Intelligent doctor appointment inquiry service platform integrated with doctor-patient community management

Abstract

The invention relates to the technical field of appointment and inquiry service, in particular to an intelligent doctor appointment and inquiry service platform integrated with doctor-patient community management, the platform integrates multi-dimensional data, accurately estimates injury degree and analyzes illness state evolution and environmental influence by utilizing an analysis model, a patient does not need to rush among different departments or hospitals to find a proper doctor, and the patient experience is improved. The platform automatically matches the most suitable community doctors for the patients, the doctors and the patients are comprehensively matched through multiple factors, the patients are allocated to the most suitable doctors, waste and unreasonable use of medical resources are avoided, and efficient utilization of the medical resources is achieved.

Description

Technical Field

[0001] The present invention relates to the technical field of appointment consultation services, and particularly to an intelligent doctor appointment consultation service platform integrating doctor-patient community management. Background Art

[0002] With the development of society and the improvement of people's health awareness, the demand for medical services is increasing day by day. There are many problems in traditional medical service models in aspects such as patient appointment, consultation, treatment, etc., and it is difficult to meet the requirements of high efficiency, precision, and personalization of modern medical services. Under the traditional medical model, patients often need to go to the hospital by themselves, queue up for registration and see a doctor between different departments, consuming a lot of time and energy. For some patients with more complex conditions, they may need to go to different hospitals or departments several times to obtain an accurate diagnosis and effective treatment. During the diagnosis process, doctors mainly rely on patients' self-report, clinical examinations, and limited medical records, and it is difficult to comprehensively understand the evolution of patients' conditions, physical conditions, and environmental factors, etc. This may lead to misdiagnosis, missed diagnosis, etc., affecting the treatment effect. Due to the lack of comprehensive and accurate analysis of patients' conditions, doctors may not be able to predict potential risks in advance during the treatment process, and the treatment plans formulated are not comprehensive enough, thus increasing the probability of medical accidents.

[0003] In summary, the existing technologies for evacuating on-site personnel in case of infectious diseases have many deficiencies and cannot meet the needs of practical applications. Therefore, there is an urgent need for an intelligent doctor appointment consultation service platform integrating doctor-patient community management. Summary of the Invention

[0004] In order to overcome the defects and deficiencies existing in the prior art, the present invention provides an intelligent doctor appointment consultation service platform integrating doctor-patient community management.

[0005] In order to achieve the above object, the present invention adopts the following technical solutions: In a first aspect, the present invention provides an intelligent doctor appointment and consultation service platform integrating doctor-patient community management, including a doctor-patient information acquisition module, a patient condition evolution analysis module, a community situation anomaly analysis module, a doctor situation analysis module, and a doctor resource allocation module. Among them, the doctor-patient information acquisition module is used to acquire the condition data described by the patient, the community scene data, and the corresponding doctor situation data. Among them, the condition data includes the symptom situation data corresponding to the patient's description and the change data of the patient's physical characteristics. Among them, the physical characteristic data of the patient can be obtained through the intelligent monitoring bracelet carried by the patient. The community scene data includes the scene environment data of the corresponding injury location in the community. The patient condition evolution analysis module is used to analyze the patient condition evolution according to the patient condition change. The community situation anomaly analysis module is used to analyze the impact of the scene situation at the corresponding location in the community on the condition of the corresponding patient. The doctor situation analysis module is used to obtain the historical treatment patient situation and the good at field situation of the corresponding community doctor to analyze the matching degree of patient treatment. The doctor resource allocation module is used to allocate medical staff according to the analysis result of the patient treatment matching degree.

[0006] In an implementation manner of the present invention, the doctor-patient information acquisition module includes a patient condition acquisition unit, a community scene data acquisition unit, and a doctor situation acquisition unit. Among them, the patient condition acquisition unit is used to acquire the symptom situation data corresponding to the patient's description and the change data of the patient's physical characteristics. The community scene data acquisition unit is used to acquire the accident scene situation and the environmental situation at the corresponding location of the community scene. The doctor situation acquisition unit is used to acquire the historical treatment patient situation and the good at field situation of the corresponding community doctor.

[0007] In an implementation manner of the present invention, the analysis of the patient condition evolution according to the patient condition change includes the following specific contents: S11. Obtain the accident situation described by the community personnel or the patient and the patient condition change data, quickly obtain the identity situation of the corresponding patient based on the community personnel positioning module, and obtain the basic disease situation information of the patient through the patient's identity information. Because this is automatically completed in the system, and at the same time, obtaining the information is also for public safety and does not involve the problem of privacy leakage; S12. Obtain the collision change situation at each position during the accident process and the basic disease situation of each position of the patient, and estimate the accident injury degree of each position of the patient by constructing a neural network model; S13. Obtain the change situation of the patient's physical characteristics, and perform abnormal analysis of the patient's physical characteristics based on the stability and deterioration speed of the patient's physical characteristics. Among them, the patient physical characteristic abnormal analysis formula is: , where n is the types of physical characteristic data that can be monitored, such as body temperature, heart rate, etc., ri is the influence weight of the i-th physical characteristic, a is the physical stability influence coefficient, b is the deterioration speed influence coefficient, xi is the average value of the i-th physical characteristic during the monitoring period, xim is the median of the safety range of the i-th physical characteristic, xits is the monitoring data of the i-th physical characteristic at the end moment within the set monitoring duration, xitr is the monitoring data of the i-th physical characteristic at the starting moment within the set monitoring duration, ximax is the maximum value of the safety range of the i-th physical characteristic, ximin is the minimum value of the safety range of the i-th physical characteristic, where, reflects the static cumulative effect of the characteristic value deviating from the safety median during the monitoring period and is suitable for detecting chronic abnormalities, captures the dynamic change rate of the characteristic value during the monitoring period, is more sensitive to acute deterioration, and obtains the true injury degree of the corresponding position through the accident injury degree at each position and the analysis result of the patient's physical abnormality. The calculation formula for the true injury degree at the r-th position is: , Rr is the accident injury degree at the r-th position, and f is the amplification coefficient.

[0008] In an implementation manner of the present invention, the analysis of the influence of the scene situation at the corresponding position of the community on the condition of the corresponding patient includes the following specific steps: S21. Obtain the scene situation at the corresponding position of the community, and analyze the scene influence value based on the scene situation at the corresponding position of the community. Among them, the calculation formula for the scene influence value is: , where N is the number of environmental types, bc is the influence coefficient of the c-th environment, zc is the specific value of the c-th environmental type at the corresponding position, zcm is the median of the safety range of the c-th environmental type, zcmax is the maximum value of the safety range of the c-th environmental type, and zcmin is the minimum value of the safety range of the c-th environmental type. In this way, the influence of the environment on the injury is quantified through the gap between the environment and the safety range; S22. Obtain the condition influence degree at the corresponding position based on the scene influence value and the true injury degree at the corresponding position. Among them, the condition influence degree at the r-th position is: , where v is the environmental sensitivity coefficient, which is used to analyze the sensitivity degree of the r-th position to the environment. Through this step, the influence of environmental characteristics on the patient's injured area is quantified, and the accuracy of the abnormal analysis of the patient's injured area is improved.

[0009] In an implementation manner of the present invention, the analysis of the matching degree of patient treatment by obtaining the historical treatment patient situation and the good at field situation of the corresponding community doctor includes the following specific contents: S31. Obtain the condition influence situations of all positions of the patient, and obtain the condition importance degree of each position based on the condition influence situations of all positions of the patient. Among them, the condition importance degree at the r-th position is: where Y is the number of positions. For a patient, the impact of the condition on different parts of the body may vary. By obtaining the impact of the condition on all positions, doctors can comprehensively and meticulously understand the overall condition of the patient. Understanding the specific situation of each position helps doctors formulate a more comprehensive treatment plan. Calculating the importance of the condition at each position can assist doctors in determining which positions have a greater impact on the patient's health. The weights of the conditions at different positions in the overall condition are different. Through this quantitative method, the conditions of key parts can be highlighted; S32. Substitute the importance of the condition at each position of the patient, the impact of the condition at each position, and the average impact of the condition at each position of the patients successfully treated by each community doctor into the matching value calculation formula to calculate the matching value between the patient and the doctor. The matching value calculation formula is: where Prm is the average impact of the condition at the r-th position of the patients successfully treated by the community doctor. Introducing the average impact of the condition at each position of the patients successfully treated by the community doctor as a reference can make full use of the experience of past successful treatments. This means that when selecting a doctor, not only the current patient's condition is considered, but also the situation of the patients with similar conditions successfully treated by this doctor in the past is referred to. The matching value calculation formula comprehensively considers the importance and impact of the condition at each position of the patient, as well as the average impact of the condition of the patients successfully treated by the community doctor. This comprehensive consideration of multiple factors can more comprehensively evaluate the matching degree between the patient and the doctor. Through this comprehensive calculation, a doctor who is relatively good at dealing with the conditions at each position can be found; S33. Select the community doctor corresponding to the largest matching value to go to the patient's location for the treatment of the patient, and send the selection information and patient information to the community doctor. This method can ensure that the right patient is assigned to the right doctor, achieving the optimal allocation of medical resources. It avoids the situation of poor treatment effect and waste of medical resources caused by randomly assigning patients to doctors.

[0010] In the second aspect, the present invention also provides an intelligent doctor appointment and consultation service method integrating doctor-patient community management, including the following specific steps: Obtain the data of the patient's described condition, community scenario data, and corresponding doctor situation data; Conduct an analysis of the patient's condition evolution according to the changes in the patient's condition; Analyze the impact of the scenario situation at the corresponding position in the community on the condition of the corresponding patient; Obtain the historical treatment situation of the corresponding community doctor and the situation of the fields of expertise to conduct an analysis of the matching degree of patient treatment; Allocate medical staff according to the analysis results of the matching degree of patient treatment.

[0011] In a third aspect, an electronic device provided by the present invention includes: a processor and a memory. Among them, a computer program that can be called by the processor is stored in the memory, and the processor executes an intelligent doctor appointment and consultation service method integrating doctor-patient community management by calling the computer program stored in the memory.

[0012] In a fourth aspect, a computer-readable storage medium provided by the present invention stores instructions, and when the instructions run on a computer, the computer is made to execute an intelligent doctor appointment and consultation service method integrating doctor-patient community management.

[0013] Compared with the prior art, this platform has the following advantages and beneficial effects: This platform synthesizes multi-dimensional data and uses an analysis model to accurately estimate the degree of injury, analyze the evolution of the condition and the environmental impact. Patients do not need to rush between different departments or hospitals to find a suitable doctor. The platform automatically matches the most suitable community doctor for the patient, and matches doctors and patients through multi-factor comprehensive matching, and assigns the patient to the most suitable doctor, avoiding waste and unreasonable use of medical resources and realizing the efficient use of medical resources. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] By reading the detailed description of the non-restrictive embodiments with reference to the following drawings, other features, purposes and advantages of the present invention will become more obvious: Figure 1 It is a schematic diagram of the overall process of System Embodiment 1 of the present invention; Figure 2 It is a schematic diagram of the analysis process of the patient processing matching degree of Method Embodiment 1 of the present invention; Figure 3 It is a schematic diagram of the structure of System Embodiment 2 of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0015] In order to make the above objects, features and advantages of the present invention more obvious and understandable, the following detailed description of the specific embodiments of the present invention will be made with reference to the drawings of the specification.

[0016] In the following description, many specific details are set forth in order to fully understand the present invention. However, the present invention can also be implemented in other ways different from the description herein. Those skilled in the art can make similar generalizations without departing from the connotation of the present invention. Therefore, the present invention is not limited by the specific embodiments disclosed below.

[0017] Secondly, the so-called "one embodiment" or "embodiment" herein refers to a specific feature, structure or characteristic that can be included in at least one implementation manner of the present invention. The "in one embodiment" that appears in different places in this specification does not all refer to the same embodiment, nor is it an embodiment that is separately or selectively mutually exclusive with other embodiments.

[0018] Example 1 As Figures 1 to 2 shown, this embodiment provides an intelligent doctor appointment and consultation service method integrating doctor-patient community management, which specifically includes the following steps: Obtain the disease condition data, community scenario data, and corresponding doctor condition data described by the patient; In this embodiment, the disease condition data includes the symptom condition data corresponding to the patient's description and the change data of the patient's physical characteristics. Among them, the patient's physical characteristic data can be obtained through the intelligent monitoring bracelet carried by the patient. The community scenario data includes the scenario environment data of the corresponding injury location in the community, which is used to analyze the impact of the nearby scenario on the patient's condition, and then estimate the condition based on the impact and evolution of the condition; Conduct an analysis of the patient's disease evolution according to the changes in the patient's condition; In this embodiment, it includes the following specific contents: First, obtain the accident situation described by the community personnel or the patient and the change data of the patient's condition. Based on the community personnel positioning module, quickly obtain the identity information of the corresponding patient. Since most communities now identify community personnel through face recognition and cameras are installed at various positions in the community, the identity information of the patient can be accurately identified through cameras and recognition technology. At the same time, obtain the collision situation of each position of the patient with the ground when the accident occurs. For example, the collision height and the number of collisions can be obtained through image processing. Falls or collisions of the elderly or children in the community are the main sources of injury. For example, falling when climbing stairs. Because when a patient has a fall or collision accident, the impact on internal organs usually needs to be considered, rather than just looking at the external impact. This is a conventional technical means in this field. Obtain the basic disease condition information of the patient through the patient's identity information. Since this is automatically completed in the system and the information is obtained for public safety, there is no problem of privacy leakage; Secondly, obtain the collision change situation of each position during the accident process and the basic disease condition of each position of the patient, and estimate the accident injury degree of each position of the patient by constructing a neural network model. Among them, the division of the accident injury degree is a conventional technical means in the prior art, and can be divided by using the division method of injury levels. Exemplarily, for example, the disability level can be divided into levels 1 to 12; The specific steps for constructing a neural network model to estimate the accident damage degree at each position of a patient are as follows: Obtain the collision change situation at each position during the historical accident process, the underlying disease situation at each position of the patient, and the accident damage degree at each position of the patient. Respectively, the collision change situation at each position during the historical accident process, the underlying disease situation at each position of the patient, and the accident damage degree at each position of the patient are used as the 85% weight, bias training set, and 15% weight, bias test set; Input the 85% weight, bias training set into the deep learning neural network model for training to obtain an initial deep learning neural network model; Use the 15% weight, bias test set to test the initial deep learning neural network model, and output the initial deep learning neural network model output that satisfies the maximum accuracy of the accident damage degree judgment at each position of the patient as the neural network model. Among them, the formula in the neural network model is: , where is the output k of the s-th neuron in the m + 1 layer, is the connection weight G between the j-th neuron in the m-th layer and the s-th neuron in the m + 1 layer, represents the input R of the j-th neuron in the m-th layer, represents the bias z of the linear relationship between the j-th neuron in the m-th layer and the s-th neuron in the m + 1 layer. Sigmoid() represents the Sigmoid activation function, and w is the number of input neurons in the neural network model of the m-th layer; This step significantly improves the prediction accuracy while ensuring real-time performance by deeply fusing dynamic collision features and static pathological features; Exemplarily, it is predicted that a fall causes a grade 5 concussion injury to the patient's brain, and at the same time, a grade 4 leg fracture injury is also taken into account; Finally, obtain the change situation of the patient's physical characteristics, and conduct patient physical abnormality analysis based on the stability and deterioration speed of the patient's physical characteristics. Among them, the patient physical abnormality analysis formula is: , where n is the type of physical characteristic data that can be monitored, such as body temperature, heart rate, etc., ri is the influence weight of the i-th physical characteristic, a is the physical stability influence coefficient, b is the deterioration speed influence coefficient, xi is the average value of the i-th physical characteristic during the monitoring time, xim is the median of the safety range of the i-th physical characteristic, xits is the monitoring data of the i-th physical characteristic at the end moment within the set monitoring duration, xitr is the monitoring data of the i-th physical characteristic at the starting moment within the set monitoring duration, ximax is the maximum value of the safety range of the i-th physical characteristic, and ximin is the minimum value of the safety range of the i-th physical characteristic. Among them, reflects the static cumulative effect of the characteristic value deviating from the safety median during the monitoring period and is suitable for detecting chronic abnormalities, Capture the dynamic change rate of the characteristic value during the monitoring period, which is more sensitive to acute deterioration. By quantifying the dual dimensions of stability and deterioration speed, it achieves more accurate anomaly detection than the traditional threshold method, obtains the accident injury degree at each position and the analysis results of the patient's physical abnormality, and obtains the true injury degree of the corresponding position through the accident injury degree at each position and the analysis results of the patient's physical abnormality. The calculation formula for the true injury degree at the r-th position is: , where Rr is the accident injury degree at the r-th position, f is the amplification factor, with a value ranging from 0.5 to 2, determined by the injury type, calibrating the parameters through the injury database (such as NTDB), and more accurately predicting the actual clinical risk through the non-linear coupling of injury and physical condition Analyze the impact of the scene situation at the corresponding position in the community on the condition of the corresponding patient; In this embodiment, it includes the following specific steps: First, obtain the scene situation at the corresponding position in the community, and analyze the scene impact value based on the scene situation at the corresponding position in the community. Among them, the calculation formula for the scene impact value is: , where N is the number of environmental types, bc is the impact coefficient of the c-th environment, zc is the specific value of the c-th environmental type at the corresponding position, zcm is the median of the safety range of the c-th environmental type, zcmax is the maximum value of the safety range of the c-th environmental type, and zcmin is the minimum value of the safety range of the c-th environmental type. In this way, the impact of the environment on the injury is quantified through the gap between the environment and the safety range, such as a fall injury under high temperature; Secondly, obtain the disease impact degree at the corresponding position based on the scene impact value and the true injury degree at the corresponding position. Among them, the disease impact degree at the r-th position is: , where v is the environmental sensitivity coefficient, used to analyze the sensitivity of the r-th position to the environment, obtained through experiments, with a default value of 0.8, and 1.2 for respiratory diseases, obtained through historical data experiments. By this step, the impact of environmental characteristics on the patient's injured area is quantified, improving the accuracy of the abnormal analysis of the patient's injured area; Obtain the historical treatment patient situation and the area of expertise of the corresponding community doctor to analyze the matching degree of patient treatment; In this embodiment, it includes the following specific content: First, obtain the disease impact situation of all positions of the patient, and obtain the disease importance degree of each position based on the disease impact situation of all positions of the patient. Among them, the disease importance degree at the r-th position is: , where Y is the number of positions. For a patient, the impact of the condition on different parts of the body may vary. By obtaining the impact of the condition on all positions, doctors can comprehensively and meticulously grasp the overall condition of the patient. For example, a patient with multiple traumas may have injuries in different positions such as the head, chest, and legs. Understanding the specific situation of each position helps doctors formulate a more comprehensive treatment plan. Calculating the importance of the condition at each position can assist doctors in determining which positions have a greater impact on the patient's health. The weights of the conditions at different positions in the overall condition are different. Through this quantitative method, the conditions of key parts can be highlighted; Then, substitute the importance of the condition at each position of the patient, the impact of the condition at each position, and the average impact of the condition at each position of the patients successfully treated by each community doctor into the matching value calculation formula to calculate the matching value between the patient and the doctor. Among them, the matching value calculation formula is: , where Prm is the average impact of the condition at the r-th position of the patients successfully treated by the community doctor. Introducing the average impact of the condition at each position of the patients successfully treated by the community doctor as a reference can make full use of the experience of past successful treatments. This means that when choosing a doctor, not only the current patient's condition is considered, but also the situation of the patients with similar conditions successfully treated by this doctor in the past is referred to. The matching value calculation formula comprehensively considers the importance of the condition and the impact of the condition at each position of the patient, as well as the average impact of the condition of the patients successfully treated by the community doctor. This comprehensive consideration of multiple factors can more comprehensively evaluate the matching degree between the patient and the doctor. For example, for a patient with a complex condition and different degrees of conditions in multiple positions, through this comprehensive calculation, a doctor who is relatively good at dealing with the conditions at each position can be found; Finally, select the community doctor corresponding to the largest matching value to go to the patient's location for the treatment of the patient, and send the selection information and patient information to the community doctor. This method can ensure that the appropriate patient is assigned to the appropriate doctor, realizing the optimal allocation of medical resources, and avoiding the situation of poor treatment effect and waste of medical resources caused by randomly assigning patients to doctors. For example, letting a doctor who is good at treating bone diseases treat a brain disease patient may delay the treatment time due to the doctor's lack of experience in dealing with brain diseases, and choosing a doctor through the matching value can effectively avoid this situation; Allocate medical staff according to the analysis results of the patient's matching degree; It should be specifically noted here that the value-taking method of the set parameters in the embodiments of the present application is: obtained through historical data experiments. The specific experimental method is: obtaining the disease condition data described by historical patients, community scenario data, and corresponding doctor situation data. At the same time, the most suitable doctor is selected through expert voting for patient treatment, and the data is imported into each step of this embodiment to calculate the community doctor corresponding to the maximum matching value. The matching value of the matching doctor and the voting selection value are imported into the fitting software to output the value-taking of the set parameters that meets the maximum selection accuracy rate; It should be noted in this embodiment that this embodiment has the following advantages: integrating multi-dimensional data, using the analysis model to accurately estimate the degree of injury, analyze the evolution of the disease condition and environmental impact. Patients do not need to rush between different departments or hospitals to find a suitable doctor. The platform automatically matches the most suitable community doctor for the patient. By comprehensively matching doctors and patients with multiple factors, the patient is assigned to the most suitable doctor, avoiding the waste and unreasonable use of medical resources and realizing the efficient utilization of medical resources.

[0019] Embodiment 2 As Figure 3 shown, this embodiment provides an intelligent doctor appointment and consultation service platform integrating doctor-patient community management, which is implemented based on the intelligent doctor appointment and consultation service method of integrating doctor-patient community management in Embodiment 1, including a doctor-patient information acquisition module, a patient disease condition evolution analysis module, a community situation anomaly analysis module, a doctor situation analysis module, and a doctor resource allocation module. Among them, the doctor-patient information acquisition module is used to obtain the disease condition data described by the patient, community scenario data, and corresponding doctor situation data. Among them, the disease condition data includes the symptom situation data corresponding to the patient's description and the change data of the patient's physical characteristics. Among them, the patient's physical characteristic data can be obtained through the intelligent monitoring bracelet carried by the patient. The community scenario data includes the scenario environment data of the corresponding injury location in the community, which is used to analyze the impact of the nearby scenario situation on the corresponding patient's disease condition, and then comprehensively estimate the disease condition situation based on the impact of the disease condition and the evolution of the disease condition. The patient disease condition evolution analysis module is used to analyze the evolution of the patient's disease condition according to the change of the patient's disease condition. The community situation anomaly analysis module is used to analyze the impact of the scenario situation at the corresponding location in the community on the corresponding patient's disease condition. The doctor situation analysis module is used to obtain the historical treatment patient situation and the expertise field situation of the corresponding community doctor to analyze the matching degree of patient treatment. The doctor resource allocation module is used to allocate medical staff according to the analysis result of the patient treatment matching degree. The specific steps of each module in the embodiment of this system are the same as the specific steps of the method embodiment in Embodiment 1, and will not be described again here.

[0020] Embodiment 3 An electronic device according to an embodiment of the present invention includes: a processor and a memory. Among them, a computer program that can be called by the processor is stored in the memory, and the processor executes an intelligent doctor appointment and consultation service method integrating doctor-patient community management by calling the computer program stored in the memory. It should be noted that: all computer programs of the intelligent doctor appointment and consultation service method integrating doctor-patient community management are implemented using the C language.

[0021] Embodiment 4 This embodiment provides a computer-readable storage medium, on which a rewritable computer program is stored; When the computer program runs on a computer device, it causes the computer device to execute the above-mentioned intelligent doctor appointment and consultation service method integrating doctor-patient community management.

[0022] The above embodiments can be implemented in whole or in part by software, hardware, firmware, or any other combination. When implemented using software, the above embodiments can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, the processes or functions according to the embodiments of the present invention are generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable devices. The computer instructions can be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via a wired network or / and a wireless network. The computer-readable storage medium can be any available medium that the computer can access, or a data storage device such as a server or data center that includes one or more collections of available media. The available medium can be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium. The semiconductor medium can be a solid-state drive.

[0023] Those of ordinary skill in the art can realize that the units and algorithm steps of each example described in combination with the embodiments disclosed in the present invention can be implemented by electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are executed in a hardware or software manner depends on the specific application and design constraints of the technical solution. Professional technicians can use different systems for each specific application to implement the described functions, but such implementation should not be considered to exceed the scope of the present invention.

[0024] Those skilled in the art can clearly understand that for the convenience and brevity of description, the specific working processes of the systems, devices, and units described above can refer to the corresponding processes in the foregoing system embodiments and will not be elaborated herein.

[0025] In several embodiments provided by the present invention, it should be understood that the disclosed systems, devices, and systems can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For example, the division of units is only one type, and there can be other division methods in actual implementation. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the displayed or discussed couplings or direct couplings or communication connections to each other can be through some interfaces, and the indirect couplings or communication connections of devices or units can be in electrical, mechanical, or other forms.

[0026] The units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they can be located in one place or distributed to multiple network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0027] In addition, the functional units in various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically alone, or two or more units can be integrated into one unit.

[0028] In the description of this specification, the descriptions referring to terms such as "one embodiment", "example", "specific example", etc. mean that the specific features, structures, materials, or characteristics described in connection with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials, or characteristics described can be combined in a suitable manner in any one or more embodiments or examples.

[0029] The above shows and describes the basic principles, main features, and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited by the above embodiments. The above embodiments and the descriptions in the specification only illustrate the principles of the present invention. Without departing from the spirit and scope of the present invention, the present invention will have various changes and improvements, and these changes and improvements fall within the scope of the present invention claimed. The scope of protection claimed by the present invention is defined by the appended claims and their equivalents.

Claims

1. An intelligent doctor appointment and consultation service platform integrating doctor-patient community management, characterized in that It includes a doctor-patient information acquisition module, a patient's condition evolution analysis module, a community situation anomaly analysis module, a doctor situation analysis module, and a doctor resource allocation module. Among them, the doctor-patient information acquisition module is used to acquire the condition data described by the patient, the community scene data, and the corresponding doctor situation data. The patient's condition evolution analysis module is used to analyze the patient's condition evolution according to the patient's condition changes. The community situation anomaly analysis module is used to analyze the impact of the scene situation at the corresponding position in the community on the condition of the corresponding patient. The doctor situation analysis module is used to acquire the historical treatment patient situation and the expertise field situation of the corresponding community doctor to analyze the matching degree of patient treatment. The doctor resource allocation module is used to allocate medical staff according to the analysis result of the patient treatment matching degree.

2. The intelligent doctor appointment and consultation service platform integrating doctor-patient community management according to claim 1, characterized in that The analysis of the patient's condition evolution according to the patient's condition changes includes the following specific contents: Acquire the accident situation described by community members or patients and the condition change data of the patient. Based on the community member positioning module, quickly acquire the identity situation of the corresponding patient, and acquire the underlying disease situation information of the patient through the patient's identity information. Acquire the collision change situations at various positions during the accident process and the underlying disease situations of various positions of the patient, and estimate the accident injury degree of various positions of the patient by constructing a neural network model. Obtain the changes in the physical characteristics of a patient, and conduct an analysis of abnormal patient physique based on the stability and deterioration speed of the patient's physical characteristics. Among them, the formula for analyzing abnormal patient physique is: , where n is the type of physical characteristic data that can be monitored, such as body temperature, heart rate, etc., ri is the influence weight of the i-th physical characteristic, a is the physical stability influence coefficient, b is the deterioration speed influence coefficient, xi is the average value of the i-th physical characteristic during the monitoring time, xim is the median of the safety range of the i-th physical characteristic, xits is the monitoring data of the i-th physical characteristic at the end moment within the set monitoring duration, xitr is the monitoring data of the i-th physical characteristic at the starting moment within the set monitoring duration, ximax is the maximum value of the safety range of the i-th physical characteristic, and ximin is the minimum value of the safety range of the i-th physical characteristic; The true degree of injury at each position is obtained through the analysis results of the accident injury degree and the physical abnormality of the patient. The calculation formula for the true degree of injury at the r-th position is: , where Rr is the accident injury degree at the r-th position, and f is the amplification factor.

3. The intelligent doctor appointment and consultation service platform integrating doctor-patient community management according to claim 2, characterized in that, The analysis of the impact of the scene situation at the corresponding position in the community on the condition of the corresponding patient includes the following specific steps: Acquire the scene situation at the corresponding position in the community, and analyze the scene impact value based on the scene situation at the corresponding position in the community. Obtain the disease condition impact degree of the corresponding location based on the scenario impact value and the true damage degree of the corresponding location. Among them, the disease condition impact degree of the r-th location is: , where v is the environmental sensitivity coefficient and Ks is the scenario impact value.

4. The intelligent doctor appointment and consultation service platform integrating doctor-patient community management according to claim 3, characterized in that, The acquisition of the historical treatment patient situation and the expertise field situation of the corresponding community doctor to analyze the matching degree of patient treatment includes the following specific contents: Obtain the disease impact conditions at all positions of the patient, and obtain the disease importance levels of each position based on the disease impact conditions at all positions of the patient. Among them, the disease importance level of the r-th position is: , where Y is the number of positions; Acquire the importance degree of the condition of various positions of the patient, the impact situation of the condition of various positions, and the average impact situation of the condition of various positions of the patients successfully treated by each community doctor, and substitute them into the matching value calculation formula to calculate the matching value between the patient and the doctor. Select the community doctor corresponding to the maximum matching value to go to the patient's location for patient treatment, and send the selection information and patient information to the community doctor.

5. The intelligent doctor appointment and consultation service platform integrating doctor-patient community management according to claim 1, characterized in that, The doctor-patient information acquisition module includes a patient condition acquisition unit, a community scene data acquisition unit, and a doctor situation acquisition unit. Among them, the patient condition acquisition unit is used to acquire the symptom situation data described by the patient and the physical characteristic change data of the patient. The community scene data acquisition unit is used to acquire the accident scene situation at the corresponding community scene position and the environmental situation at the corresponding position. Among them, the environmental situation includes environmental data such as temperature and pollutant situation that affect the patient's condition. The doctor situation acquisition unit is used to acquire the historical treatment patient situation and the expertise field situation of the corresponding community doctor.

6. The intelligent doctor appointment and consultation service platform integrating doctor-patient community management according to claim 2, characterized in that, The estimation of the accident injury degree of various positions of the patient by constructing a neural network model includes the following specific contents: The specific steps for constructing a neural network model to estimate the accident injury degree of each position of a patient are as follows: Obtain the collision change conditions of each position during the historical accident process, the underlying disease conditions of each position of the patient, and the accident injury degree of each position of the patient. Respectively, the collision change conditions of each position during the historical accident process, the underlying disease conditions of each position of the patient, and the accident injury degree of each position of the patient are used as the 85% weight, bias training set, and 15% weight, bias test set; Input the 85% weight, bias training set into the deep learning neural network model for training to obtain an initial deep learning neural network model; Use the 15% weight, bias test set to test the initial deep learning neural network model, and output the initial deep learning neural network model output that meets the maximum accuracy of the accident injury degree judgment of each position of the patient as the neural network model. Among them, the formula in the neural network model is: , where is the output k of the s-th neuron in the (m + 1)-th layer, is the connection weight G between the j-th neuron in the m-th layer and the s-th neuron in the (m + 1)-th layer, represents the input R of the j-th neuron in the m-th layer, represents the bias z of the linear relationship between the j-th neuron in the m-th layer and the s-th neuron in the (m + 1)-th layer. Sigmoid() represents the Sigmoid activation function, and w is the number of input neurons in the m-th layer of the neural network model.

7. The intelligent doctor appointment and consultation service platform integrating doctor-patient community management according to claim 6, characterized in that, The calculation formula for the matching value is as follows: , where Prm is the average disease condition impact of the r-th position of the patients successfully treated by community doctors.

8. The intelligent doctor appointment and consultation service platform integrating doctor-patient community management according to claim 7, characterized in that, The calculation formula for the scenario influence value is as follows: , where N is the number of environmental types, bc is the influence coefficient of the c-th environment, zc is the specific value of the c-th environmental type at the corresponding position, zcm is the median of the safety range of the c-th environmental type, zcmax is the maximum value of the safety range of the c-th environmental type, and zcmin is the minimum value of the safety range of the c-th environmental type.

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