Intestinal tract risk assessment method and system and storage medium

Abstract

The invention provides an intestinal tract risk assessment method and system and a storage medium, and the method comprises the steps: firstly obtaining basic biological information, a drug type and a drug dosage, predicting the total defecation times needed for gut purge, and carrying out the prediction of the total defecation times at different time nodes before and after the preoperative intestinal tract preparation of a patient; detecting markers such as indole concentration, short-chain fatty acid concentration, calprotectin and the like which reflect intestinal flora function states and mucous membrane function states, and analyzing a static risk index, a dynamic risk index and a risk disturbance rate based on the data; and further calculating and analyzing a risk assessment coefficient according to the static risk index, the dynamic risk index and the risk disturbance rate, and finally outputting a risk assessment level based on the risk assessment coefficient. Compared with the prior art, the technical scheme has the beneficial effects that the medicine action effect is judged in time, so that the technical purposes of protecting the intestinal microecosystem and fully cleaning the intestinal tract are achieved.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of medical information technology processing, and relates to an intestinal tract risk assessment method, system and storage medium for assisting in preoperative intestinal tract cleaning. BACKGROUND

[0002] Colorectal surgery and endoscopy are indeed two core and complementary important means for evaluating and treating digestive tract diseases, especially colorectal diseases. The common point of the two is that patients need to undergo preoperative intestinal tract preparation, that is, the patient is prompted to clear the intestinal contents in a short time through oral laxatives and the like, so as to improve the operation field of view and reduce the risk of infection. In the prior art, the patient needs to take the medicine in advance for a certain period of time, and the intestinal tract is cleaned through multiple excretions.

[0003] In the process of cleaning the intestinal tract, laxatives can help patients to remove intestinal debris after playing a role, but can have a significant impact on the intestinal micro-ecology. Excessive cleaning can lead to intestinal flora disorder and micro-ecological imbalance, damage the intestinal mucosal barrier, weaken the natural barrier function of the intestinal tract to pathogens, increase the risk of infection, and induce intestinal inflammatory response.

[0004] At present, there is still a lack of an index system and tool that can dynamically quantify the stability of the intestinal micro-ecology to guide the individualized adjustment of the preoperative preparation scheme. In other words, the doctor cannot accurately assess the tolerance of the intestinal flora of a certain patient to the cleaning measures and the vulnerability of the mucosal barrier before the operation. The prior art urgently needs to provide an intestinal tract risk assessment scheme to strike a balance between fully cleaning the intestinal tract and doing the best to protect the intestinal micro-ecosystem. SUMMARY

[0005] The technical problem to be solved by the present application is to provide an intestinal tract risk assessment method, system and storage medium based on data acquisition and processing to reflect the functional state of the intestinal flora and the functional state of the mucosa, which can fully clean the intestinal tract while protecting the intestinal micro-ecosystem, in view of the above defects of the prior art.

[0006] The technical solution adopted by the present application to solve the technical problem is as follows:

[0007] An intestinal tract risk assessment method, comprising the following steps:

[0008] S1. Obtain the basic biological information of the patient and the type and dose of the required drug, and match the corresponding intestinal cleaning required defecation total number N of the patient in the preset intestinal preparation database combined with the basic biological information, drug type and drug dose, wherein the basic biological information includes the age and weight of the patient;

[0009] S2. Collect the original indole concentration Original short-chain fatty acid concentration the original indole concentration the original indole concentration the original short-chain fatty acid concentration the original short-chain fatty acid concentration the original calprotectin concentration

[0010] S3. the original indole concentration the original short-chain fatty acid concentration the original calprotectin concentration a static indole risk parameter from the preset intestinal preparation database analysis a static short-chain fatty acid risk parameter and a static calprotectin risk parameter

[0011] S4. a static risk index GDI(t0) is calculated according to the static indole risk parameter, the static short-chain fatty acid risk parameter and the static calprotectin risk parameter;

[0012] S5. a dynamic indole concentration a dynamic short-chain fatty acid concentration a dynamic calprotectin concentration the dynamic indole concentration the dynamic short-chain fatty acid concentration the dynamic calprotectin concentration the dynamic calprotectin concentration

[0013] S6. a dynamic indole risk parameter is analyzed according to the original indole concentration the original short-chain fatty acid concentration the original calprotectin concentration the dynamic indole concentration a dynamic short-chain fatty acid risk parameter and a dynamic calprotectin risk parameter a dynamic short-chain fatty acid risk parameter and a dynamic calprotectin risk parameter

[0014] S7. a dynamic risk index GDI(tN-1) is calculated according to the static indole risk parameter the static short-chain fatty acid risk parameter the static calprotectin risk parameter the dynamic indole risk parameter a dynamic short-chain fatty acid risk parameter ​and dynamic calprotectin risk parameters calculating a dynamic risk index GDI(t1);

[0015] S8. According to the static indole risk parameter static short-chain fatty acid risk parameter static calprotectin risk parameter dynamic indole risk parameter dynamic short-chain fatty acid risk parameter and dynamic calprotectin risk parameters calculating a risk disturbance rate AGDI rate ;

[0016] S9. According to the static risk index GDI(t0), the dynamic risk index GDI(t1) and the risk disturbance rate AGDI rate , according to GDI final = γ1GDI(t0) + γ2GDI(t1) + γ3AGDI rate calculating an analysis risk assessment coefficient;

[0017] S10. Outputting a risk assessment level based on the risk assessment coefficient.

[0018] Compared with the prior art, the beneficial effects of the technical scheme are: detecting the indole concentration, short-chain fatty acid concentration and calprotectin and other markers reflecting the functional state of intestinal flora and the functional state of mucosa at different time nodes before and after the patient's preoperative intestinal preparation, and analyzing the static risk index, dynamic risk index and risk disturbance rate based on the above data, judging the drug effect in time, so as to realize the technical purpose of protecting the intestinal microecosystem and fully cleaning the intestinal tract.

[0019] Further, in step S3, in combination with the original indole concentration Indole T0 , the original short-chain fatty acid concentration SCFA T0 and the original calprotectin concentration Cal T0 , according to the preset intestinal preparation database, the static indole risk parameter static short-chain fatty acid risk parameter and static calprotectin risk parameter are analyzed and obtained. Specifically, the following steps are included:

[0020] S301. Obtaining the indole concentration average μ indole , the short-chain fatty acid concentration average μ SCFA and the calprotectin concentration average μ Cal from the preset intestinal preparation database;

[0021] S302. Based on the preset intestinal preparation database, the indole concentration standard deviation σindole standard deviation σ of short-chain fatty acid concentration SCFA and standard deviation μ of calprotectin concentration Cal ;

[0022] S303. Calculate a static indole risk parameter wherein

[0023] S304. Calculate a static short-chain fatty acid risk parameter wherein,

[0024] S305. Calculate a static calprotectin risk parameter wherein,

[0025] In step S6, a dynamic indole risk parameter a dynamic short-chain fatty acid risk parameter and a dynamic calprotectin risk parameter are analyzed according to the original indole concentration the original short-chain fatty acid concentration and the original calprotectin concentration and the dynamic indole concentration the dynamic short-chain fatty acid concentration and the dynamic calprotectin concentration Specifically comprises the following steps:

[0026] S601. Calculate a dynamic indole risk parameter wherein,

[0027] S602. Calculate a dynamic short-chain fatty acid risk parameter wherein,

[0028] S603. Calculate a dynamic calprotectin risk parameter wherein,

[0029] The beneficial effects of the above scheme are: the clinical-based intestinal preparation database can reflect the marker data of different patients from the perspective of big data, calculate the static indole risk parameter, the static short-chain fatty acid risk parameter and the static calprotectin risk parameter through the indole concentration average value, the short-chain fatty acid concentration average value, the calprotectin concentration average value, the indole concentration standard deviation, the short-chain fatty acid concentration standard deviation and the calprotectin concentration standard deviation, and further calculate the dynamic indole risk parameter, the dynamic short-chain fatty acid risk parameter and the dynamic calprotectin risk parameter, and the intestinal condition of the patient before and after taking the medicine is reflected through data, which prepares for subsequent data processing.

[0030] Further, in step S4, calculating the static risk index specifically comprises the following steps:

[0031] S401. Constructing a static characteristic risk model;

[0032] S402. Obtaining static characteristic risk coefficients a1, a2 and a3, wherein a1+a2+a3=1;

[0033] S403. Based on the static characteristic risk model and the static characteristic risk coefficients a1, a2 and a3, analyzing the static indole risk parameter the static short-chain fatty acid risk parameter and the static calprotectin risk parameter to obtain a static risk index.

[0034] The beneficial effect of the above scheme is that after determining the static characteristic risk coefficients a1, a2 and a3 based on the static characteristic risk model, the static risk index is analyzed, and on this basis, the risk of the intestinal tract of the patient before taking the drug can be evaluated through the static indole risk parameter, the static short-chain fatty acid risk parameter and the static calprotectin risk parameter.

[0035] Further, in step S7, calculating the dynamic risk index GDI(t1) specifically comprises the following steps:

[0036] S701. Constructing a dynamic characteristic risk model;

[0037] S702. Obtaining dynamic characteristic risk coefficients b1, b2 and b3, wherein b1+b2+b3=1;

[0038] S703. Based on the dynamic characteristic risk model and the dynamic characteristic risk coefficients b1, b2 and b3, analyzing the dynamic indole risk parameter the dynamic short-chain fatty acid risk parameter and the dynamic calprotectin risk parameter to obtain a dynamic risk index.

[0039] The beneficial effect of the above scheme is that after determining the dynamic characteristic risk coefficients b1, b2 and b3 based on the dynamic characteristic risk model, the dynamic risk index is analyzed, and on this basis, the risk of the intestinal tract of the patient after taking the drug can be evaluated through the dynamic indole risk parameter, the dynamic short-chain fatty acid risk parameter and the dynamic calprotectin risk parameter.

[0040] Further, in step S8, calculating the risk disturbance rate AGDI rate Specifically comprising the following steps:

[0041] S801. Obtain risk disturbance coefficients γ1, γ2 and γ3, wherein γ1+γ2+γ3=1;

[0042] S802. Obtain static indole risk parameters static short-chain fatty acid risk parameters and static calprotectin risk parameters

[0043] S803. Obtain dynamic indole risk parameters dynamic short-chain fatty acid risk parameters and dynamic calprotectin risk parameters

[0044] S804. Based on risk disturbance coefficients γ1, γ2 and γ3, calculate risk disturbance rates according to static indole risk parameters static short-chain fatty acid risk parameters static calprotectin risk parameters dynamic indole risk parameters dynamic short-chain fatty acid risk parameters and dynamic calprotectin risk parameters wherein T0 is the timestamp corresponding to the collection of the original indole concentration original short-chain fatty acid concentration and original calprotectin concentration T1 is the timestamp corresponding to the collection of dynamic indole concentration dynamic short-chain fatty acid concentration and dynamic calprotectin concentration .

[0045] The beneficial effects of the above scheme are: based on static indole risk parameters, static short-chain fatty acid risk parameters, static calprotectin risk parameters, dynamic indole risk parameters, dynamic short-chain fatty acid risk parameters and dynamic calprotectin risk parameters, the data before and after taking the medicine are summarized and analyzed to obtain the risk disturbance rate, and the effect of the medicine on the intestinal tract of the patient is parameterized, which more directly reflects the risk disturbance change of the intestinal tract of the patient before and after taking the medicine.

[0046] Correspondingly, an intestinal tract risk assessment system comprises:

[0047] an intestinal tract cleaning frequency prediction module, configured to obtain basic biological information of a patient and a type and a dose of a required medicine to be taken by the patient, and match a preset intestinal tract preparation database to obtain a total number N of defecations required for cleaning the intestinal tract of the patient in combination with the basic biological information, the type and the dose of the medicine, wherein the basic biological information comprises an age and a weight of the patient.

[0048] ​raw indole concentration raw short chain fatty acid concentration raw calprotectin concentration the raw indole concentration the raw short chain fatty acid concentration the raw calprotectin concentration the raw calprotectin concentration

[0049] static risk parameter analysis module, configured to analyze the raw indole concentration raw short chain fatty acid concentration raw calprotectin concentration a static indole risk parameter obtained according to a preset intestinal preparation database analysis a static short chain fatty acid risk parameter and a static calprotectin risk parameter

[0050] static risk index calculation module, configured to calculate a static risk index GDI(t0) according to the static indole risk parameter, the static short chain fatty acid risk parameter and the static calprotectin risk parameter

[0051] dynamic data acquisition module, configured to acquire a dynamic indole concentration dynamic short chain fatty acid concentration dynamic calprotectin concentration the dynamic indole concentration the dynamic short chain fatty acid concentration the dynamic calprotectin concentration the dynamic calprotectin concentration

[0052] dynamic risk parameter analysis module, configured to analyze the raw indole concentration raw short chain fatty acid concentration raw calprotectin concentration dynamic indole concentration dynamic short chain fatty acid concentration and dynamic calprotectin concentration a dynamic indole risk parameter obtained according to the raw indole concentration a dynamic short chain fatty acid risk parameter and a dynamic calprotectin risk parameter

[0053] dynamic risk index calculation module, configured to calculate a dynamic risk index GDI(tN-1) according to the static indole risk parameter Static indole risk parameter Static calprotectin risk parameter Dynamic indole risk parameter Dynamic short-chain fatty acid risk parameter and dynamic calprotectin risk parameter calculating a dynamic risk index GDI(t1);

[0054] a risk perturbation rate calculation module configured to calculate a risk perturbation rate AGDI Static indole risk parameter Static calprotectin risk parameter Dynamic indole risk parameter Dynamic short-chain fatty acid risk parameter and dynamic calprotectin risk parameter calculating a risk perturbation rate AGDI rate ;

[0055] a risk coefficient evaluation module configured to calculate an analysis risk evaluation coefficient according to the static risk index GDI(t0), the dynamic risk index GDI(t1) and the risk perturbation rate AGDI rate , according to GDI final = γ1GDI(t0) + γ2GDI(t1) + γ3AGDI rate ;

[0056] a risk level output module configured to output a risk evaluation level based on the risk evaluation coefficient.

[0057] Further, the static risk parameter analysis module comprises:

[0058] a mean value acquisition unit configured to acquire an indole concentration mean value μ indole , a short-chain fatty acid concentration mean value μ SCFA and a calprotectin concentration mean value μ Cal from a preset intestinal preparation database;

[0059] a standard deviation calculation unit configured to calculate an indole concentration standard deviation σ indole , a short-chain fatty acid concentration standard deviation σ SCFA and a calprotectin concentration standard deviation σ Cal based on the preset intestinal preparation database analysis;

[0060] a static indole risk parameter calculation unit configured to calculate a static indole risk parameter wherein

[0061] a static short-chain fatty acid risk parameter calculation unit configured to calculate a static short-chain fatty acid risk parameter wherein,

[0062] a static calprotectin risk parameter calculation unit configured to calculate a static calprotectin risk parameter wherein,

[0063] Further, the dynamic risk parameter analysis module comprises:

[0064] a dynamic indole risk parameter calculation unit configured to calculate a dynamic indole risk parameter wherein,

[0065] a dynamic short-chain fatty acid risk parameter calculation unit configured to calculate a dynamic short-chain fatty acid risk parameter wherein,

[0066] a dynamic calprotectin risk parameter calculation unit configured to calculate a dynamic calprotectin risk parameter wherein,

[0067] Further, the static risk index calculation module comprises:

[0068] a static feature risk model construction unit configured to construct a static feature risk model;

[0069] a static feature risk coefficient acquisition unit configured to acquire static feature risk coefficients a1, a2 and a3, wherein a1+a2+a3=1;

[0070] a static risk index analysis unit configured to obtain a static risk index based on the static feature risk model and the static feature risk coefficients a1, a2 and a3, according to a static indole risk parameter a static short-chain fatty acid risk parameter and a static calprotectin risk parameter The dynamic risk index calculation module comprises:

[0071] a dynamic feature risk model construction unit configured to construct a dynamic feature risk model;

[0072] a dynamic feature risk coefficient acquisition unit configured to acquire dynamic feature risk coefficients b1, b2 and b3, wherein b1+b2+b3=1;

[0073] a dynamic risk index analysis unit configured to obtain a dynamic risk index based on the dynamic feature risk model and the dynamic feature risk coefficients b1, b2 and b3, according to a dynamic indole risk parameter

[0074] a dynamic short-chain fatty acid risk parameter ​and dynamic calprotectin risk parameters The dynamic risk index is obtained by analysis

[0075] Further, the risk disturbance rate calculation module comprises:

[0076] a risk disturbance coefficient acquisition unit configured to acquire risk disturbance coefficients γ1, γ2 and γ3, wherein γ1+γ2+γ3=1;

[0077] a static risk disturbance coefficient acquisition unit configured to acquire a static indole risk parameter a static short-chain fatty acid risk parameter and a static calprotectin risk parameter

[0078] a dynamic risk disturbance coefficient acquisition unit configured to acquire a dynamic indole risk parameter a dynamic short-chain fatty acid risk parameter and a dynamic calprotectin risk parameter

[0079] a risk disturbance rate calculation unit configured to calculate a risk disturbance rate based on the risk disturbance coefficients γ1, γ2 and γ3, according to the static indole risk parameter a static short-chain fatty acid risk parameter a static calprotectin risk parameter a dynamic indole risk parameter a dynamic short-chain fatty acid risk parameter and a dynamic calprotectin risk parameter wherein T0 is the time stamp corresponding to the acquisition of the original indole concentration the original short-chain fatty acid concentration and the original calprotectin concentration T1 is the time stamp corresponding to the acquisition of the dynamic indole concentration the dynamic short-chain fatty acid concentration and the dynamic calprotectin concentration .

[0080] Correspondingly, a storage medium storing a computer program, the computer program comprising program instructions, when the program instructions are executed by a processor, the processor executes the intestinal tract risk assessment method as described above. BRIEF DESCRIPTION OF DRAWINGS

[0081] Figure 1 is a flowchart of the intestinal tract risk assessment method of the present application.

[0082] Figure 2 is a structural schematic diagram of the intestinal tract risk assessment system of the present application.​

[0083] In the drawings, the components shown with reference numerals are listed below:

[0084] Intestinal cleaning frequency prediction module 1, raw data acquisition module 2, static risk parameter analysis module 3, static risk index calculation module 4, dynamic data acquisition module 5, dynamic risk parameter analysis module 6, dynamic risk index calculation module 7, risk disturbance rate calculation module 8, risk coefficient evaluation module 9, risk level output module 10. DETAILED DESCRIPTION

[0085] In order to make the objectives, technical solutions and advantages of the present application clearer and more explicit, the present application is further described in detail below with reference to the drawings and examples. It should be understood that the specific examples described herein are only used to explain the present application and do not limit the present application.

[0086] In the description of the present application, it should be understood that the terms "center", "upper", "lower", "front", "back", "left", "right" and the like indicate the orientation or positional relationship shown in the drawings, and are only for the convenience of describing the present application and simplifying the description, and therefore cannot be understood as indicating or implying that the devices or components referred to must have a particular orientation, be constructed and operated in a particular orientation, and therefore cannot be understood as limiting the present application. In addition, the terms "first" and "second" are only for descriptive purposes and cannot be understood as indicating or implying relative importance.

[0087] In the description of the present application, it should be noted that unless otherwise explicitly specified and limited, the terms "mounting", "connection", "connection" should be understood broadly, for example, it can be fixedly connected, or detachably connected, or integrally connected; it can be mechanically connected, or electrically connected; it can be directly connected, or indirectly connected through an intermediate medium, or it can be connected inside two components. When a component is referred to as "fixed to" or "disposed on" another element, it can be directly on another component or there can be a middle component. When a component is considered to be "connected" to another element, it can be directly connected to another element or there can be a middle element. For those skilled in the art, the specific meanings of the above terms in the present application can be understood according to the specific circumstances.

[0088] Colorectal surgery and endoscopy are indeed two core and complementary important means for evaluating and treating digestive tract diseases, especially colorectal diseases. The common point of the two is that the patient needs to be prepared for surgery, that is, the patient is prompted to clear the intestinal contents in a short time through oral laxatives and the like, in order to improve the operation field of view and reduce the risk of infection. In the prior art, the patient needs to take the medicine in advance for a certain period of time, and the intestinal cleaning is achieved through multiple excretions.

[0089] During the process of cleaning the intestines, after the laxative takes effect, it can help the patient to remove the intestinal sundries, but it can have a significant impact on the intestinal micro-ecology. Excessive cleaning can lead to intestinal flora disorder and micro-ecological imbalance, damage the intestinal mucosal barrier, weaken the natural barrier function of the intestinal tract to pathogens, increase the risk of infection, and induce intestinal inflammatory response.

[0090] Currently, there is still a lack of an index system and tool that can dynamically quantify the stability of intestinal micro-ecology to guide the individualized adjustment of preoperative preparation scheme. In other words, doctors cannot accurately assess the tolerance of a patient's intestinal flora to cleaning measures and the vulnerability of the mucosal barrier before surgery. The existing technology urgently needs to provide an intestinal risk assessment scheme to strike a balance between thoroughly cleaning the intestines and doing the best to protect the intestinal micro-ecosystem.

[0091] As shown in Figure 1 To solve the above technical problems, the technical solution provides an intestinal risk assessment method, comprising the following steps:

[0092] S1. Obtain the basic biological information of the patient and the type and dose of the required drug, and match the corresponding intestinal cleaning required defecation total number N of the patient in the preset intestinal preparation database combined with the basic biological information, drug type and drug dose. The basic biological information includes the age and weight of the patient. Cleaning the intestines requires taking laxatives, and after the laxatives take effect, multiple defecations are required until the excrement is clear to achieve the purpose of cleaning the intestines. In the clinical process, different ages and weights of patients use different drug types and drug doses, and the corresponding number of defecations required to achieve the purpose of cleaning the intestines. In the preset intestinal preparation database of the technical solution, these information is associated, that is, only the basic biological information, drug type and drug dose need to be determined to predict the total number of defecations required for intestinal cleaning N. In addition, a large amount of clinical historical data is recorded in the intestinal preparation database. Based on these clinical historical data, the average value of indole concentration, the average value of short-chain fatty acid concentration, the average value of calprotectin concentration, the standard deviation of indole concentration, the standard deviation of short-chain fatty acid concentration and the standard deviation of calprotectin concentration can be analyzed in the subsequent steps. Step S1 predicts the total number of defecations required for intestinal cleaning by a large number of clinical samples combined with the basic biological information, drug type and drug dose of the individual.

[0093] S2. Collect the original indole concentration The original short-chain fatty acid concentration And the original calprotectin concentration The original indole concentration is the indole concentration before taking the drug, the original short-chain fatty acid concentration is the short-chain fatty acid concentration before taking the drug, and the original calprotectin concentration The original indole concentration is the indole concentration before taking the medicine. The original short-chain fatty acid concentration is the short-chain fatty acid concentration before taking the medicine. The original calprotectin concentration is the calprotectin concentration before taking the medicine. The dynamic indole concentration is the indole concentration corresponding to the N-1st defecation after taking the medicine. The dynamic short-chain fatty acid concentration is the short-chain fatty acid concentration corresponding to the N-1st defecation after taking the medicine. The dynamic calprotectin concentration is the calprotectin concentration corresponding to the N-1st defecation after taking the medicine. The original short-chain fatty acid concentration The original calprotectin concentration The dynamic indole concentration The dynamic short-chain fatty acid concentration

[0094] The dynamic calprotectin concentration According to the preset intestinal preparation database analysis, the static indole risk parameter is obtained The static short-chain fatty acid risk parameter The static calprotectin risk parameter According to the preset intestinal preparation database analysis, the static indole risk parameter is obtained The static short-chain fatty acid risk parameter The static calprotectin risk parameter

[0095] S4. According to the static indole risk parameter, the static short-chain fatty acid risk parameter, and the static calprotectin risk parameter, the static risk index GDI(t0) is calculated. The Gut Dysbiosis Index (GDI) can quantify the risk of microecological disorder. In the technical solution, by calculating the static risk index GDI(t0) before taking the medicine, the intestinal microecological fluctuation risk before taking the medicine can be classified.

[0096] S5. The dynamic indole concentration is collected The dynamic short-chain fatty acid concentration The dynamic calprotectin concentration The dynamic indole concentration The dynamic short-chain fatty acid concentration The dynamic calprotectin concentration The dynamic calprotectin concentration The dynamic short-chain fatty acid concentration The dynamic calprotectin concentration data, thus reflecting the intestinal microecological condition of the patient after taking the drug.

[0097] S6. According to the original indole concentration Original short-chain fatty acid concentration Original calprotectin concentration Dynamic indole concentration Dynamic short-chain fatty acid concentration and dynamic calprotectin concentration Analysis of dynamic indole risk parameters Dynamic short-chain fatty acid risk parameters and dynamic calprotectin risk parameters

[0098] S7. According to the static indole risk parameter Static short-chain fatty acid risk parameter Static calprotectin risk parameter Dynamic indole risk parameter Dynamic short-chain fatty acid risk parameter and dynamic calprotectin risk parameter Calculate the dynamic risk index GDI(t1). In step S4, by calculating the static risk index GDI(t0) before taking the drug, the risk of microecological fluctuation during preoperative bowel preparation can be graded, and correspondingly, in step S7, by calculating the dynamic risk index GDI(t1) after taking the drug, the risk of intestinal microecological fluctuation after taking the drug can be graded.

[0099] S8. According to the static indole risk parameter Static short-chain fatty acid risk parameter Static calprotectin risk parameter Dynamic indole risk parameter Dynamic short-chain fatty acid risk parameter and dynamic calprotectin risk parameter Calculate the risk disturbance rate AGDI rate . Through data acquisition and data analysis, the static risk index GDI(t0) has been obtained in step S4, and the dynamic risk index GDI(t1) has been obtained in step S7. Further, in step S8, the risk disturbance rate is calculated by the static indole risk parameter, the static short-chain fatty acid risk parameter, the static calprotectin risk parameter, the dynamic indole risk parameter, the dynamic short-chain fatty acid risk parameter and the dynamic calprotectin risk parameter collected before, which parameterizes the effect of the drug on the patient's intestine, and more directly reflects the risk disturbance change of the patient's intestine before and after taking the drug.

[0100] S9. Calculate the risk assessment coefficient according to the static risk index GDI(t0), the dynamic risk index GDI(t1) and the risk disturbance rate AGDI rate , according to GDI final = γ1GDI(t0) + γ2GDI(t1) + γ3AGDI rate Calculate the risk assessment coefficient. In step S9, the static risk index GDI(t0), the dynamic risk index GDI(t1) and the risk disturbance rate AGDI rate , the risk assessment coefficient can be calculated, so as to determine the microecological condition of the intestinal tract of the patient after N-1 defecations.

[0101] S10. Output the risk assessment level based on the risk assessment coefficient. GDI final less than 30 is low risk, GDI final between 30 and 60 is medium risk, GDI final greater than 60 is high risk.

[0102] Based on the above technical solution, the indole concentration, short-chain fatty acid concentration and calprotectin and other markers reflecting the functional state of intestinal flora and the functional state of the mucosa are detected at different time nodes before and after the preoperative intestinal preparation of the patient, and the static risk index, the dynamic risk index and the risk disturbance rate are analyzed based on the above data, the effect of the drug is judged in time, so as to realize the technical purpose of protecting the intestinal microecosystem and thoroughly cleaning the intestinal tract. On the one hand, the technical solution can fill the gap of preoperative microecological evaluation, helping doctors to discover high-risk microecological imbalance tendency in time before operation; on the other hand, the technical solution is in line with the concept of precision medicine and rapid recovery, and the incidence of postoperative complications such as infection is reduced by protecting the ecological balance of the intestinal tract.

[0103] It should be particularly pointed out that the technical solution focuses on the data collection and data processing process to assist in realizing the protection of the intestinal microecosystem and ensuring the cleaning effect of the intestinal tract, and does not belong to "disease diagnosis and treatment method".

[0104] Preferably, in step S3, the original indole concentration The original short-chain fatty acid concentration and the original calprotectin concentration According to the preset intestinal preparation database, the static indole risk parameter The static short-chain fatty acid risk parameter and the static calprotectin risk parameter Specifically includes the following steps:

[0105] S301. Obtain the average value μ indole of indole concentration, the average value μ SCFAand the average value of the calprotectin concentration μ Cal The average value in step S301 is calculated by a large amount of sample data in the clinic, and is not for a specific patient individual.

[0106] S302. Calculate the standard deviation σ of indole concentration based on the preset intestinal preparation database analysis indole , the standard deviation σ of short-chain fatty acid concentration SCFA and the standard deviation σ of calprotectin concentration Cal The standard deviation in step S302 is also calculated by a large amount of sample data in the clinic, as the average value in step S301. Steps S301 and S302 are referenced by clinical big data, thereby preparing for predicting the number of defecations.

[0107] S303. Calculate the static indole risk parameter wherein

[0108] S304. Calculate the static short-chain fatty acid risk parameter wherein,

[0109] S305. Calculate the static calprotectin risk parameter wherein,

[0110] In steps S303-305, by combining the average value and the standard deviation in the preset intestinal preparation database, the static indole risk parameter the static short-chain fatty acid risk parameter and the static calprotectin risk parameter corresponding to a specific patient can be analyzed from the original indole concentration the static short-chain fatty acid risk parameter and the static calprotectin risk parameter It should be noted that the static indole risk parameter, the static short-chain fatty acid risk parameter and the static calprotectin risk parameter are analyzed by integrating a large amount of clinical data, combining the original indole concentration, the original short-chain fatty acid concentration and the original calprotectin concentration before taking the drug, which has guiding significance for the intestinal cleansing scheme, but is not completely accurate, and further judgment is still needed in the subsequent intestinal cleansing process.

[0111] Preferably, in step S6, according to the original indole concentration the original short-chain fatty acid concentration and the original calprotectin concentration and the dynamic indole concentration the dynamic short-chain fatty acid concentration and the dynamic calprotectin concentration Analysis yielded dynamic indole risk parameters. Dynamic short-chain fatty acid risk parameters and dynamic calprotectin risk parameters Specifically, the following steps are included:

[0112] S601. Calculate dynamic indole risk parameters in,

[0113] S602. Calculation of dynamic short-chain fatty acid risk parameters in,

[0114] S603. Calculate dynamic calprotectin risk parameters in,

[0115] In steps 601-603, the initial indole concentration to be used Original Short Chain Fatty Acid Concentration (SCFA) T0 Compared with the original calprotectin concentration The data, such as the dynamic indole concentration, has already been obtained in step S2. Dynamic short-chain fatty acid concentration With dynamic calprotectin concentration Cal T1 The indole concentration average μ obtained in step S5, combined with that obtained in steps S301 and S302, is... indole Average concentration of short-chain fatty acids (μ) SCFA Average concentration of calprotectin (μ) Cal Standard deviation of indole concentration σ indole Standard deviation of short-chain fatty acid concentration σ SCFA and the standard deviation of calprotectin concentration μ Cal The dynamic indole risk parameter can then be calculated. Dynamic short-chain fatty acid risk parameters and dynamic calprotectin risk parameters

[0116] Through the above technical solutions, the clinically-based bowel preparation database can reflect biomarker data of different patients from a big data perspective. By calculating the average indole concentration, average short-chain fatty acid concentration, average calprotectin concentration, and standard deviation of indole concentration, short-chain fatty acid concentration, and calprotectin concentration, static indole risk parameters, static short-chain fatty acid risk parameters, and static calprotectin risk parameters are calculated. Furthermore, dynamic indole risk parameters, dynamic short-chain fatty acid risk parameters, and dynamic calprotectin risk parameters are calculated, thus digitizing the patient's bowel condition before and after medication, preparing for subsequent data processing.

[0117] Preferably, in step S4, the calculation of the static risk index specifically comprises the following steps:

[0118] S401. Construct a static characteristic risk model. In step S401, the process of constructing the static characteristic risk model can be understood as constructing a mathematical model, the static characteristic risk model including a plurality of coefficients and a plurality of parameters, wherein the coefficients and the parameters are one-to-one corresponding. After the static characteristic risk model is constructed, the coefficients and the parameters are determined, and the corresponding static risk index can be obtained.

[0119] S402. Obtain static characteristic risk coefficients α1, α2 and α3, wherein α1+α2+α3=1. The static characteristic risk model includes three static characteristic risk coefficients. In step S402, the static characteristic risk coefficients can be determined through a large amount of sample data, and repeatedly verified and updated in the clinical process.

[0120] S403. Based on the static characteristic risk model and the static characteristic risk coefficients α1, α2 and α3, the static indole risk parameter the static short-chain fatty acid risk parameter and the static calprotectin risk parameter analysis to obtain a static risk index

[0121] Through the above technical solution, the static risk index is analyzed after the static characteristic risk model and the static characteristic risk coefficients α1, α2 and α3 are determined. On this basis, the static indole risk parameter, the static short-chain fatty acid risk parameter and the static calprotectin risk parameter can be used to evaluate the risk of the patient's intestinal tract before taking the drug.

[0122] Preferably, in step S7, the calculation of the dynamic risk index GDI(t1) specifically comprises the following steps:

[0123] S701. Construct a dynamic characteristic risk model;

[0124] S702. Obtain dynamic characteristic risk coefficients β1, β2 and β3, wherein β1+β2+β3=1;

[0125] S703. Based on the dynamic characteristic risk model and the dynamic characteristic risk coefficients β1, β2 and β3, the dynamic indole risk parameter the dynamic short-chain fatty acid risk parameter and the dynamic calprotectin risk parameter analysis to obtain a dynamic risk index

[0126] The process of calculating the dynamic risk index and the idea of calculating the static risk index are the same, and will not be repeated here. Based on the dynamic characteristic risk model and the determination of the dynamic characteristic risk coefficients β1, β2 and β3, the dynamic risk index is analyzed, and on this basis, the dynamic indole risk parameter, the dynamic short-chain fatty acid risk parameter and the dynamic calprotectin risk parameter can be used to evaluate the risk of the intestinal tract of the patient after taking the drug.

[0127] Preferably, in step S8, the risk disturbance rate AGDI is calculated rate Specifically includes the following steps:

[0128] S801. Obtain risk disturbance coefficients γ1, γ2 and γ3, wherein γ1+γ2+γ3=1;

[0129] S802. Obtain static indole risk parameters Static short-chain fatty acid risk parameters And static calprotectin risk parameters

[0130] S803. Obtain dynamic indole risk parameters Dynamic short-chain fatty acid risk parameters And dynamic calprotectin risk parameters

[0131] S804. Based on the risk disturbance coefficients γ1, γ2 and γ3, according to the static indole risk parameters Static short-chain fatty acid risk parameters Static calprotectin risk parameters Dynamic indole risk parameters Dynamic short-chain fatty acid risk parameters And dynamic calprotectin risk parameters Calculate the risk disturbance rate Wherein T0 is the time stamp corresponding to the collection of the original indole concentration The original short-chain fatty acid concentration And the original calprotectin concentration T1 is the time stamp corresponding to the collection of the dynamic indole concentration Dynamic short-chain fatty acid concentration And dynamic calprotectin concentration .

[0132] The process of calculating the risk disturbance rate and the idea of calculating the static risk index are the same, and will not be repeated here. Based on the static indole risk parameter, the static short-chain fatty acid risk parameter, the static calprotectin risk parameter, the dynamic indole risk parameter, the dynamic short-chain fatty acid risk parameter and the dynamic calprotectin risk parameter, the data before and after taking the medicine are summarized and analyzed to obtain the risk disturbance rate, and the effect of the medicine on the intestinal tract of the patient is parameterized, which more directly reflects the risk disturbance change of the intestinal tract of the patient before and after taking the medicine.

[0133] As shown in Figure 2 , a system for evaluating the intestinal tract risk, comprising an intestinal tract cleaning frequency prediction module 1, an original data acquisition module 2, a static risk parameter analysis module 3, a static risk index calculation module 4, a dynamic data acquisition module 5, a dynamic risk parameter analysis module 6, a dynamic risk index calculation module 7, a risk disturbance rate calculation module 8, a risk coefficient evaluation module 9 and a risk level output module 10, specifically:

[0134] The intestinal tract cleaning frequency prediction module 1 is used to obtain the basic biological information of the patient and the type and dose of the medicine to be taken, and combines the basic biological information, the type and dose of the medicine to obtain the total number of defecations N required for cleaning the intestinal tract of the patient from the preset intestinal tract preparation database, wherein the basic biological information includes the age and weight of the patient.

[0135] The original data acquisition module 2 is used to acquire the original indole concentration The original short-chain fatty acid concentration And the original calprotectin concentration The original indole concentration is the indole concentration before taking the medicine, the original short-chain fatty acid concentration is the short-chain fatty acid concentration before taking the medicine, and the original calprotectin concentration is the calprotectin concentration before taking the medicine.

[0136] The static risk parameter analysis module 3 is used to combine the original indole concentration The original short-chain fatty acid concentration And the original calprotectin concentration According to the preset intestinal tract preparation database, the static indole risk parameter The static short-chain fatty acid risk parameter And the static calprotectin risk parameter

[0137] The static risk index calculation module 4 is used to calculate the static risk index GDI(t0) according to the static indole risk parameter, the static short-chain fatty acid risk parameter and the static calprotectin risk parameter.

[0138] Dynamic data acquisition module 5 for acquiring dynamic indole concentration Dynamic short-chain fatty acid concentration Dynamic calprotectin concentration Dynamic indole concentration Dynamic short-chain fatty acid concentration Dynamic calprotectin concentration Dynamic calprotectin concentration

[0139] Dynamic risk parameter analysis module 6 for analyzing the raw indole concentration Raw short-chain fatty acid concentration Raw calprotectin concentration Dynamic indole concentration Dynamic short-chain fatty acid concentration And dynamic calprotectin concentration Dynamic indole risk parameter Dynamic short-chain fatty acid risk parameter And dynamic calprotectin risk parameter

[0140] Dynamic risk index calculation module 7 for calculating the dynamic risk index GDI(t1) according to the static indole risk parameter Static short-chain fatty acid risk parameter Static calprotectin risk parameter Dynamic indole risk parameter Dynamic short-chain fatty acid risk parameter And dynamic calprotectin risk parameter

[0141] Risk disturbance rate calculation module 8 for calculating the risk disturbance rate AGDI Static short-chain fatty acid risk parameter Static calprotectin risk parameter Dynamic indole risk parameter Dynamic short-chain fatty acid risk parameter And dynamic calprotectin risk parameter rate

[0142] Risk coefficient evaluation module 9 for evaluating the static risk index GDI(t0), the dynamic risk index GDI(t1) and the risk disturbance rate AGDI rate According to GDI final = γ1GDI(t0) + γ2GDI(t1) + γ3AGDI rate ​​​calculating and analyzing the risk assessment coefficient;

[0143] a risk level output module 10 for outputting a risk assessment level based on the risk assessment coefficient.

[0144] Preferably, the static risk parameter analysis module comprises:

[0145] an average value acquisition unit for acquiring an average value μ of indole concentration from a preset intestinal preparation database indole , an average value μ of short-chain fatty acid concentration SCFA and an average value μ of calprotectin concentration Cal ;

[0146] a standard deviation calculation unit for calculating a standard deviation σ of indole concentration indole , a standard deviation σ of short-chain fatty acid concentration SCFA and a standard deviation σ of calprotectin concentration Cal based on the preset intestinal preparation database analysis;

[0147] a static indole risk parameter calculation unit for calculating a static indole risk parameter wherein

[0148] a static short-chain fatty acid risk parameter calculation unit for calculating a static short-chain fatty acid risk parameter wherein,

[0149] a static calprotectin risk parameter calculation unit for calculating a static calprotectin risk parameter wherein,

[0150] Preferably, the dynamic risk parameter analysis module comprises:

[0151] a dynamic indole risk parameter calculation unit for calculating a dynamic indole risk parameter wherein,

[0152] a dynamic short-chain fatty acid risk parameter calculation unit for calculating a dynamic short-chain fatty acid risk parameter wherein,

[0153] a dynamic calprotectin risk parameter calculation unit for calculating a dynamic calprotectin risk parameter wherein,

[0154] Preferably, the static risk index calculation module comprises:

[0155] a static feature risk model construction unit for constructing a static feature risk model;

[0156] a static feature risk coefficient acquisition unit, configured to acquire static feature risk coefficients α1, α2, and α3, where α1+α2+α3=1;

[0157] a static risk index analysis unit, configured to analyze a static risk index based on the static feature risk model and the static feature risk coefficients α1, α2, and α3, according to static indole risk parameters a static short-chain fatty acid risk parameter and a static calprotectin risk parameter

[0158] The dynamic risk index calculation module includes:

[0159] a dynamic feature risk model construction unit, configured to construct a dynamic feature risk model;

[0160] a dynamic feature risk coefficient acquisition unit, configured to acquire dynamic feature risk coefficients β1, β2, and β3, where β1+β2+β3=1;

[0161] a dynamic risk index analysis unit, configured to analyze a dynamic risk index based on the dynamic feature risk model and the dynamic feature risk coefficients β1, β2, and β3, according to dynamic indole risk parameters a dynamic short-chain fatty acid risk parameter and a dynamic calprotectin risk parameter

[0162] Preferably, the risk disturbance rate calculation module includes:

[0163] a risk disturbance coefficient acquisition unit, configured to acquire risk disturbance coefficients γ1, γ2, and γ3, where γ1+γ2+γ3=1;

[0164] a static risk disturbance coefficient acquisition unit, configured to acquire static indole risk parameters a static short-chain fatty acid risk parameter and a static calprotectin risk parameter

[0165] a dynamic risk disturbance coefficient acquisition unit, configured to acquire dynamic indole risk parameters a dynamic short-chain fatty acid risk parameter and a dynamic calprotectin risk parameter

[0166] a risk disturbance rate calculation unit, configured to calculate a risk disturbance rate based on the risk disturbance coefficients γ1, γ2, and γ3, according to static indole risk parameters a static short-chain fatty acid risk parameter ​​Static calprotectin risk parameter Dynamic indole risk parameter Dynamic short chain fatty acid risk parameter And dynamic calprotectin risk parameter Calculating the risk perturbation rate Wherein, T0 is the time stamp corresponding to the original indole concentration Original short chain fatty acid concentration And original calprotectin concentration T1 is the time stamp corresponding to the dynamic indole concentration Dynamic short chain fatty acid concentration And dynamic calprotectin concentration Corresponding time stamp.

[0167] Correspondingly, in order to solve the above technical problems, the technical scheme also provides a storage medium, the storage medium stores a computer program, the computer program includes program instructions, when the program instructions are executed by a processor, the processor executes the intestinal tract risk assessment method as described above.

[0168] It should be understood that the application of the present application is not limited to the above examples, and those skilled in the art can make improvements or changes according to the above description, and all these improvements and changes shall belong to the protection scope of the appended claims of the present application.

Claims

1. A method for assessing intestinal risk, characterized in that, Includes the following steps: S1. Obtain the patient's basic biological information and the type and dosage of the required medication. Combine the basic biological information, medication type and dosage with the preset bowel preparation database to obtain the total number of bowel movements N required for the patient's bowel preparation. The basic biological information includes the patient's age and weight. S2. Collect the original indole concentration. Original short-chain fatty acid concentration Compared with the original calprotectin concentration The original indole concentration The indole concentration before medication administration, and the original short-chain fatty acid concentration. The concentration of short-chain fatty acids before drug administration, and the original calprotectin concentration. This refers to the concentration of calprotectin before taking the medication; S3. Combined with the original indole concentration Original short-chain fatty acid concentration Compared with the original calprotectin concentration Static indole risk parameters were obtained based on analysis of a pre-defined bowel preparation database. Static short-chain fatty acid risk parameters and static calprotectin risk parameters S4. Calculate the static risk index GDI(t0) based on the static indole risk parameter, static short-chain fatty acid risk parameter, and static calprotectin risk parameter; S5. Collect dynamic indole concentration. Dynamic short-chain fatty acid concentration With dynamic calprotectin concentration The dynamic indole concentration The indole concentration corresponding to the (N-1)th bowel movement after taking the medication, and the dynamic short-chain fatty acid concentration. The short-chain fatty acid concentration corresponding to the (N-1)th bowel movement after taking the medication, and the dynamic calprotectin concentration. This represents the calprotectin concentration corresponding to the N-1th bowel movement after taking the medication. S6. Based on the original indole concentration Original short-chain fatty acid concentration Original calprotectin concentration Dynamic indole concentration Dynamic short-chain fatty acid concentration and dynamic calprotectin concentration Analysis yielded dynamic indole risk parameters. Dynamic short-chain fatty acid risk parameters and dynamic calprotectin risk parameters S7. Based on static indole risk parameters Static short-chain fatty acid risk parameters Static calprotectin risk parameters Dynamic indole risk parameters Dynamic short-chain fatty acid risk parameters and dynamic calprotectin risk parameters Calculate the dynamic risk index GDI(t1); S8. Based on static indole risk parameters Static short-chain fatty acid risk parameters Static calprotectin risk parameters Dynamic indole risk parameters Dynamic short-chain fatty acid risk parameters and dynamic calprotectin risk parameters Calculate the risk disturbance rate ΔGDI rate ; S9. Based on the static risk index GDI(t0), the dynamic risk index GDI(t1), and the risk disturbance rate ΔGDI rate According to GDI final =γ1GDI(t0)+γ2GDI(t1)+γ3△GDI rate Calculate and analyze risk assessment coefficients; S10. Output the risk assessment level based on the risk assessment coefficient.

2. The intestinal risk assessment method according to claim 1, characterized in that, In step S3, the original indole concentration is considered. Original short-chain fatty acid concentration Compared with the original calprotectin concentration Based on a pre-defined gut preparation database, static indole risk parameters were obtained through analysis. Static short-chain fatty acid risk parameters and static calprotectin risk parameters Specifically, the following steps are included: S301. Obtain the average indole concentration μ from the preset intestinal preparation database. indole Average concentration of short-chain fatty acids (μ) SCFA and the average concentration of calprotectin μ Cal ; S302. Calculate the standard deviation σ of indole concentration based on a pre-set intestinal preparation database. indole Standard deviation of short-chain fatty acid concentration σ SCFA and the standard deviation of calprotectin concentration μ Cal ; S303. Calculate static indole risk parameters in S304. Calculation of static short-chain fatty acid risk parameters in, S305. Calculate static calprotectin risk parameters in, In step S6, based on the original indole concentration Original short-chain fatty acid concentration Compared with the original calprotectin concentration and dynamic indole concentration Dynamic short-chain fatty acid concentration With dynamic calprotectin concentration Analysis yielded dynamic indole risk parameters. Dynamic short-chain fatty acid risk parameters and dynamic calprotectin risk parameters Specifically, the following steps are included: S601. Calculate dynamic indole risk parameters in, S602. Calculation of dynamic short-chain fatty acid risk parameters in, S603. Calculate dynamic calprotectin risk parameters in, 3. The intestinal risk assessment method according to claim 1, characterized in that, In step S4, calculating the static risk index specifically includes the following steps: S401. Construct a static feature risk model; S402. Obtain the static characteristic risk coefficients α1, α2 and α3, where α1+α2+α3=1; S403. Based on the static characteristic risk model and static characteristic risk coefficients α1, α2, and α3, according to the static indole risk parameters... Static short-chain fatty acid risk parameters and static calprotectin risk parameters The analysis yielded a static risk index.

4. The intestinal risk assessment method according to claim 1, characterized in that, In step S7, calculating the dynamic risk index GDI(t1) specifically includes the following steps: S701. Construct a dynamic feature risk model; S702. Obtain the dynamic characteristic risk coefficients β1, β2 and β3, where β1+β2+β3=1; S703. Based on the dynamic characteristic risk model and dynamic characteristic risk coefficients β1, β2, and β3, according to the dynamic indole risk parameter... Dynamic short-chain fatty acid risk parameters and dynamic calprotectin risk parameters Analysis yields dynamic risk index 5. The intestinal risk assessment method according to claim 1, characterized in that, In step S8, the risk disturbance rate ΔGDI is calculated. rate Specifically, the following steps are included: S801. Obtain the risk disturbance coefficients γ1, γ2 and γ3, where γ1+γ2+γ3=1; S802. Obtain static indole risk parameters Static short-chain fatty acid risk parameters and static calprotectin risk parameters S803. Obtain dynamic indole risk parameters Dynamic short-chain fatty acid risk parameters and dynamic calprotectin risk parameters S804. Based on the risk disturbance coefficients γ1, γ2, and γ3, according to the static indole risk parameter Static short-chain fatty acid risk parameters Static calprotectin risk parameters Dynamic indole risk parameters Dynamic short-chain fatty acid risk parameters and dynamic calprotectin risk parameters Calculate the risk disturbance rate Where T0 is the original indole concentration collected. Original short-chain fatty acid concentration Compared with the original calprotectin concentration The corresponding timestamp, T1, represents the collected dynamic indole concentration. Dynamic short-chain fatty acid concentration With dynamic calprotectin concentration The corresponding timestamp.

6. An intestinal risk assessment system, characterized in that, include: The bowel cleansing frequency prediction module is used to obtain the patient's basic biological information and the type and dosage of the required medication. Combining the basic biological information, medication type and dosage, it matches the patient's corresponding total number of bowel movements N for bowel cleansing in a preset bowel preparation database. The basic biological information includes the patient's age and weight. The raw data acquisition module is used to collect raw indole concentrations. Original short-chain fatty acid concentration Compared with the original calprotectin concentration The original indole concentration The indole concentration before medication administration, and the original short-chain fatty acid concentration. The concentration of short-chain fatty acids before drug administration, and the original calprotectin concentration. This refers to the concentration of calprotectin before taking the medication; The static risk parameter analysis module is used to combine the original indole concentration. Original short-chain fatty acid concentration Compared with the original calprotectin concentration Static indole risk parameters were obtained based on analysis of a pre-defined bowel preparation database. Static short-chain fatty acid risk parameters and static calprotectin risk parameters The static risk index calculation module is used to calculate the static risk index GDI(t0) based on the static indole risk parameter, the static short-chain fatty acid risk parameter, and the static calprotectin risk parameter. The dynamic data acquisition module is used to collect dynamic indole concentrations. Dynamic short-chain fatty acid concentration With dynamic calprotectin concentration The dynamic indole concentration The indole concentration corresponding to the (N-1)th bowel movement after taking the medication, and the dynamic short-chain fatty acid concentration. The short-chain fatty acid concentration corresponding to the (N-1)th bowel movement after taking the medication, and the dynamic calprotectin concentration. This represents the calprotectin concentration corresponding to the N-1th bowel movement after taking the medication. The dynamic risk parameter analysis module is used to analyze the original indole concentration. Original short-chain fatty acid concentration Original calprotectin concentration Dynamic indole concentration Dynamic short-chain fatty acid concentration and dynamic calprotectin concentration Analysis yielded dynamic indole risk parameters. Dynamic short-chain fatty acid risk parameters and dynamic calprotectin risk parameters The dynamic risk index calculation module is used to calculate the risk index based on the static indole risk parameter. Static short-chain fatty acid risk parameters Static calprotectin risk parameters Dynamic indole risk parameters Dynamic short-chain fatty acid risk parameters and dynamic calprotectin risk parameters Calculate the dynamic risk index GDI(t1); The risk perturbation rate calculation module is used to calculate the rate based on the static indole risk parameter. Static short-chain fatty acid risk parameters Static calprotectin risk parameters Dynamic indole risk parameters Dynamic short-chain fatty acid risk parameters and dynamic calprotectin risk parameters Calculate the risk disturbance rate ΔGDI rate ; The risk coefficient assessment module is used to assess the risk coefficient based on the static risk index GDI(t0), the dynamic risk index GDI(t1), and the risk disturbance rate ΔGDI. rate ,in accordance with GDI final =γ1GDI(t0)+γ2GDI(t1)+γ3△GDI rate Calculate and analyze risk assessment coefficients; The risk level output module is used to output the risk assessment level based on the risk assessment coefficient.

7. The intestinal risk assessment system according to claim 6, characterized in that, The static risk parameter analysis module includes: The average value acquisition unit is used to obtain the average indole concentration μ from a preset intestinal preparation database. indole Average concentration of short-chain fatty acids (μ) SCFA and the average concentration of calprotectin μ Cal ; The standard deviation calculation unit is used to calculate the standard deviation σ of indole concentration based on a pre-set intestinal preparation database. indole Standard deviation of short-chain fatty acid concentration σ SCFA and the standard deviation of calprotectin concentration μ Cal ; The static indole risk parameter calculation unit is used to calculate the static indole risk parameter. in The static short-chain fatty acid risk parameter calculation unit is used to calculate the static short-chain fatty acid risk parameter. in, The static calprotectin risk parameter calculation unit is used to calculate the static calprotectin risk parameter. in, The dynamic risk parameter analysis module includes: The dynamic indole risk parameter calculation unit is used to calculate the dynamic indole risk parameter. in, The dynamic short-chain fatty acid risk parameter calculation unit is used to calculate dynamic short-chain fatty acid risk parameters. in, The dynamic calprotectin risk parameter calculation unit is used to calculate dynamic calprotectin risk parameters. in, 8. The intestinal risk assessment system according to claim 6, characterized in that, The static risk index calculation module includes: Static feature risk model building unit, used to build static feature risk models; The static feature risk coefficient acquisition unit is used to acquire static feature risk coefficients α1, α2 and α3, where α1+α2+α3=1; The static risk index analysis unit is used to analyze static indole risk parameters based on the static characteristic risk model and static characteristic risk coefficients α1, α2, and α3. Static short-chain fatty acid risk parameters and static calprotectin risk parameters The analysis yielded a static risk index. The dynamic risk index calculation module includes: The dynamic feature risk model building unit is used to build dynamic feature risk models. The dynamic feature risk coefficient acquisition unit is used to acquire dynamic feature risk coefficients β1, β2 and β3, where β1+β2+β3=1; The dynamic risk index analysis unit is used to analyze dynamic indole risk parameters based on a dynamic characteristic risk model and dynamic characteristic risk coefficients β1, β2, and β3. Dynamic short-chain fatty acid risk parameters and dynamic calprotectin risk parameters Analysis yields dynamic risk index 9. The intestinal risk assessment system according to claim 6, characterized in that, The risk disturbance rate calculation module includes: The risk disturbance coefficient acquisition unit is used to acquire risk disturbance coefficients γ1, γ2 and γ3, where γ1+γ2+γ3=1; The static risk perturbation coefficient acquisition unit is used to acquire static indole risk parameters. Static short-chain fatty acid risk parameters and static calprotectin risk parameters The dynamic risk disturbance coefficient acquisition unit is used to acquire dynamic indole risk parameters. Dynamic short-chain fatty acid risk parameters and dynamic calprotectin risk parameters The risk disturbance rate calculation unit is used to calculate the rate based on the risk disturbance coefficients γ1, γ2, and γ3, according to the static indole risk parameter. Static short-chain fatty acid risk parameters Static calprotectin risk parameters Dynamic indole risk parameters Dynamic short-chain fatty acid risk parameters and dynamic calprotectin risk parameters Calculate the risk disturbance rate Where T0 is the original indole concentration collected. Original short-chain fatty acid concentration Compared with the original calprotectin concentration The corresponding timestamp, T1, represents the collected dynamic indole concentration. Dynamic short-chain fatty acid concentration With dynamic calprotectin concentration The corresponding timestamp.

10. A storage medium, characterized in that, The storage medium stores a computer program, which includes program instructions. When the program instructions are executed by a processor, the processor performs the intestinal risk assessment method according to any one of claims 1-5.

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