Liver, gall and kidney stone auxiliary diagnosis method based on ultrasonic image dynamic identification

Abstract

The invention relates to the technical field of health management, in particular to an ultrasonic image dynamic recognition-based liver, gall and kidney stone auxiliary diagnosis method, which comprises the following steps of: deploying a recommended sports diet monitoring server, collecting sports nutrition data for analysis and processing, constructing a sports nutrition mind map, and constructing a health management system; deploying an ultrasonic image scanner to acquire feature data of calculus personnel and outputting a calculus risk behavior set; mapping the obtained calculus risk behavior set to a motor nutriology mind map, and outputting a suggested mapping text to judge whether the calculus risk behavior set is unreasonable or not; collecting medical record checking data associated with a calculus person, analyzing and processing a calculus risk behavior set passing checking, and judging whether to output early warning data or not based on a contrastive analysis record; and collecting immune evaluation data of the associated calculus person in the sports diet monitoring time, deploying adaptive correction data, and performing reply processing. According to the invention, health management can be effectively implemented, so that liver, gall and kidney stones of patients can be effectively relieved.

Description

Technical Field

[0001] The present application relates to the field of health management technology, and in particular to an auxiliary diagnosis method for liver, gallbladder and kidney stones based on dynamic recognition of ultrasonic images. Background Art

[0002] With the change of dietary structure, the incidence of hepatobiliary and kidney stones has been increasing year by year. Due to people's lack of knowledge about hepatobiliary and kidney stones and their indifferent attitude, hepatobiliary and kidney stones have become more serious, and some patients have developed serious diseases such as uremia. The key to curing hepatobiliary and kidney stones is early detection and early treatment. We all know that hepatobiliary and kidney stones are closely related to the long-term unhealthy lifestyle. Therefore, the early prevention of hepatobiliary and kidney stones focuses on the intervention of lifestyle, that is, the health management of hepatobiliary and kidney stones is very important.

[0003] Currently, routine health management is carried out by doctors issuing medical orders, and patients manage their own health according to the orders, or the patients' family members supervise and ensure the execution of the orders. However, in real life, many plans are often not implemented very well or even effectively, resulting in patients' liver, gallbladder and kidney stones not being effectively relieved, and some conditions may even be aggravated.

[0004] With the widespread application of AI intelligence, the application of AI chips in smart medical care is becoming more and more in-depth. How to effectively combine AI for intelligent assistance for patients with hepatobiliary and kidney stones is an important research direction for efficient auxiliary diagnosis of hepatobiliary and kidney stones. Summary of the invention

[0005] To achieve the above objectives, this application provides the following technical solutions: According to the first aspect of the present invention, the present invention claims a method for auxiliary diagnosis of hepatobiliary and kidney stones based on dynamic recognition of ultrasonic images, comprising the following steps: Step S1: deploying a recommended sports diet monitoring server, collecting sports nutrition data, analyzing and processing the sports nutrition data, collecting sports diet record features and sports diet mapping data, and constructing a sports nutrition mind map based on the acquired data; Step S2: deploying an ultrasonic imaging scanner in the recommended exercise and diet monitoring server, collecting characteristic data of stone-forming personnel based on the ultrasonic imaging scanner, and having the inspector output a stone-forming risk behavior set based on the characteristic data of stone-forming personnel; Step S3: Map the obtained set of stone risk behaviors to the mind map of sports nutrition, output the recommended mapping text, and determine whether the set of stone risk behaviors is unreasonable based on the recommended mapping text; collect the medical record verification data associated with the stone patients, and determine whether the set of stone risk behaviors without unreasonableness conforms to the associated stone patients, and determine whether this set of stone risk behaviors passes the verification based on the judgment record; Step S4: Analyze and process the set of stone risk behaviors that have passed the verification, collect the global sports diet evaluation parameters, monitor the stone dynamics of the stone patients, collect the sports nutrition monitoring data, compare and analyze the obtained sports nutrition monitoring data and the global sports diet evaluation parameters, and determine whether to output warning data based on the comparison and analysis record; Step S5: Configure the sports diet monitoring time, collect the immune evaluation data of the associated stone patients during the sports diet monitoring time, deploy the adaptive correction data based on the immune evaluation data, and transmit it to the associated medical database. The inspector conducts a response process based on the received warning data and the adaptive correction data.

[0006] Further, the process of deploying the recommended sports diet monitoring server and constructing the mind map of sports nutrition includes: Deploy the recommended sports diet monitoring server. A sports diet input menu is deployed inside the recommended sports diet monitoring server, and the sports diet input menu is used to collect sports diet basic data based on big data retrieval. The sports diet basic data includes sports diet names, sports diet attributes, medical record data, sports diet interactions, and sports diet physiological reflex data; Analyze and process the obtained sports diet basic data, collect all the sports diet record features in the recommended sports diet monitoring server, construct a classification system structure based on the sports diet record features, deploy sports diet attribute nodes based on the associated sports diet record features, collect the influence degree between each sports diet attribute node based on the sports diet interactions, and construct a mind map of sports nutrition based on the obtained influence degree. The mind map of sports nutrition includes the stone influence degree between different sports diet names, and the stone influence degree includes positive influence degree, negative influence degree, and no influence degree.

[0007] Further, the process of outputting the set of stone risk behaviors includes: A ultrasonic image scanner is deployed inside the recommended sports diet monitoring server. The ultrasonic image scanner stores the associated medical database and the stone patient database. The inspector conducts a diagnosis process on the corresponding stone patient database based on the associated medical database inside the ultrasonic image scanner; The stone patient submits the stone patient characteristic data through the stone patient database. The stone patient characteristic data includes the personal data of the stone patient, the treatment history data, and the stone patient symptom data. The stone patient characteristic data is transmitted to the medical staff database based on the included treatment history data. The inspector collects and associates with the stone patient database based on the stone patient characteristic data received in the medical staff database, diagnoses the corresponding stone patient, and outputs the stone patient diagnosis record and the associated stone risk behavior set based on the diagnosis record. The stone risk behavior set includes the associated exercise and diet names and medication standards. The stone risk behavior set associated with the associated stone patient database and the stone patient diagnosis record are noted and saved.

[0008] Further, the process of determining whether the stone risk behavior set is unreasonable includes: The exercise and diet monitoring server is deployed with an exercise and diet log processor. The exercise and diet log processor collects the stone risk behavior set associated with the associated stone patient account, analyzes and processes it, and determines whether the stone risk behavior set is unreasonable. The exercise and diet log processor stores the exercise and nutrition mind map obtained in the server. The exercise and diet names associated in the stone patient account associated stone risk behavior set are mapped to the exercise and nutrition mind map. The recommended mapping text associated with the stone patient database associated stone risk behavior set is collected. The recommended mapping text includes the exercise and diet attributes associated with the associated exercise and diet names and the degree of stone impact between them. The various exercise and diet attributes in the recommended mapping text are combined without repetition. Each combination collected without repetition is recorded as an exercise and diet combination subset. The obtained exercise and diet combination subsets are totaled as the exercise and diet combination set. The degree of stone impact between the associated exercises and diets in each exercise and diet combination subset in the exercise and diet combination set is determined to see if there is an exercise and diet combination subset associated with a negative impact degree. If not, the stone risk behavior set passes the check. If so, the exercise and diet attributes in the exercise and diet combination subset are further analyzed and processed. The exercise and diet log processor collects the exercise and diet names and the stone patient diagnosis record in the exercise and diet combination subset. An expert personnel set is deployed in it. The expert personnel set includes exercise nutrition experts. The exercise and diet combination subset with a negative impact degree and the stone patient diagnosis record are transmitted to the expert personnel set, and are checked by the exercise nutrition experts. If the check passes, the stone risk behavior set is not unreasonable. If it fails, it is unreasonable.

[0009] Further, the process of checking the medical records of the lithiasis patients, collecting the medical record check data, and determining whether the set of unreasonable lithiasis risk behaviors is relevant to the medical record check data includes: The exercise and diet log processor collects a set of unreasonable lithiasis risk behaviors, collects the exercise and diet names included in the set of lithiasis risk behaviors, and collects the medical record data associated with each exercise and diet name; based on the set of lithiasis risk behaviors, it collects the associated lithiasis patient database, and checks the medical records of the lithiasis patients based on the obtained lithiasis patient database and medical records, and collects the medical record check data. The exercise and diet log processor analyzes and processes the medical record check data obtained for the corresponding lithiasis patients. When the medical record data of all exercise and diet names in the set of lithiasis risk behaviors are relevant to the associated medical record check data, then the set of lithiasis risk behaviors passes the check; if there is any unsuccessful relevance, then the set of lithiasis risk behaviors fails the check; the exercise and diet log processor outputs the recommended unreasonable data for the set of behaviors that fail the check or have unreasonable lithiasis risk behaviors, and transmits it to the recommended exercise and diet monitoring server.

[0010] Further, the process of collecting exercise and diet assessment parameters and exercise and nutrition monitoring data, and determining whether to output warning data based on the comparison record includes: Collect the set of lithiasis risk behaviors associated with the lithiasis patient database, collect the exercise and diet physiological reflex data associated with each exercise and diet name in the set of lithiasis risk behaviors, extract the regular features from the obtained exercise and diet physiological reflex data, and collect the exercise and diet physiological reflex data. The recommended exercise and diet monitoring server stores the exercise and diet physiological reflex data associated with the associated exercise and diet names, and collects the historical exercise and diet physiological reflex data set associated with the historical lithiasis risk behavior set based on the big data algorithm; constructs an exercise and diet assessment model based on the historical exercise and diet physiological reflex data set using the deep learning algorithm; inputs the exercise and diet physiological reflex data in the obtained set of lithiasis risk behaviors into the exercise and diet assessment model, and collects the global exercise and diet assessment parameters. The recommended exercise and diet monitoring server deploys an exercise and nutrition monitoring terminal, which is used to collect the global exercise and diet assessment parameters associated with the set of lithiasis risk behaviors of the associated lithiasis patients in the associated lithiasis patient database, and perform exercise and nutrition monitoring on them, collect the exercise and nutrition monitoring data, and determine whether the obtained exercise and nutrition monitoring data conforms to the global exercise and diet assessment parameters. If it conforms, no warning data is output; if it does not conform, then monitoring warning data is output.

[0011] Further, the process of deploying adaptive correction data includes: Configure the sports diet monitoring time, collect the sports nutrition monitoring data and global sports diet evaluation parameters of the associated kidney stone personnel database within the cycle, respectively output the monitoring dynamic line graph and evaluation dynamic line graph associated with the sports nutrition monitoring data and global sports diet evaluation parameters in the cycle, collect the associated deviation information based on the monitoring dynamic line graph and evaluation dynamic line graph, configure the deviation threshold value, collect the floor value of the obtained deviation information, compare and analyze the obtained floor value and the deviation threshold value. When the floor value exceeds the deviation threshold value, collect the sign of the deviation information associated with the floor value. When the sign is a positive sign, mark it as allowing unreasonableness. When the sign is a negative sign, mark it as immune to unreasonableness; based on the cycle occupancy ratios associated with allowing unreasonableness and immune to unreasonableness, and record the obtained cycle occupancy ratios as adaptive evaluation data, configure the correction threshold value. When the adaptive evaluation data exceeds the correction threshold value, output the adaptive correction data.

[0012] Further, the medical staff database associated with the recommended sports diet monitoring server is corrected by the inspector based on the received content, based on the recommended unreasonable data, monitoring warning data and adaptive correction data associated with the kidney stone personnel database.

[0013] This auxiliary diagnosis method for hepatobiliary and kidney stones based on dynamic ultrasound image recognition collects and analyzes sports nutrition data by deploying a recommended sports diet monitoring server, constructs a sports nutrition mind map, and deploys an ultrasound image scanner to collect the characteristic data of kidney stone personnel and output a set of stone risk behaviors; maps the obtained set of stone risk behaviors to the sports nutrition mind map, outputs a recommended mapping text to judge whether the set of stone risk behaviors is unreasonable; collects the medical record verification data associated with the kidney stone personnel, analyzes and processes the set of stone risk behaviors that pass the verification, and judges whether to output warning data based on the comparison analysis record; collects the immune evaluation data of the associated kidney stone personnel during the sports diet monitoring time, deploys the adaptive correction data and conducts a reply process. The present invention can effectively implement health management and effectively relieve the hepatobiliary and kidney stones of patients. Description of the Drawings

[0014] Figure 1 It is a working flowchart of an auxiliary diagnosis method for hepatobiliary and kidney stones based on dynamic ultrasound image recognition requested to be protected by the embodiments of the present application. Detailed Embodiments

[0015] Next, the technical solutions in the embodiments of the present application will be clearly and completely described in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present application.

[0016] As Figure 1 shown, a method for assisting in the diagnosis of hepatobiliary and renal calculi based on dynamic recognition of ultrasonic images includes the following steps: Step S1: Deploy a recommended exercise and diet monitoring server, collect exercise and nutrition data, analyze and process the exercise and nutrition data, collect exercise and diet record features and exercise and diet mapping data, and construct an exercise and nutrition mind map based on the obtained data; Step S2: Deploy an ultrasonic image scanner inside the recommended exercise and diet monitoring server, collect stone personnel feature data based on the ultrasonic image scanner, and output a set of stone risk behaviors by an inspector based on the stone personnel feature data; Step S3: Map the obtained set of stone risk behaviors to the exercise and nutrition mind map, output a recommended mapping text, and determine whether the set of stone risk behaviors is unreasonable based on the recommended mapping text; collect medical record check data associated with the stone personnel, determine whether the set of stone risk behaviors without unreasonableness conforms to the associated stone personnel, and determine whether the set of stone risk behaviors passes the check based on the judgment record; Step S4: Analyze and process the set of stone risk behaviors that pass the check, collect global exercise and diet evaluation parameters, monitor the stone dynamics of the stone personnel, collect exercise and nutrition monitoring data, compare and analyze the obtained exercise and nutrition monitoring data and global exercise and diet evaluation parameters, and determine whether to output warning data based on the comparison and analysis record; Step S5: Configure the exercise and diet monitoring time, collect immune evaluation data of the associated stone personnel during the exercise and diet monitoring time, deploy adaptive correction data based on the immune evaluation data, and transmit it to the associated medical care database, and the inspector performs a reply process based on the received warning data and adaptive correction data.

[0017] It should be further noted that in the specific implementation process, the process of deploying the recommended exercise and diet monitoring server, collecting exercise and nutrition data, analyzing and processing the exercise and nutrition data, collecting exercise and diet record features and exercise and diet mapping data, and constructing an exercise and nutrition mind map includes: Deploy a recommended exercise and diet monitoring server, deploy an exercise and diet input menu inside the recommended exercise and diet monitoring server, and the exercise and diet input menu is used to collect exercise and diet basic data based on big data retrieval. The exercise and diet basic data includes exercise and diet names, exercise and diet attributes, medical record data, exercise and diet interactions, and exercise and diet physiological reflection data; the exercise and diet attributes are the action mechanism, treatment scope, and treatment effect of the exercise and diet; the medical record data is the medical record source data associated with the exercise and diet name; the exercise and diet interaction is the degree of stone impact associated with different exercise and diet names; Classify the obtained basic data of sports diet, collect all the characteristics of sports diet records in the recommended sports diet monitoring server, construct a classification system structure based on the characteristics of sports diet records, deploy sports diet attribute nodes based on the associated characteristics of sports diet records, collect the influence degree between each sports diet attribute node based on the interaction of sports diet, construct a mind map of sports nutrition based on the obtained influence degree, and the mind map of sports nutrition includes the influence degree of calculus between different basic data of sports diet. The influence degree of calculus includes positive influence degree, negative influence degree and no influence degree; the positive influence degree is the degree of mutual promotion influence, the negative influence degree is the degree of mutual opposition influence, and the no influence degree is the degree of mutual non-influence.

[0018] It should be further noted that in the specific implementation process, the process of deploying an ultrasonic imaging scanner in the recommended sports diet monitoring server and collecting the characteristic data of calculus patients by the inspector based on the characteristic data of calculus patients and outputting the calculus risk behavior set includes: An ultrasonic imaging scanner is deployed in the recommended sports diet monitoring server, and an associated medical staff database and a calculus patient database are stored in the ultrasonic imaging scanner. The inspector conducts a diagnosis process on the corresponding calculus patient database based on the associated medical staff database in the ultrasonic imaging scanner; The calculus patient submits the characteristic data of the calculus patient through the calculus patient database. The characteristic data of the calculus patient includes the personal data of the calculus patient, the treatment history data and the symptoms data of the calculus patient; based on the included treatment history data, the characteristic data of the calculus patient is transmitted to the medical staff database; The inspector collects the associated calculus patient database based on the characteristic data of the calculus patient received in the medical staff database, diagnoses the corresponding calculus patient, outputs the diagnosis record of the calculus patient and the associated calculus risk behavior set based on the diagnosis record. The calculus risk behavior set includes the associated sports diet name and medication standard; the calculus risk behavior set associated with the associated calculus patient database and the diagnosis record of the calculus patient are noted and saved.

[0019] It should be further noted that in the specific implementation process, the process of mapping the obtained calculus risk behavior set to the mind map of sports nutrition, outputting the recommended mapping text, judging whether the calculus risk behavior set is unreasonable based on the recommended mapping text, collecting the medical record verification data associated with the calculus patient, and judging whether the calculus risk behavior set without unreasonableness conforms to the associated calculus patient and judging whether the calculus risk behavior set passes the verification based on the judgment record includes: The recommended sports diet monitoring server deploys a sports diet log processor, which collects the set of stone risk behaviors associated with the associated stone personnel account, analyzes and processes it, and determines whether the set of stone risk behaviors is unreasonable; The sports diet log processor stores the mind map of sports nutrition obtained in the server, maps the sports diet names associated in the set of stone risk behaviors of the stone personnel account to the mind map of sports nutrition, and collects the recommended mapping text associated with the set of stone risk behaviors of the stone personnel database. The recommended mapping text includes the sports diet attributes associated with the associated sports diet names and the degree of stone influence between them; The various sports diet attributes in the recommended mapping text are combined without repetition, and each combination collected without repetition is recorded as a sports diet combination subset, and the obtained sports diet combination subsets are totaled as a sports diet combination set; the degree of stone influence between the associated sports diets in each sports diet combination subset in the sports diet combination set is judged to determine whether there is a sports diet combination subset associated with a negative influence degree. If not, the set of stone risk behaviors passes the check. If so, further analysis and processing are performed on the sports diet attributes in the sports diet combination subset; The sports diet log processor collects the sports diet names and the diagnostic records of stone personnel in the sports diet combination subset, and deploys a set of expert personnel, which includes sports nutrition experts. The sports diet combination subset with a negative influence degree and the diagnostic records of stone personnel are transmitted to the set of expert personnel, and the sports nutrition experts check them to determine whether there is an influence between the associated sports diets, and output a check record based on the mutual records; if the check passes, the set of stone risk behaviors is not unreasonable. If not, it is unreasonable.

[0020] The sports diet log processor collects the set of stone risk behaviors that are not unreasonable, collects the sports diet names included in the set of stone risk behaviors, and collects the medical record data associated with each sports diet name; based on the set of stone risk behaviors, the associated stone personnel database is collected, and the stone personnel are checked against the medical records based on the obtained stone personnel database and medical records, and the medical record check data is collected; The sports diet log processor analyzes and processes the medical record check data obtained for the corresponding stone personnel. When the medical record data of all sports diet names in the set of stone risk behaviors is relevant to the associated medical record check data, the set of stone risk behaviors passes the check. If there is an unsuccessful correlation, the set of stone risk behaviors fails the check; the sports diet log processor outputs the recommended unreasonable data for the set of stone risk behaviors that fails the check or is unreasonable, and transmits it to the recommended sports diet monitoring server.

[0021] It should be further noted that in the specific implementation process, the analysis and processing of the verified set of stone risk behaviors, the collection of global exercise and diet assessment parameters, the monitoring of the stone dynamics of stone patients, the collection of exercise and nutrition monitoring data, and the comparison and analysis of the obtained exercise and nutrition monitoring data and global exercise and diet assessment parameters, and the judgment of whether to output warning data based on the comparison and analysis records include: Collect the set of stone risk behaviors associated with the stone patient database, collect the exercise and diet physiological reflex data associated with each exercise and diet name in the set of stone risk behaviors, extract the regular features of the obtained exercise and diet physiological reflex data, and collect the exercise and diet physiological reflex data; The recommended exercise and diet monitoring server stores the exercise and diet physiological reflex data associated with the relevant exercise and diet names, collects the historical exercise and diet physiological reflex data set associated with the historical set of stone risk behaviors based on the big data algorithm; constructs an exercise and diet assessment model based on the historical exercise and diet physiological reflex data set using the deep learning algorithm; inputs the exercise and diet physiological reflex data in the obtained set of stone risk behaviors into the exercise and diet assessment model to collect global exercise and diet assessment parameters; The recommended exercise and diet monitoring server is deployed with an exercise and nutrition monitoring terminal, which is used to collect the global exercise and diet assessment parameters associated with the set of stone risk behaviors of the stone patient associated with the stone patient database, conduct exercise and nutrition monitoring on it, collect exercise and nutrition monitoring data, and determine whether the obtained exercise and nutrition monitoring data conforms to the global exercise and diet assessment parameters. If it conforms, no warning data is output. If it does not conform, monitoring warning data is output.

[0022] It should be further noted that in the specific implementation process, the process of configuring the exercise and diet monitoring time, collecting the immune assessment data of the stone patient associated during the exercise and diet monitoring time, deploying adaptive correction data based on the immune assessment data, and transmitting it to the associated medical staff database, and the inspector's reply processing based on the received warning data and adaptive correction data includes: Configure the sports diet monitoring time, collect the sports nutrition monitoring data and global sports diet evaluation parameters of the associated stone patient database within the cycle, and respectively output the monitoring dynamic line chart and evaluation dynamic line chart associated with the sports nutrition monitoring data and global sports diet evaluation parameter data in the cycle. Based on the deviation information collected by subtracting the evaluation dynamic line chart in the associated cycle from the monitoring dynamic line chart, configure the deviation threshold value, collect the floor value of the obtained deviation information, and compare and analyze the obtained floor value with the deviation threshold value. When the floor value exceeds the deviation threshold value, collect the sign of the deviation information associated with the floor value. When the sign is a positive sign, mark it as allowing unreasonableness. When the sign is a negative sign, mark it as immune to unreasonableness; based on the cycle occupancy ratio associated with allowing unreasonableness and immune to unreasonableness, and record the obtained cycle occupancy ratio as the adaptive evaluation data, configure the correction threshold value. When the adaptive evaluation data exceeds the correction threshold value, output the adaptive correction data. The adaptive correction data includes the physiological reflection situation of the sports diet effect during the use of the sports diet by the associated stone patient; the correction suggestions output for the dosage of the sports diet associated with the stone risk behavior set based on the physiological reflection situation of the sports diet effect. Based on the recommended unreasonable data, monitoring and warning data, and adaptive correction data associated with the stone patient database of the recommended sports diet monitoring server, the inspector makes a reply and correction based on the received content.

[0023] The specific implementation manner of the invention has been described in detail above, but it is only an example, and this application is not limited to the specific implementation manner described above. For those skilled in the art, any equivalent modification or substitution of the invention is also within the scope of this application. Therefore, equivalent transformations, modifications, improvements, etc. made without departing from the spirit and principle of this application should all be covered within the scope of this application.

Claims

1. A method for auxiliary diagnosis of liver, gallbladder and kidney stones based on dynamic recognition of ultrasonic images, characterized in that: The following steps are involved: Step S1: deploying a recommended sports diet monitoring server, collecting sports nutrition data, analyzing and processing the sports nutrition data, collecting sports diet record features and sports diet mapping data, and constructing a sports nutrition mind map based on the acquired data; Step S2: deploying an ultrasonic imaging scanner in the recommended exercise and diet monitoring server, collecting characteristic data of stone-forming personnel based on the ultrasonic imaging scanner, and having the inspector output a stone-forming risk behavior set based on the characteristic data of stone-forming personnel; Step S3: Map the obtained stone risk behavior set to the sports nutrition mind map, output the suggested mapping text, and judge whether the stone risk behavior set is unreasonable based on the suggested mapping text; collect the medical record verification data associated with the stone personnel, judge whether the stone risk behavior set that is not unreasonable is consistent with the associated stone personnel, and judge whether the stone risk behavior set passes the verification based on the judgment record; Step S4: Analyze and process the verified stone risk behavior set, collect global sports diet assessment parameters, monitor the stone dynamics of stone-forming personnel, collect sports nutrition monitoring data, compare and analyze the obtained sports nutrition monitoring data with the global sports diet assessment parameters, and determine whether to output warning data based on the comparative analysis records; Step S5: Configure the exercise and diet monitoring time, collect the immune assessment data of the stone-related personnel during the exercise and diet monitoring time, deploy adaptive correction data based on the immune assessment data, and transmit it to the related medical database. The inspector responds based on the received warning data and adaptive correction data.

2. The method for auxiliary diagnosis of hepatobiliary and kidney stones based on dynamic recognition of ultrasonic images as claimed in claim 1, characterized in that: The process of deploying the recommended sports diet monitoring server and constructing a sports nutrition mind map includes: Deploy a recommended sports diet monitoring server, wherein a sports diet input menu is deployed in the recommended sports diet monitoring server, wherein the sports diet input menu is used to retrieve and collect basic sports diet data based on big data, wherein the basic sports diet data includes sports diet name, sports diet attributes, medical record data, sports diet interaction, and sports diet physiological reflection data; The obtained basic sports diet data is analyzed and processed, all sports diet record features in the recommended sports diet monitoring server are collected, a classification architecture is constructed based on the sports diet record features, and sports diet attribute nodes are deployed based on the associated sports diet record features. The influence degree between each sports diet attribute node is collected based on the sports diet interaction, and a sports nutrition mind map is constructed based on the obtained influence degree, wherein the sports nutrition mind map includes the influence degree of stones between different sports diet names, and the influence degree of stones includes positive influence degree, negative influence degree and no influence degree.

3. The method for auxiliary diagnosis of liver, gallbladder and kidney stones based on dynamic recognition of ultrasonic images as claimed in claim 2, characterized in that: The process of outputting the stone risk behavior set includes: The recommended exercise and diet monitoring server is equipped with an ultrasonic imaging scanner, which stores a related medical and nursing database and a stone-forming personnel database. The inspector performs diagnosis and processing on the corresponding stone-forming personnel database based on the medical and nursing database associated with the ultrasonic imaging scanner; The stone-bearing person submits stone-bearing person characteristic data through the stone-bearing person database, wherein the stone-bearing person characteristic data includes the stone-bearing person's personal data, treatment history data and stone-bearing person's symptom data; based on the treatment history data included therein, the stone-bearing person characteristic data is transmitted to the medical care database; The inspector collects the associated stone-causing personnel database based on the characteristic data of stone-causing personnel received in the medical database, diagnoses the corresponding stone-causing personnel, and outputs the stone-causing personnel's diagnosis record and the associated stone risk behavior set based on the diagnosis record, wherein the stone risk behavior set includes the associated exercise and diet names and medication standards; the stone risk behavior set and the stone-causing personnel's diagnosis record associated with the associated stone-causing personnel database are annotated and saved.

4. The method for auxiliary diagnosis of hepatobiliary and kidney stones based on dynamic recognition of ultrasonic images as claimed in claim 3, characterized in that: The process of judging whether the stone risk behavior set is unreasonable includes: The recommended exercise and diet monitoring server is equipped with an exercise and diet log processor, which collects the stone risk behavior set associated with the associated stone-forming personnel account, analyzes and processes it, and determines whether the stone risk behavior set is unreasonable; The sports diet log processor stores the sports nutrition mind map obtained in the server, maps the sports diet name associated with the stone risk behavior set associated with the stone personnel account to the sports nutrition mind map, collects the suggested mapping text associated with the stone risk behavior set associated with the stone personnel database, and the suggested mapping text includes the sports diet attributes associated with the associated sports diet name and the degree of stone impact between them; Match each sports diet attribute in the suggested mapping text without duplication, record each match collected without duplication as a sports diet match subset, and count the obtained sports diet match subsets as a sports diet match set; judge whether there is a sports diet match subset with a negative impact degree association among the sports diet match subsets in the sports diet match set, if not, the stone risk behavior set passes the verification, if yes, further analyze and process the sports diet attributes in the sports diet match subset; The sports diet log processor collects the names of sports diets and the diagnosis records of stone-causing persons in the sports diet combination subset, wherein an expert staff set is deployed, and the expert staff set includes sports nutrition experts. The sports diet combination subsets and the diagnosis records of stone-causing persons with negative impact levels are transmitted to the expert staff set, and the sports nutrition experts check them. If the check passes, the stone risk behavior set is not unreasonable; if it fails, it is unreasonable.

5. The method for auxiliary diagnosis of hepatobiliary and kidney stones based on dynamic recognition of ultrasonic images as claimed in claim 4, characterized in that: The process of checking the medical records of stone-forming personnel, collecting medical record checking data, and determining whether the set of behaviors that do not have unreasonable stone risk is related to the medical record checking data includes: The exercise and diet log processor collects a set of behaviors that do not have unreasonable risk of stone formation, collects the names of exercise and diet contained in the set of behaviors that have risk of stone formation, and collects medical record data associated with each name of exercise and diet; collects a database of stone formation personnel associated with the set of behaviors that have risk of stone formation, verifies the medical records of stone formation personnel based on the obtained database of stone formation personnel and medical records, and collects medical record verification data; The sports and diet log processor analyzes and processes the medical record verification data obtained from the corresponding stone-risk personnel. When the medical record data of all sports and diet names in the stone risk behavior set are related to the associated medical record verification data, the stone risk behavior set passes the verification. If there is a related failure, the stone risk behavior set fails the verification. The sports and diet log processor outputs unreasonable data for the stone risk behavior set that fails the verification or has unreasonable data, and transmits it to the recommended sports and diet monitoring server.

6. The method for auxiliary diagnosis of hepatobiliary and kidney stones based on dynamic recognition of ultrasonic images as claimed in claim 5, characterized in that: The process of collecting sports diet assessment parameters and sports nutrition monitoring data and judging whether to output warning data based on comparison records includes: Collecting a stone risk behavior set associated with a stone-forming personnel database, collecting sports and diet physiological reflex data associated with each sports and diet name in the stone risk behavior set, extracting regular features from the obtained sports and diet physiological reflex data, and collecting sports and diet physiological reflex data; The recommended sports diet monitoring server stores sports diet physiological reflection data associated with the associated sports diet name, collects historical sports diet physiological reflection data sets associated with historical stone risk behavior sets based on a big data algorithm; constructs a sports diet assessment model based on the historical sports diet physiological reflection data sets based on a deep learning algorithm; inputs the obtained sports diet physiological reflection data in the stone risk behavior set into the sports diet assessment model, and collects global sports diet assessment parameters; A sports nutrition monitoring terminal is deployed in the recommended sports diet monitoring server, and the sports nutrition monitoring terminal is used to collect global sports diet assessment parameters associated with the stone risk behavior set of stone-forming personnel associated with the stone-forming personnel database, and conduct sports nutrition monitoring on them, collect sports nutrition monitoring data, and check whether the obtained sports nutrition monitoring data meets the global sports diet assessment parameters. If yes, no warning data is output; if not, monitoring warning data is output.

7. The method for auxiliary diagnosis of hepatobiliary and kidney stones based on dynamic recognition of ultrasonic images as claimed in claim 6, characterized in that: The process of deploying the adaptive correction data includes: Configure the sports and diet monitoring time, collect the sports nutrition monitoring data and global sports and diet assessment parameters of the associated stone personnel database within the period, respectively output the monitoring dynamic broken line and the evaluation dynamic broken line associated with the sports nutrition monitoring data and the global sports and diet assessment parameter data of the period, collect the associated deviation information based on the monitoring dynamic broken line and the evaluation dynamic broken line, configure the deviation threshold value, collect the rounded value of the obtained deviation information, compare and analyze the obtained rounded value and the deviation threshold value, when the rounded value exceeds the deviation threshold value, collect the sign of the deviation information associated with the rounded value, when the sign is positive, it is annotated as allowed unreasonable, when the sign is negative, it is annotated as immune unreasonable; based on the period proportion associated with allowed unreasonable and immune unreasonable, and record the obtained period proportion as adaptive evaluation data, configure the correction threshold value, and when the adaptive evaluation data exceeds the correction threshold value, output the adaptive correction data.

8. The method for auxiliary diagnosis of hepatobiliary and kidney stones based on dynamic recognition of ultrasonic images as claimed in claim 7, characterized in that: The medical database associated with the recommended exercise and diet monitoring server is based on the unreasonable recommended data, monitoring warning data and adaptive correction data associated with the stone personnel database, and the inspector makes replies and corrections based on the received content.

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