of the control management scheme is achieved through a feedback loop.

[0015] The size of the interval and threshold is set for the convenience of comparison. The size of the threshold depends on the amount of sample data and the number of bases set by technicians in this field for each group of sample data; as long as it does not affect the proportional relationship between the parameter and the quantized value; The above formulas are all dimensionless and numerical calculations. The formula is a formula obtained by collecting a large amount of data and performing software simulation to obtain the most recent real situation. The preset parameters in the formula are set by technicians in this field according to actual conditions. If the functions are implemented in the form of software function modules and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art, or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions for enabling a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the methods described in the various embodiments of the present application. The aforementioned storage medium includes: various media that can store program codes, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk. The above description is only a preferred specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any technician familiar with the technical field can make equivalent replacements or changes according to the technical scheme and inventive concept of the present invention within the technical scope disclosed by the present invention, which should be covered by the protection scope of the present invention.

Claims

1. A control system for producing biomass graphene quantum dots based on agricultural waste, characterized by: It includes an information collection unit, a preprocessing unit, a comprehensive processing unit and a condition control unit, and the information collection unit, the preprocessing unit, the comprehensive processing unit and the condition control unit are communicatively connected; First, a preliminary control and management plan is set up for the production and preparation environment of biomass graphene quantum dots produced from agricultural waste, and the preparation environment information A of the preparation environment is collected through the information collection unit, and a collection cycle is set to perform regular collection and detection of the preparation environment information A; The preprocessing unit performs data correction and cleaning operations on the collected preparation environment information A, then outputs the preprocessed preparation environment information B, and sends it to the comprehensive processing unit; The comprehensive processing unit analyzes the preparation environment information B, obtains the comprehensive evaluation coefficient and generates corresponding control signals and prompt signals to the condition control unit; wherein the comprehensive processing unit includes a preparation analysis module, a control evaluation module and a comprehensive analysis module; The preparation state coefficient is generated through the preparation analysis module, the control management coefficient is generated through the control evaluation module, and the comprehensive analysis module generates a comprehensive evaluation coefficient by combining the preparation state coefficient and the control management coefficient, and generates a control signal and a prompt signal; After receiving the control signal and the prompt signal, the condition control unit adjusts and refreshes the control management plan accordingly, generates and feeds back the control instruction to the information collection unit, and repeats the above operation to realize the automatic optimization of the control management of the preparation environment of biomass graphene quantum dots produced from agricultural waste.

2. The control system for producing biomass graphene quantum dots based on agricultural waste according to claim 1 is characterized in that: The specific working process of the information collection unit collecting the preparation environment information A of the preparation environment is as follows: Step A1, establishing a three-dimensional spatial model of the production preparation environment, dividing the preparation environment into N areas, marking any area as Ni, and collecting and obtaining corresponding preparation environment information A in the area Ni after using the first laser pulse, the second laser pulse and the third laser pulse; Step A2, setting a collection period T1, regularly collecting and monitoring the preparation environment information A, the preparation environment information A including the particle size, ID / IG ratio and oxygen content of the biomass graphene quantum dots; Among them, the morphology of biomass graphene quantum dots is set to have nf kinds of crystal phase structures, any crystal phase structure is marked as j, and the particle size of the crystal phase structure j is marked as cj; the functional surface groups are set to have ns kinds, any functional surface group is marked as r, and the oxygen content is marked as Hr; and the ID / IG ratio is marked as V.

3. The control system for producing biomass graphene quantum dots based on agricultural waste according to claim 1 is characterized in that: The specific process of the preprocessing unit is: Step B1, construct information preprocessing model: Step B1-1, setting and marking a set of preparation environment information A Qa={particle size C, ID / IG ratio V, oxygen content H}; Step B1-11, input any element in the set Qa, set the measurement period T2, and the number of acquisitions within the measurement period T2 is nc; then obtain the values ​​of nc elements within the measurement period, and establish a two-dimensional curve graph Sc with the acquisition period T1 as the horizontal coordinate and the value of the current element as the vertical coordinate; at the same time, preset the threshold curve graph Se of the two-dimensional curve graph Sc in the coordinate system, and then calculate the distance value jl of the corresponding point of the current element between the two-dimensional curve graph Sc and the threshold curve graph Se; Step B1-12, set the difference range ±wc%, when the distance value jl corresponding to any element exceeds the set difference range ±wc%, obtain the coordinate points of the element on the two-dimensional curve graph Sc and the threshold curve graph Se, and mark them as coordinate point zb1 and coordinate point zb2 respectively; then calculate the average value of the ordinates of coordinate point zb1 and coordinate point zb2 to obtain the corrected coordinate JZj, and replace the value of coordinate point zb1 with the corrected coordinate JZj; Step B2, applying the information preprocessing model: substituting the elements in the set Qa of the preparation environment information A into the information preprocessing model in sequence, correcting and cleaning the preparation environment information A, and then outputting the preparation environment information B.

4. The control system for producing biomass graphene quantum dots based on agricultural waste according to claim 1, characterized in that: The specific working process of generating the preparation state coefficient through the preparation analysis module is as follows: Step C1, constructing a preliminary analysis model for the preparation status: marking the input information as a set Qb, where there are M0 elements in the set Qb, marking the two-dimensional change curve corresponding to any element as Sr, and then obtaining the data Cm of the end point of the curve; and assigning a corresponding weight coefficient to the data Cm to obtain the corresponding influence coefficient; Step C2, applying the preliminary analysis model of the preparation state: after the preprocessing operation, a two-dimensional curve graph of the preparation environment information B of N areas in the production preparation environment is obtained; and then the preparation environment information B using the first laser pulse, the second laser pulse and the third laser pulse are sequentially substituted into the preliminary analysis model of the preparation state to obtain the corresponding particle size terminal influence coefficient, ID / IG ratio terminal influence coefficient and oxygen content terminal influence coefficient respectively; Step C3, assigning corresponding normalization coefficients to the particle size terminal influence coefficient, ID / IG ratio terminal influence coefficient and oxygen content terminal influence coefficient in the Ni region, and establishing a formula to obtain the regional state coefficient QYi of the region Ni; Then, the average value of the N regional state coefficients QYi is calculated. , and then calculate the variance to obtain the coefficient of difference Cy, and compare the coefficient of difference Cy with the mean value Combined, the preparation state coefficient F1 of the production preparation environment is obtained.

5. The control system for producing biomass graphene quantum dots based on agricultural waste according to claim 1, characterized in that: The specific working process of generating control management coefficients through the control evaluation module is as follows: Step D1: Construct a preliminary analysis model for management evaluation: Establish a change curve of the preparation state coefficient F1-time T, preset a standard range of the preparation state coefficient F1, and when the preparation state coefficient F1 exceeds the standard range, mark the time node that exceeds the value as an abnormal time point; set the historical period Tx, extract two time series from the historical period Tx, and calculate and generate the abnormal curve time period, and then sort them according to the time series and mark them as T1, T2, T3, ..., Tn; where the time series represents the dynamic change amount, and n is the number of abnormal times; The abnormal curve time period is divided into equal parts and marked as dm, where dm is a dynamic value. When it has a remainder, it is dm+1, and dm is a positive integer; When it is dm, the corresponding abnormal curve time period is set, and the corresponding abnormal factor wr is calculated by setting the formula; When it is the abnormal curve time period corresponding to dm+1, set the formula to calculate the corresponding abnormal factor wr; Step D2: Obtain the abnormal factors of all abnormal curve time periods through the preliminary analysis model of management capability, and then obtain the average value of all abnormal factors. , by taking the outlier factor wr and its mean Combined with the above, the control management coefficient F2 of the production and preparation environment is obtained.

6. The control system for producing biomass graphene quantum dots based on agricultural waste according to claim 1, characterized in that: The specific working process of generating corresponding control signals and prompt signals is as follows: Step E1, firstly combine the preparation state coefficient F1 and the control management coefficient F2 of the production preparation environment to generate a comprehensive evaluation coefficient PG; Step E2, generate a control signal through the comprehensive evaluation coefficient PG: A standard interval Z1 of the preset comprehensive evaluation coefficient PG is used for comparative analysis. When the comprehensive evaluation coefficient PG exceeds the standard interval Z1, a control signal is generated; Step E3, set up the depth analysis model again and generate the corresponding prompt signal: Obtain comprehensive evaluation coefficients PG under the first laser pulse, the second laser pulse, and the third laser pulse, mark them as PG1, PG2, and PG3 respectively, and then construct a three-dimensional feature change space with PG1, PG2, and PG3 as the X-axis and time T as the Y-axis; At the same time, a standard threshold space of the three-dimensional feature change space is preset in the coordinate system, and then the three-dimensional feature change space and the standard threshold space are compared and analyzed, and the horizontal and vertical screenshots thereof are obtained and compared to generate the total overlapping area cmj and the total non-overlapping area fmj, and a formula is set to obtain the passive control factor B0, and then the proportion values ​​of the passive control factor B0 in the three-dimensional coordinate system are obtained, and the proportion values ​​under the first laser pulse, the second laser pulse and the third laser pulse are marked as the first proportion, the second proportion and the third proportion, respectively, and then the thresholds of the first proportion, the second proportion and the third proportion are set respectively, and when the proportion value exceeds the preset threshold value, different prompt signals are generated; Step E4, after receiving the corresponding prompt signal, the control management plan is adjusted, and then the production preparation environment is regulated and managed through the control signal.

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