An intelligent safety nursing monitoring system and method

Abstract

The present invention relates to the field of smart medical technology, and in particular to an intelligent safety nursing monitoring method and method, which collects monitoring information of persons to be cared for, extracts the monitoring time and space information of persons at home and before home from the monitoring information of persons to be cared for; performs parsing operation on the monitoring time and space information of persons to be cared for, and determines whether the monitoring information of persons to be cared for is stable and reliable through deviation value ratio analysis; identifies dangerous factors in the monitoring information of persons to be cared for, and pushes the dangerous factor information of the monitoring information of persons to be cared for; traverses the safety evaluation matrix elements of the dangerous factor information one by one to see whether there are a preset number of safety evaluation results, and if so, selects the one with the largest number of the same safety evaluations to push, otherwise it is determined that the safety evaluation results of the dangerous factor information are not pushed. The present invention cooperates with the detection and early warning model of falls, can eliminate occasional instability, improves the results of early warning errors after reduction, and makes the overall detection results more effective.

Description

Technical Field

[0001] The present invention belongs to the field of home scene monitoring, and specifically relates to an intelligent safety care monitoring system and method. Background Art

[0002] Balance disorders such as vertigo are common diseases that seriously affect human health and quality of life. They manifest as dizziness, nausea, vomiting, cold sweats and other symptoms of autonomic nervous system disorders, often leading to psychological disorders such as panic and anxiety. Those with severe conditions cannot take care of themselves, which places a great burden on their families and society. Statistics show that the number of vertigo patients in my country can reach 70 million per year, accounting for about 3.4-4.9% of the total population. With the increase of age, the risk of disease will increase significantly. Among them, patients with "vertigo" as the main complaint account for about 80% of geriatric outpatient clinics. Secondary injuries such as falls caused by vertigo in elderly patients, such as fractures and brain trauma, are a serious threat to the health and quality of life of the elderly.

[0003] Moreover, observation of the monitoring person is often very harmful, and the occurrence of dizziness cannot be determined based solely on the monitoring person's situation. Generally speaking, when dizziness occurs, the home scene changes significantly with the action, but the existing technology lacks a dizziness prediction solution for home scene recognition. Summary of the invention

[0004] According to a first aspect of the present invention, the present invention claims protection for an intelligent safety nursing monitoring system, characterized in that it comprises:

[0005] A spatiotemporal information collection unit collects monitoring information of the person to be cared for, and extracts the monitoring spatiotemporal information of the person at home and before home from the monitoring information of the person to be cared for;

[0006] An analysis unit performs an analysis operation on the personnel monitoring time-space information, and determines whether the monitoring information of the person to be cared for is stable and reliable through deviation value ratio analysis;

[0007] A risk factor identification unit, which identifies risk factors of the monitoring information of the person to be cared for, and pushes the risk factor information of the monitoring information of the person to be cared for;

[0008] A front safety evaluation unit, when the monitoring information of the person to be cared for is stable and reliable, collects the safety evaluation model of the risk factor information and pushes the safety evaluation result of the risk factor information;

[0009] The fall safety assessment unit traverses the safety assessment matrix elements of the hazardous factor information one by one to see if there are a preset number of safety assessment results. If so, the one with the largest number of identical safety assessments is selected for push; otherwise, it is determined that the safety assessment result of the hazardous factor information will not be pushed.

[0010] Furthermore, the parsing operation of the personnel monitoring spatiotemporal information and determining whether the monitoring information of the person to be cared for is stable and reliable by analyzing the deviation value ratio may also include:

[0011] The correlation between the two sets of monitoring information in the time-space information of personnel monitoring at home and before home is determined by deviation value ratio analysis.

[0012] If the correlation is not greater than a preset correlation value, it is determined that the monitoring information of the nursing staff is credible.

[0013] Furthermore, the method of determining the correlation between the two sets of monitoring information in the personnel monitoring time-space information at home and before home by using the deviation value ratio analysis also includes:

[0014] The spatiotemporal information of personnel monitoring at home and before home is changed into parameter clustering, and expression recognition operation is performed;

[0015] Performing Euclidean distance transformation on the personnel monitoring spatiotemporal information of the warning standardized operation during and before staying at home;

[0016] The number of unadapted spatiotemporal information of personnel monitoring at home and before home, when the number of unadapted is not greater than the conditional value, it is determined that the monitoring information of the person to be cared for is stable and reliable, otherwise it is in a stable and invalid state.

[0017] Furthermore, the step of identifying risk factors of the monitoring information of the person to be cared for and pushing risk factor information of the monitoring information of the person to be cared for further includes:

[0018] The hazardous factor information includes the hazardous factor coordinates of the hazardous factor, the hazardous factor safety evaluation result, the number of updated fall factors, and whether a new hazardous factor safety evaluation result is added.

[0019] Furthermore, the step of identifying risk factors of the monitoring information of the person to be cared for and pushing risk factor information of the monitoring information of the person to be cared for further includes:

[0020] Identify the urgency value conditions of facial expressions of pain one day apart in the monitoring information of the person to be cared for one by one, and determine whether the urgency value of a certain fall factor on the previous day and a certain fall factor when at home is not greater than a first correlation value, and the urgency value is the maximum value of the comparison of all facial expressions of pain on other days and when at home;

[0021] If the conditions are met, it is considered that a certain fall factor of the previous day is the same as a certain fall factor at home, and the information of the fall factor is updated. If the conditions are not met, the number of fall factor updates already exists + 1, and the new fall factor initializes the risk factor information of the fall factor.

[0022] Furthermore, the step of identifying risk factors of the monitoring information of the person to be cared for and pushing risk factor information of the monitoring information of the person to be cared for further includes:

[0023] When there is an updated fall requirement that has been updated continuously for five days in the monitoring information of the person to be cared for, the updated fall requirement is identified as an invalid fall requirement, and all information of the invalid fall requirement is deleted;

[0024] When the invalid fall element causes three updates during the urgency value adaptation process because the conditions for successful adaptation are not met, and when the invalid fall element exists after five days of observation, it is identified as a new fall element.

[0025] Furthermore, when the monitoring information of the nursing personnel is stable and reliable, the safety evaluation model of the risk factor information is collected and the safety evaluation result of the risk factor information is pushed, which also includes:

[0026] The hazardous factor information is collected into a safety evaluation model. If the hazardous factor information has existing information, the safety evaluation result of the hazardous factor information is saved in the safety evaluation result of the existing information. Otherwise, the safety evaluation result of the hazardous factor information is stored in an initialized safety evaluation matrix element.

[0027] Further, the step of traversing the safety evaluation matrix elements of the hazardous factor information one by one to determine whether there are a preset number of safety evaluation results, and if so, selecting the one with the largest number of identical safety evaluation results to be pushed, and otherwise determining that the safety evaluation result of the hazardous factor information is not pushed, further includes:

[0028] Initialize the safety evaluation matrix elements, collect the safety evaluation results of the first day's dangerous factor information that meet the trustworthy conditions, and put the safety evaluation results of the first day's dangerous factor information into the safety evaluation matrix elements;

[0029] Collect the safety evaluation results of the second day's hazardous factor information, put the safety evaluation results of the second day's hazardous factor information into the safety evaluation matrix element, and loop until the safety evaluation results of the hazardous factor information of all observation days are put into the safety evaluation matrix element;

[0030] Determine whether the safety evaluation matrix element has a preset number of safety evaluation results. If so, select the one with the largest number of identical safety evaluation results to be pushed. Otherwise, determine that the safety evaluation result of the hazardous factor information will not be pushed.

[0031] Further, when the monitoring information of the person to be cared for is in a stable invalid state, it is determined through the adaptation condition whether to follow the safety evaluation result of the previous fall requirement;

[0032] When the risk factor information is an existing fall condition that has not been adapted, it is a fall condition that has not been found at home but has been found and recorded in the past, and there is no need to inherit it;

[0033] Update the update number of the existing fall element to update number + 1;

[0034] When the risk factor information is a newly added fall factor that is not adapted, it is a fall factor that exists at home but has not been discovered before, and there is no inheritance condition;

[0035] When the hazardous factor information is a fall condition with existing information after adaptation and also exists at home, it is determined whether the number of days with safety evaluation results of the hazardous factor information reaches a preset number. If so, the push with the largest number of identical safety evaluations is selected, otherwise it is determined that the safety evaluation results of the hazardous factor information will not be pushed.

[0036] According to a second aspect of the present invention, the present invention seeks protection for an intelligent safety nursing monitoring method, characterized in that it is used to execute the workflow of the intelligent safety nursing monitoring system.

[0037] The present invention relates to the field of smart medical technology, and in particular to an intelligent safety nursing monitoring method and method, which collects monitoring information of persons to be cared for, extracts the monitoring time and space information of persons at home and before home from the monitoring information of persons to be cared for; performs parsing operation on the monitoring time and space information of persons to be cared for, and determines whether the monitoring information of persons to be cared for is stable and reliable through deviation value ratio analysis; identifies dangerous factors in the monitoring information of persons to be cared for, and pushes the dangerous factor information of the monitoring information of persons to be cared for; traverses the safety evaluation matrix elements of the dangerous factor information one by one to see whether there are a preset number of safety evaluation results, and if so, selects the one with the largest number of the same safety evaluations to push, otherwise it is determined that the safety evaluation results of the dangerous factor information are not pushed. The present invention cooperates with the detection and early warning model of falls, can eliminate occasional instability, improves the results of early warning errors after reduction, and makes the overall detection results more effective. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] Figure 1 A structural module diagram of an intelligent safety nursing monitoring system claimed for protection in an embodiment of the present application;

[0039] Figure 2 A workflow diagram of an intelligent safety nursing monitoring system claimed for protection in an embodiment of the present application;

[0040] Figure 3 A second working flow chart of an intelligent safety nursing monitoring system claimed for protection in an embodiment of the present application;

[0041] Figure 4This is the third workflow diagram of an intelligent safety nursing monitoring system claimed for protection in an embodiment of the present application. DETAILED DESCRIPTION

[0042] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0043] According to the first embodiment of the present invention, the present invention claims an intelligent safety nursing monitoring system, referring to the attached Figure 1 , including spatiotemporal information acquisition unit, analysis unit, risk factor identification unit, pre-safety evaluation unit, and fall safety evaluation unit, refer to Figure 2 The working steps of the system include:

[0044] S1, collecting monitoring information of the person to be cared for, and extracting the monitoring time-space information of the person at home and before home from the monitoring information of the person to be cared for;

[0045] S2, analyze the spatiotemporal information of personnel monitoring, and determine whether the monitoring information of the nursing personnel is stable and reliable through deviation value ratio analysis;

[0046] S3, identifying dangerous factors in the monitoring information of the nursing staff, and pushing dangerous factor information of the monitoring information of the nursing staff;

[0047] S4, when the monitoring information of the nursing staff is stable and reliable, the safety evaluation model of the dangerous factor information is collected and the safety evaluation result of the dangerous factor information is pushed;

[0048] S5, traverse the safety evaluation matrix elements of the hazardous factor information one by one to see if there are a preset number of safety evaluation results. If so, select the one with the largest number of identical safety evaluation results to be pushed; otherwise, determine that the safety evaluation results of the hazardous factor information will not be pushed.

[0049] Among them, in this embodiment, for a specific action of the person to be cared for, the home activity scene will obtain the action monitoring data corresponding to the activity behavior. The action monitoring data mainly includes the time, place, home activity scene of the action, etc., which are used to describe the current action of the person.

[0050] Furthermore, step S2 further includes the steps of:

[0051] S21, determining the correlation between two sets of monitoring information in the time-space information of personnel monitoring at home and before home through deviation value ratio analysis,

[0052] S22, if the correlation is not greater than the preset correlation value, it is determined that the monitoring information of the nursing staff is credible, and a credible correlation value is set. The correlation value is obtained through multiple tests.

[0053] Among them, in this embodiment, for the determined person to be cared for, their actions have certain recommended treatment rules to follow. Based on abnormal actions, the home activity scene can characterize the abnormal action time of the person to be cared for, and obtain specific rules, that is, the distribution of abnormal action occurrence dates corresponding to the person to be cared for. The distribution of abnormal action occurrence dates can effectively illustrate the abnormal action time rules of the person to be cared for within a recommended treatment time period. According to the abnormal action occurrence date distribution display, the weight of the person to be cared for on a specific occurrence date can be determined, and the action time fall risk weight of the activity behavior can be determined; the action time fall risk weight can be used to characterize the duration of abnormal actions of the responding personnel after delivery, and then can be used to indicate the post-delivery recovery fall risk of the assessed personnel.

[0054] Further, see Figure 3 , step S21, further comprising:

[0055] S211, changing the spatiotemporal information of personnel monitoring at home and before home into parameter clustering, and performing expression recognition operation;

[0056] S212, performing Euclidean distance transformation on the personnel monitoring spatiotemporal information during and before the warning standard operation of staying at home;

[0057] S213, the number of unadapted spatiotemporal information of personnel monitoring at home and before home. When the number of unadapted information is not greater than the condition value, it is determined that the monitoring information of the person to be cared for is stable and reliable, otherwise it is in a stable and invalid state.

[0058] Among them, in this embodiment, for the determined person to be cared for, based on the abnormal actions of the person to be cared for, the home activity scene can characterize the characteristics of the area where the abnormal actions occur within a period of time, that is, the distribution of the abnormal action occurrence area. The distribution shows the weight of a person's action in a specific occurrence area. Based on the distribution of the abnormal action occurrence area, the weight of the person to be cared for in a specific occurrence area can be determined, and the fall risk weight of the action occurrence area of ​​the activity behavior can be determined.

[0059] Furthermore, step S3 further includes:

[0060] The hazardous factor information includes the hazardous factor coordinates of the hazardous factor, the hazardous factor safety evaluation results, the number of updated fall factors, and whether there are new hazardous factor safety evaluation results.

[0061] Furthermore, step S3 further includes:

[0062] Identify the urgency value conditions of facial expressions of pain one day apart in the monitoring information of the nursing personnel one by one, and determine whether the urgency value of a certain fall factor on the previous day and a certain fall factor at home is not greater than a first correlation value, and the urgency value is the maximum value of the comparison of all facial expressions of pain on other days and at home;

[0063] If the conditions are met, it is considered that a certain fall factor of the previous day is the same as a certain fall factor at home, and the information of the fall factor is updated. If the conditions are not met, the number of fall factor updates already exists + 1, and the new fall factor initializes the risk factor information of the fall factor.

[0064] Among them, in this embodiment, for the determined person to be cared for, the interval between two consecutive actions is also one of the important factors for judging whether the action has a fall risk. For example, for a specific person to be cared for, his past abnormal actions have never included two consecutive actions within less than one hour. Then, when the time interval between the activity behavior of the person to be cared for and the last action is less than 1, his fall risk weight is relatively large. By analyzing the abnormal actions of the person to be cared for, the home activity scene can determine the corresponding abnormal action time interval distribution, and based on this distribution, the fall risk weight of a specific action time interval of the person to be cared for can be determined.

[0065] Furthermore, step S3 further includes:

[0066] When there are updated fall elements that have been updated for five consecutive days in the monitoring information of the waiting nursing staff, the updated fall elements are identified as invalid fall elements, and all information of the invalid fall elements is deleted;

[0067] When an invalid fall element is updated three times during the urgency value adaptation process because the conditions for successful adaptation are not met, and when the invalid fall element exists after five days of observation, it is considered to be a new fall element.

[0068] In this embodiment, the specific names used are explained as follows:

[0069] Unadapted existing fall elements: the previously saved facial expression of pain information does not meet the adaptation conditions with the facial expression of pain existing at home, that is, the existing fall elements are updated at home;

[0070] Unmatched new fall elements: Information and history of a fall element at home found that the recorded facial expression of pain did not meet the matching conditions;

[0071] New fall elements that are successfully matched with existing fall elements: A facial expression of pain at home is matched with the facial expression of pain recorded in historical findings and is considered to be the same fall element.

[0072] Among them, in this embodiment, for the determined person to be cared for, there are certain rules between the home activity scenes of the actions. The home activity scene analyzes the abnormal actions of the person to be cared for, and obtains a weight matrix (abnormal action state transition matrix) for evaluating the change of the person to be cared for between different home activity scenes, that is, the weight of jumping from the home activity scene A corresponding to the previous action to the home activity scene B corresponding to the next action. When a new action needs to be evaluated, as long as the home activity scene of the previous action of the activity behavior is determined, the fall risk weight of the home activity scene change of the person to be cared for from the matching home activity scene to the current home activity scene can be determined from the abnormal action state transition matrix.

[0073] Among them, the abnormal action state transition matrix is ​​a matrix that represents the state transition of abnormal actions of high-risk personnel after delivery. The elements of the matrix are the time, duration, and pain intensity information of abnormal actions.

[0074] The home activity scene shows the information of people who participate in home activities and home activity wards, and is used to characterize the stability of the condition of high-risk persons. When the condition of high-risk persons is relatively stable, the home activity scene will not be changed frequently, otherwise it will need to be changed frequently. Therefore, the fall risk weight of changing the home activity scene is used to characterize the health stability of high-risk persons.

[0075] Furthermore, step S4 further includes:

[0076] The safety evaluation model is collected for the hazardous factor information. If the hazardous factor information has existing information, the safety evaluation result of the hazardous factor information is saved in the safety evaluation result of the existing information. Otherwise, the safety evaluation result of the hazardous factor information is stored in the initialized safety evaluation matrix element.

[0077] Among them, in this embodiment, for the determined person to be cared for, the action time, the action area, the change of the home activity scene and the action time interval are respectively one aspect of an action, and are all influencing factors for the fall risk assessment of the person's action. When the fall risk assessment is performed on the specific person's action, it is necessary to determine the weight ratio of the action time fall risk weight, the action area fall risk weight, the home activity scene change fall risk weight and the action time interval fall risk weight. In one embodiment, the above-mentioned weight ratios can be the same or different from each other, and this is not specifically limited in this embodiment. Therefore, the home activity scene needs to comprehensively consider the action time fall risk weight, the action area fall risk weight, the home activity scene change fall risk weight and the action time interval fall risk weight, in order to accurately assess whether a specific action is a fall risk, that is, determine the weight ratio corresponding to each influencing factor according to demand.

[0078] In the above-mentioned personnel fall risk action monitoring method, the activity behavior monitoring data such as the activity behavior occurrence date, the activity behavior occurrence area and the current home activity scene identifier of the current home activity scene corresponding to the activity behavior of the person to be cared for are combined with the abnormal action monitoring data corresponding to the last action of the person to be cared for, to determine the action time fall risk weight, action occurrence area fall risk weight, home activity scene change fall risk weight and action time interval fall risk weight corresponding to the activity behavior of the person to be cared for, and perform fall risk monitoring on the activity behavior of the person to be cared for. Among them, based on the characteristic curve (distribution) corresponding to each fall risk feature (action time, action occurrence area, home activity scene change and action time interval) of the abnormal action of the person to be cared for, the fall risk weights of the action time, action occurrence area, home activity scene change and action time interval that are most relevant to the person's action are fully considered, thereby improving the accuracy of the judgment of the fall risk of the action of the person to be cared for.

[0079] Further, see Figure 4 , step S5, further comprising:

[0080] S51, initializing the safety evaluation matrix elements, collecting the safety evaluation results of the first day's dangerous factor information that meet the trust conditions, and putting the safety evaluation results of the first day's dangerous factor information into the safety evaluation matrix elements;

[0081] S52, collect the safety evaluation results of the second day's hazardous factor information, put the safety evaluation results of the second day's hazardous factor information into the safety evaluation matrix element, and loop until the safety evaluation results of the hazardous factor information of all observation days are put into the safety evaluation matrix element;

[0082] S53, determining whether there are a preset number of safety evaluation results in the safety evaluation matrix element, if so, selecting the one with the largest number of identical safety evaluation results to be pushed, otherwise determining that the safety evaluation result of the hazardous factor information will not be pushed.

[0083] Among them, in this embodiment, under the condition that the trusted conditions are met, the safety evaluation model is collected for the fall conditions at home, and the number of existing fall conditions that have not been adapted is updated by +1; the safety evaluation model is collected for the new fall conditions that have not been adapted, and the results at home are pushed and saved in the safety evaluation matrix elements; the new fall conditions that are successfully adapted to the existing fall conditions can collect the safety evaluation model, and the pushed home conditions are added to the safety evaluation matrix elements, and then the safety evaluation with the largest number is selected and pushed.

[0084] Furthermore, the system also includes:

[0085] When the monitoring information of the nursing staff is in a stable and invalid state, it is determined through the adaptation conditions whether the safety evaluation results of the previous fall elements can be followed;

[0086] When the risk factor information is an existing fall factor that has not been adapted, it is a fall factor that has not been found at home but has been found and recorded in the past, and there is no need to inherit it;

[0087] Update the update number of the existing fall element to update number + 1;

[0088] When the risk factor information is a newly added fall factor that has not been adapted, it is a fall factor that existed at home but has not been discovered before, and there is no inheritance condition;

[0089] When the hazardous factor information is a fall condition with existing information after adaptation and also exists at home, it is determined whether the number of days with safety evaluation results with hazardous factor information reaches the preset number. If so, the one with the largest number of identical safety evaluations is selected for push; otherwise, it is determined that the safety evaluation results of the hazardous factor information will not be pushed.

[0090] Among them, in this embodiment, if the conditions are not met, the safety evaluation model will not be collected for the fall factor at home, and it can only be determined through the adaptation conditions whether the safety evaluation results of the previous fall factors can be inherited. The existing fall factors that have not been adapted are fall factors that have not been discovered at home but have been discovered and recorded in history, and there is no need to inherit. Therefore, it is only necessary to update the update number of the existing fall factors to the update number + 1; the newly added fall factors that have not been adapted only exist when at home, and there is no inheritance condition for the fall factors that have not been discovered before; only the fall factors that have existing information and are also adapted at home need to determine whether the existing results can be inherited.

[0091] In the embodiments provided in the present application, 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 only schematic. For example, the division of units is only a logical function division. There may 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 performed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be an indirect coupling or communication connection through some interfaces, devices or units, which can be electrical, mechanical or other forms.

[0092] The specific implementation methods of the invention are described in detail above, but they are only examples, and the present application is not limited to the specific implementation methods described above. For those skilled in the art, any equivalent modification or substitution of the invention is also within the scope of the present application, and therefore, the equal conversion, modification, improvement, etc. made without departing from the spirit and principle of the present application should be included in the scope of the present application.

Claims

1. An intelligent safety nursing monitoring system, characterized in that: include: A spatiotemporal information collection unit collects monitoring information of the person to be cared for, and extracts the monitoring spatiotemporal information of the person at home and before home from the monitoring information of the person to be cared for; An analysis unit performs an analysis operation on the personnel monitoring time-space information, and determines whether the monitoring information of the person to be cared for is stable and reliable through deviation value ratio analysis; A risk factor identification unit, which identifies risk factors of the monitoring information of the person to be cared for, and pushes the risk factor information of the monitoring information of the person to be cared for; A front-end safety evaluation unit, when the monitoring information of the person to be cared for is stable and reliable, inputs the risk factor information into a safety evaluation model and pushes the safety evaluation result of the risk factor information; The fall safety assessment unit traverses the safety assessment matrix elements of the hazardous factor information one by one to see whether there are a preset number of safety assessment results. If so, the safety assessment result with the largest number is selected for push; otherwise, it is determined that the safety assessment result of the hazardous factor information is not pushed; The parsing operation of the personnel monitoring time-space information and determining whether the monitoring information of the person to be cared for is stable and reliable by analyzing the deviation value ratio also includes: The correlation between the two sets of monitoring information in the time-space information of personnel monitoring at home and before home is determined by deviation value ratio analysis. If the correlation is not greater than the preset correlation value, it is determined that the monitoring information of the nursing staff is credible; The determining of the correlation between the two sets of monitoring information in the time-space information of personnel monitoring at home and before home by the deviation value ratio analysis also includes: The spatiotemporal information of personnel monitoring at home and before home is changed into parameter clustering, and expression recognition operation is performed; Performing Euclidean distance transformation on the personnel monitoring spatiotemporal information of the warning standardized operation during and before staying at home; The number of unadapted spatiotemporal information of the personnel monitoring at home and before home, when the number of unadapted is not greater than the condition value, the monitoring information of the person to be cared for is determined to be stable and reliable, otherwise it is in a stable and invalid state; The step of identifying the risk factors of the monitoring information of the person to be cared for and pushing the risk factor information of the monitoring information of the person to be cared for further includes: The risk factor information includes the risk factor coordinates of the risk factor, the risk factor safety evaluation results, the number of fall element updates, and whether a new risk factor safety evaluation result is added; the risk factor identification of the monitoring information of the person to be cared for and the pushing of the risk factor information of the monitoring information of the person to be cared for also include: Identify the urgency value conditions of facial expressions of pain one day apart in the monitoring information of the person to be cared for one by one, and determine whether the urgency value of a certain fall factor on the previous day and a certain fall factor when at home is not greater than a first correlation value, and the urgency value is the maximum value of the comparison of all facial expressions of pain on other days and when at home; If the condition is met, it is considered that a certain fall factor of the previous day is the same as a certain fall factor at home, and the information of the fall factor is updated; if the condition is not met, the number of fall factor updates that already exists + 1, and the new fall factor initializes the risk factor information of the fall factor; the risk factor identification of the monitoring information of the person to be cared for and the pushing of the risk factor information of the monitoring information of the person to be cared for also include: When there is an updated fall requirement that has been updated continuously for five days in the monitoring information of the person to be cared for, the updated fall requirement is identified as an invalid fall requirement, and all information of the invalid fall requirement is deleted; When the invalid fall element is updated three times during the urgency value adaptation process because the conditions for successful adaptation are not met, and when the invalid fall element exists after five days of observation, it is identified as a new fall element; When the monitoring information of the nursing personnel is stable and reliable, the risk factor information is input into the safety evaluation model, and the safety evaluation result of the risk factor information is pushed, which also includes: Input the hazardous factor information into a safety evaluation model, and if the hazardous factor information has existing information, save the safety evaluation result of the hazardous factor information into the safety evaluation result of the existing information, otherwise store the safety evaluation result of the hazardous factor information in an initialized safety evaluation matrix element; The step of traversing the safety evaluation matrix elements of the hazardous factor information one by one to determine whether there are a preset number of safety evaluation results, and if so, selecting the one with the largest number of safety evaluation results to be pushed, and otherwise determining that the safety evaluation results of the hazardous factor information are not pushed, further includes: Initialize the safety evaluation matrix elements, collect the safety evaluation results of the first day's dangerous factor information that meet the trustworthy conditions, and put the safety evaluation results of the first day's dangerous factor information into the safety evaluation matrix elements; Collect the safety evaluation results of the hazardous factor information of the second day, put the safety evaluation results of the hazardous factor information of the second day into the safety evaluation matrix elements, and loop until the safety evaluation results of the hazardous factor information of all observation days are put into the safety evaluation matrix elements; Determine whether there are a preset number of safety evaluation results in the safety evaluation matrix element, if so, select the safety evaluation result with the largest number to be pushed, otherwise determine that the safety evaluation result of the hazardous factor information is not pushed; When the monitoring information of the person to be cared for is in a stable invalid state, determining whether to follow the safety evaluation result of the previous fall requirement through the adaptation condition; When the risk factor information is an existing fall condition that has not been adapted, it is a fall condition that has not been found at home but has been found and recorded in the past, and there is no need to inherit it; Update the update number of the existing fall element to update number + 1; When the risk factor information is a newly added fall factor that has not been adapted, it is a fall factor that exists at home but has not been discovered before, and there is no inheritance condition; When the hazardous factor information is a fall condition with existing information after adaptation and also exists at home, it is determined whether the number of safety evaluation results with the hazardous factor information reaches a preset number. If so, the safety evaluation results with the largest number are selected for push; otherwise, it is determined that the safety evaluation results of the hazardous factor information will not be pushed.

2. An intelligent safety nursing monitoring method, characterized in that: Used to execute the workflow of an intelligent safety nursing monitoring system as described in claim 1.

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