CN116090691B - Community cloud platform health management method - Google Patents
Community cloud platform health management method Download PDFInfo
- Publication number
- CN116090691B CN116090691B CN202310364567.6A CN202310364567A CN116090691B CN 116090691 B CN116090691 B CN 116090691B CN 202310364567 A CN202310364567 A CN 202310364567A CN 116090691 B CN116090691 B CN 116090691B
- Authority
- CN
- China
- Prior art keywords
- community
- time
- personnel
- subinterval
- health
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
Images
Classifications
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02A—TECHNOLOGIES FOR ADAPTATION TO CLIMATE CHANGE
- Y02A90/00—Technologies having an indirect contribution to adaptation to climate change
- Y02A90/10—Information and communication technologies [ICT] supporting adaptation to climate change, e.g. for weather forecasting or climate simulation
Landscapes
- Medical Treatment And Welfare Office Work (AREA)
Abstract
The invention discloses a community cloud platform health management method, which relates to the technical field of information management and comprises the following steps: step S1: enabling face recognition for mobile personnel waiting to enter a community to acquire identity information; step S2: when no personal health detection record exists in the day, carrying out advanced screening according to the historical personal health detection record so as to further judge; step S3: setting a buffered date and time interval along a timeline from past to present date and time spans; step S4: comparing the distribution of the negative and positive detection results on the second subinterval and the first subinterval on a time axis; step S5: and finally, the judged healthy person, suspected patient and ill person respectively enter a healthy channel, a review channel and a channel to be treated. According to the invention, manual screening of queuing personnel is not needed in communities, and the working efficiency of screening the personal health detection records is obviously improved.
Description
Technical Field
The invention relates to the technical field of information management, in particular to a community cloud platform health management method.
Background
At present, under the transmission background of various influenza viruses, the transmission of influenza diseases and various viruses is very rapid, the transmission range is large, and the social hazard is high. In the prior art, each person can inquire about the current health code state, the risk of the activity track and the result of virus health detection through a medical software platform bound with the identity information of the person. The traditional health management mode of community platform to community mobility personnel mainly carries out personnel in the district based on community or district property and judges whether to let the personnel enter through current health information of medical software platform such as health code and the personal condition of community mobility personnel, and the working process mainly relies on community manager to carry out with the mode of manual screening. Since in order to maintain the health state of the health code during epidemic prevention, community flowers need to maintain a high frequency of health detection and display the detection result to community managers through the health code or other medical software platforms for inspection to allow passage. When the community flow is large, each person needs to display the health detection information to the community manager for inspection in a manual mode so as to meet the passing requirement, and therefore the workload of the community manager is large. Under the background of disease management and control with lower requirements, when communities keep open management to allow personnel to flow and the hands of management personnel are insufficient, the manual inspection strength is reduced, but the epidemic prevention management cannot be relaxed objectively, so that a management method capable of facilitating the health screening of people flow is needed in communities with more flowing personnel on the premise of reducing the manual management workload.
Disclosure of Invention
The invention aims to provide a community cloud platform health management method, which aims to solve the problems of low working efficiency and insufficient screening capability of screening health detection records of a mobilization person in a community with high traffic when epidemic prevention management and control manpower is insufficient.
The invention is realized by the following technical scheme:
a community cloud platform health management method, the method comprising:
step S1: enabling facial recognition to mobile personnel waiting for entering a community to acquire identity information one by one, acquiring historical personal health detection records of the mobile personnel of the community through a health code platform for preset communication, and storing the identity information of each person and the corresponding personal health detection records in a cloud in a form of a plurality of historical times;
step S2: performing preliminary screening on mobile personnel, when the personal health detection record is positive or negative, respectively entering a channel to be treated or a health channel of a community, and when the personal health detection record is not available on the day, performing advanced screening according to the historical personal health detection record to further judge;
step S3: setting a buffer date interval along a time line from the past to the current date time span, wherein the buffer date interval is equally divided into a second subinterval and a first subinterval from the far to the near according to the past time, and the time spans of the second subinterval and the first subinterval are both set to be consistent with the average recovery period time of the target prevention and control influenza virus;
step S4: respectively adding a first identity mark and a second identity mark into historical personal health detection record data of a plurality of times of the mobile personnel according to negative and positive detection results, and comparing the distribution conditions of the two types of identity marks on a second subinterval and a first subinterval on the same time axis, wherein the judgment results of the distribution conditions are divided into three types, the first type is judged to be healthy, the second type is judged to be suspected patient, and the third type is judged to be ill;
step S5: the mobile personnel are screened and checked to judge that healthy persons enter the community through the healthy channel, and the mobile personnel enter the review channel to wait for the manager to detect the artificial symptoms if the mobile personnel are judged to be suspected patients, and the mobile personnel enter the channel to be treated to wait for medical care inquiry if the mobile personnel are judged to be suspected patients.
Further, in step S4,
the first category of conditions includes: within the first subinterval there is and only a first identity mark;
the second category of cases includes: any identity mark is absent in the buffer date interval, the last detection record is a second identity mark, and the second identity mark and the first identity mark are sequentially arranged in the second subinterval and the first subinterval according to time;
the third category of cases includes: the personal health check record of the latest date in the first subinterval is the second identity mark.
Further, adding a time stamp to the data of the last personal health detection record of the current date of the flow personnel; setting the time interval between the time stamp and the current date as a detection time difference, and setting a time threshold for the time span of the detection time difference; the time span size of the time threshold is: moving the past time to three quarters of the first interval time span by taking the current date as a reference, and reserving an integer according to the number of days; when the time stamp is the first identity mark, if the time stamp is positioned within a time threshold value on a time axis, the mobile personnel is judged to be a healthy person; the time stamp is located outside the time threshold and within the first subinterval on the time axis, and the flowman is changed and judged as a suspected patient. Further, when the time stamp is the second identity mark, if the time stamp is within the time threshold on the time axis, the mobile personnel is judged to be a patient; the time stamp is located outside the time threshold and within the first subinterval on the time axis, and the flowman is changed and judged as a suspected patient.
Further, when face recognition is performed, body temperature measurement is performed on the mobile personnel, the measured body temperature value is compared with a preset body temperature threshold value, and after the measured body temperature value exceeds the body temperature threshold value, step-by-step screening is skipped to judge the mobile personnel as a patient. The body temperature threshold judging part is integrated in the facial recognition function, and the threshold setting standard is relatively high, for example, a common infrared distance thermometer can have a body temperature lower error of 1-1.5 degrees, so that the threshold can be a high fever standard in specific application, such as about 39 degrees.
Further, when face recognition is performed, fatigue estimation is performed on the current state of the mobile personnel through the face features and the opening and closing states of eyes, the calculated estimated fatigue degree is converted into a quantized fatigue measurement value, the fatigue measurement value is compared with a preset fatigue threshold value in a numerical mode, and the mobile personnel subjected to advanced screening as a first type of judging result is changed into a second type of result. The facial features comprise facial actions such as yawning, eye rubbing, continuously lower opening and closing degree of eyes and the like in various fatigue, the facial features are set to a preset value or a preset state for capturing the facial features and then are compared, after the comparison standard is reached, the famous flow personnel are judged to be suspected patients, and the patients enter a review channel to wait for manual review processing.
Further, identity information of community personnel is pre-stored, and the community mobile personnel are classified into two types of communities and non-community according to pre-stored identity registration information, wherein the community mobile personnel comprise community health personnel and community health abnormal personnel, and the non-community mobile personnel comprise non-community health personnel and non-community health abnormal personnel; the community health personnel and the community health abnormal personnel are respectively marked as first-class households and second-class households according to life energy self-care and non-self-care.
Further, each time a community flow person enters a channel to be treated and a review channel, the community manager receives information of manual processing notification.
Further, the total number of manual processing notices to be treated and reviewed is recorded and counted respectively, after the number of the notices of the manual processing on a single day reaches a preset number warning value, the statistics of the manual processing notices are transmitted to street management personnel managing the community, and then the number of the manual processing notices on the same day is continuously recorded.
Compared with the prior art, the invention has the following advantages and beneficial effects:
1. according to the invention, the time interval is set along the time axis, the collected health detection records are compared on the time axis, community mobile personnel lacking personal health detection records on the same day of entering the community are classified and split, manual screening of each row of queuing personnel is not needed in communities with higher people flow, and the working efficiency of screening the personal health detection records is obviously improved;
2. the time spans of the first subinterval and the second subinterval for auxiliary comparison are set to be consistent with the average rehabilitation period time of the target prevention and control influenza virus, so that the classification management of community potentially diseased mobile personnel is effectively facilitated;
3. according to the invention, part of people are further classified in a broad way according to suspected ill people through the time stamp, and excessive control of mobile people is avoided on the premise of allowing community personnel to flow, so that prevention and control management is further improved in a humanized way.
Drawings
The accompanying drawings, which are included to provide a further understanding of embodiments of the invention and are incorporated in and constitute a part of this application, illustrate embodiments of the invention. In the drawings:
FIG. 1 is a flow chart of the method of the present invention;
FIG. 2 is a schematic diagram of a first marked timestamp in a first type of distribution of the present invention being within a time threshold on a time axis, wherein the arrow indicates a time flow direction;
FIG. 3 is a schematic diagram showing the last detection in the second distribution of the present invention recorded as the second ID and within the second subinterval, wherein the arrows indicate the time flow direction;
FIG. 4 is a schematic diagram of a second identity tag and a first identity tag in sequence in a first subinterval in a second type of distribution according to the present invention, wherein an arrow indicates a time flow direction;
FIG. 5 is a schematic diagram of a first marked timestamp in a second type of distribution of the present invention being located outside a time threshold and within a first subinterval on a time axis, wherein arrows indicate time flow directions;
FIG. 6 is a schematic diagram of a second identity-tagged timestamp in a second type of distribution of the present invention being outside a time threshold and inside a first subinterval on a time axis, wherein arrows represent time flow;
FIG. 7 is a schematic diagram of a second-identification-marked timestamp in a third type of distribution of the present invention being within a time threshold on a time axis, wherein the arrow indicates the time flow direction.
The reference numerals are represented as follows: a-first endpoint, C-second endpoint, B-middle endpoint, D-threshold endpoint, 101-first identity tag, 102-second identity tag, 103-timestamp.
Detailed Description
For the purpose of making apparent the objects, technical solutions and advantages of the present invention, the present invention will be further described in detail with reference to the following examples and the accompanying drawings, wherein the exemplary embodiments of the present invention and the descriptions thereof are for illustrating the present invention only and are not to be construed as limiting the present invention. It should be noted that the present invention is already in a practical development and use stage.
As shown in fig. 1, the method in this embodiment includes the steps of:
step S1: enabling facial recognition to mobile personnel waiting for entering a community to acquire identity information one by one, acquiring historical personal health detection records of the mobile personnel of the community through a health code platform for preset communication, and storing the identity information of each person and the corresponding personal health detection records in a cloud in a form of a plurality of historical times;
step S2: performing preliminary screening on mobile personnel, when the personal health detection record is positive or negative, respectively entering a channel to be treated or a health channel of a community, and when the personal health detection record is not available on the day, performing advanced screening according to the historical personal health detection record to further judge;
step S3: setting a buffer date interval along a time line from the past to the current date time span, wherein the buffer date interval is equally divided into a second subinterval and a first subinterval from the far to the near according to the past time, and the time spans of the second subinterval and the first subinterval are both set to be consistent with the average recovery period time of the target prevention and control influenza virus;
step S4: respectively adding a first identity mark and a second identity mark into historical personal health detection record data of a plurality of times of the mobile personnel according to negative and positive detection results, and comparing the distribution conditions of the two types of identity marks on a second subinterval and a first subinterval on the same time axis, wherein the judgment results of the distribution conditions are divided into three types, the first type is judged to be healthy, the second type is judged to be suspected patient, and the third type is judged to be ill;
step S5: the mobile personnel are screened and checked to judge that healthy persons enter the community through the healthy channel, and the mobile personnel enter the review channel to wait for the manager to detect the artificial symptoms if the mobile personnel are judged to be suspected patients, and the mobile personnel enter the channel to be treated to wait for medical care inquiry if the mobile personnel are judged to be suspected patients.
In the step S1, in the preliminary information acquisition stage, a condition of historical personal health detection of a mobile person in statistical record on an existing health code platform is obtained through an identification comparison mode, and after information is obtained through facial recognition, a historical detection record of the mobile person in a period of time in the recent period is temporarily stored for management.
As one possible embodiment, the temperature of the mobile person is measured during face recognition, the measured temperature is compared with a preset temperature threshold value, and the mobile person is determined as a patient by skipping the advanced screening after exceeding the temperature threshold value. The flow staff is then guided by the guide to the channel to be treated. The body temperature threshold judging part is integrated in the facial recognition function, and the threshold setting standard is relatively high, for example, a common infrared distance thermometer can have a body temperature lower error of 1-1.5 degrees, so that the threshold can be a high fever standard in specific application, such as about 39 degrees.
The form of the preliminary screening is specified in step S2. When no personal health detection record exists in the day, a historical personal health detection record is obtained according to the health data obtained from the identity information of the famous flowman, then a step screening is carried out, and finally whether the famous flowman is a healthy person, a sick person or a suspected sick person is judged through a trend estimation mode.
In the step S3 and the step S4, whether the mobile personnel with no health detection record carry the potential of diseases or not is screened for the second time, and the criterion of screening is that whether the length of the days with no health detection record is larger than the average recovery period of the target epidemic prevention disease or not is compared under the condition that the last detection result is negative and positive respectively; when the length of the days without health detection records is smaller than the average rehabilitation period of the target epidemic prevention disease, the number of days without health detection records is respectively compared with the number of days approaching the period or the number of days far from the period according to the last negative or positive result.
And the step S5 is to set different guide channels for the three discrimination results respectively. Healthiers enter the community through a health channel; the suspected patient enters a review channel to wait for a manager to detect the artificial symptoms, and the judgment of the healthy person or the ill person is made after the artificial rapid self-test or the detection of the common symptoms, and the suspected patient enters a community and goes to a channel to be treated respectively; the patient enters the channel to be treated to wait for medical inquiry, if the medical staff confirms that no disease exists, the medical staff enters the community, and if the medical staff confirms that the disease exists, the medical staff judges whether the patient needs to be sent to a hospital or returns to the community for home autonomy according to the disease degree.
Further, as one possible embodiment, as shown in fig. 2-7, in step S4,
the first category of conditions includes: within the first subinterval there is and only a first identity mark;
the second category of cases includes: any identity mark is absent in the buffer date interval, the last detection record is a second identity mark, and the second identity mark and the first identity mark are sequentially arranged in the second subinterval and the first subinterval according to time;
the third category of cases includes: the personal health check record of the latest date in the first subinterval is the second identity mark.
It should be noted that, the first endpoint a is a date and time point of the day, the date and time interval between the first endpoint a and the second endpoint C is a buffer date interval, the date and time interval between the first endpoint a and the middle endpoint B is a first subinterval, and the date and time interval between the middle endpoint B and the second endpoint C is a second subinterval. The first identity mark 101 represents a negative detection result record, the second identity mark 102 represents a positive detection result record, and the first identity mark 101 and the second identity mark 102 are distributed on the time axis according to the detection date thereof.
Specifically, as shown in fig. 2, the first case is that in the first subinterval with the latest time, only the first identity mark 101, that is, in the latest rehabilitation cycle time, there is no positive record but only a negative record, so even if the detection record is missing on the same day, it can be discriminated as a healthy person due to the high probability. The second case includes, as shown in fig. 3 and fig. 4, that the last detection in the buffer date interval records as a positive result of the second identification mark 102, and that the second identification mark 102 is located in the second subinterval, where the time interval between the second identification mark 102 and the first end point a of the current date has exceeded the average recovery period time, so that the corresponding streaming person is a patient even before one recovery period, but can be regarded as a patient with much reduced symptoms by that time, and is classified as a suspected patient; in fig. 3, the positive and negative test results are recorded in the first subinterval, and the last health test record is negative, but the positive test result exists in the last recovery period, so that the potential disease is determined to exist, namely, the potential disease is classified to suspected patients.
To further increase the accuracy of the discrimination, as a possible embodiment, as shown in fig. 5 to 7, a time stamp 103 is added to the data of the one personal health check record closest to the current date of the flow person; the time interval size between the time stamp 103 and the current date is set as the detection time difference, and a time threshold is set for the time span size of the detection time difference; the time span size of the time threshold is: moving the past time to three quarters of the first interval time span by taking the current date as a reference, and reserving an integer according to the number of days; when the time stamp 103 is the first identity mark 101, if the time stamp 103 is within the time threshold on the time axis, the mobile personnel is judged to be a healthy person; the time stamp 103 is located outside the time threshold and within the first subinterval on the time axis, and the flowman is changed and judged as a suspected patient.
As shown in fig. 5, the time stamp 103 is disposed on the first identity mark 101 or the second identity mark 102, and the date time period between the first endpoint a and the threshold endpoint D is the threshold time span size. In fig. 2, the first type of case is refined, and the first identity mark 101 is divided into two cases that are located within the time threshold and outside the time threshold and within the first subinterval, wherein the first identity mark 101 is located in a date range closer to the current date, so that the mobile personnel are classified as healthy people; conversely, the latter is classified as a suspected patient because the date is farther away, and the probability of infection being present in the middle is relatively higher.
Further, when the time stamp 103 is the second identity mark 102, if the time stamp 103 is within the time threshold on the time axis, the mobile person is determined to be the patient; the time stamp 103 is located outside the time threshold and within the first subinterval on the time axis, and the flowman is changed and judged as a suspected patient. As shown in fig. 7, when the second identity mark 102 added with the timestamp 103 is located outside the time threshold and within the first subinterval, that is, the date-time area between the middle endpoint B and the threshold endpoint D, the positive detection record is still within the first subinterval, and the last recovery period time, but since the date-time point of the positive detection record is relatively far, there may be a situation that the patient has been substantially recovered, so it is classified as a suspected patient; conversely, when the timestamp 103 is within the span of the time threshold, then it is classified as a ill since the positive detection record is relatively close to the current date.
Further, as a possible implementation manner, when face recognition is performed, fatigue is estimated on the current state of the mobile personnel through the face features and the opening and closing states of eyes, the calculated estimated fatigue degree is converted into a quantized fatigue measurement value, the fatigue measurement value is compared with a preset fatigue threshold value in a numerical mode, and the mobile personnel with the advanced screening as the first type of judging result is changed to be judged as the second type of result. The facial features comprise facial actions such as yawning, eye rubbing, continuously lower opening and closing degree of eyes and the like in various fatigue, the facial features are set to a preset value or a preset state for capturing the facial features and then are compared, after the comparison standard is reached, the famous flow personnel are judged to be suspected patients, and the patients enter a review channel to wait for manual review processing.
Further, as a feasible personnel management mode, identity information of community personnel is pre-stored, and community mobile personnel are classified into two types of communities and non-community according to pre-stored identity registration information, wherein the community mobile personnel comprise community health personnel and community health abnormal personnel, and the non-community mobile personnel comprise non-community health personnel and non-community health abnormal personnel; the community health personnel and the community health abnormal personnel are respectively marked as first-class households and second-class households according to life energy self-care and non-self-care. Therefore, further classification management of households and community foreign personnel is realized.
Further, as a possible implementation, each time a community flow person enters a channel to be treated and a review channel, the community manager receives information of a manual processing notification. The community manager can be a management responsible person for health detection or a medical care person for community health management, so that notification can be sent out timely, and waiting time of community flow personnel is reduced. More, the total number of manual processing notices to be treated and reviewed is recorded and counted respectively, after the number of times of manual processing notices in a single day reaches a preset number of times warning value, the statistics of the manual processing notices are transmitted to street management personnel managing the community, and then the number of times of manual processing notices in the same day is continuously recorded. Thus, a preliminary statistics can be completed for the number of cases in each community.
The foregoing description of the embodiments has been provided for the purpose of illustrating the general principles of the invention, and is not meant to limit the scope of the invention, but to limit the invention to the particular embodiments, and any modifications, equivalents, improvements, etc. that fall within the spirit and principles of the invention are intended to be included within the scope of the invention.
Claims (5)
1. A community cloud platform health management method is characterized by comprising the following steps:
step S1: enabling facial recognition to mobile personnel waiting for entering a community to acquire identity information one by one, acquiring historical personal health detection records of the mobile personnel of the community through a health code platform for preset communication, and storing the identity information of each person and the corresponding personal health detection records in a cloud in a form of a plurality of historical times;
step S2: performing preliminary screening on mobile personnel, when the personal health detection record is positive or negative, respectively entering a channel to be treated or a health channel of a community, and when the personal health detection record is not available on the day, performing advanced screening according to the historical personal health detection record to further judge;
step S3: setting a buffer date interval along a time line from the past to the current date time span, wherein the buffer date interval is equally divided into a second subinterval and a first subinterval from the far to the near according to the past time, and the time spans of the second subinterval and the first subinterval are both set to be consistent with the average recovery period time of the target prevention and control influenza virus;
step S4: respectively adding a first identity mark and a second identity mark into historical personal health detection record data of a plurality of times of the mobile personnel according to negative and positive detection results, and comparing the distribution conditions of the two types of identity marks on a second subinterval and a first subinterval on the same time axis, wherein the judgment results of the distribution conditions are divided into three types, the first type is judged to be healthy, the second type is judged to be suspected patient, and the third type is judged to be ill;
step S5: the mobile personnel are screened and checked to judge that healthy persons enter a community through a healthy channel, and if the mobile personnel are judged to be suspected sick persons enter a review channel to wait for a manager to detect the artificial symptoms, and the mobile personnel are judged to enter a channel to be treated to wait for medical care inquiry;
in the step S4 of the process of the present invention,
the first category of conditions includes: within the first subinterval there is and only a first identity mark;
the second category of cases includes: any identity mark is absent in the buffer date interval, the last detection record is a second identity mark, and the second identity mark and the first identity mark are sequentially arranged in the second subinterval and the first subinterval according to time;
the third category of cases includes: the personal health detection record of the latest date in the first subinterval is a second identity mark;
adding a time stamp to the data of the last personal health detection record of the current date of the streaming personnel; setting the time interval between the time stamp and the current date as a detection time difference, and setting a time threshold for the time span of the detection time difference;
the time span size of the time threshold is: moving the past time to three quarters of the first interval time span by taking the current date as a reference, and reserving an integer according to the number of days;
when the time stamp is the first identity mark, if the time stamp is positioned within a time threshold value on a time axis, the mobile personnel is judged to be a healthy person; the time stamp is positioned outside the time threshold and within the first subinterval on the time axis, and the mobile personnel is changed and judged to be a suspected patient; when the time stamp is the second identity mark, if the time stamp is positioned within a time threshold value on a time axis, the mobile personnel is judged to be a patient; the time stamp is located outside the time threshold and within the first subinterval on the time axis, and the flowman is changed and judged as a suspected patient.
2. The community cloud platform health management method of claim 1, wherein the temperature of the mobile personnel is measured during facial recognition, the measured temperature is compared with a preset temperature threshold value, and the mobile personnel is determined to be a patient by skipping the advanced screening after the measured temperature value exceeds the temperature threshold value.
3. The community cloud platform health management method according to claim 1, wherein during face recognition, fatigue is estimated on the current state of the mobile personnel through facial features and eye opening and closing conditions, the calculated estimated fatigue degree is converted into a quantized fatigue measurement value, the fatigue measurement value is compared with a preset fatigue threshold value in a numerical mode, and the mobile personnel subjected to advanced screening as a first type of discrimination result is changed to be discriminated as a second type of discrimination result.
4. The community cloud platform health management method of claim 1, wherein identity information of community personnel is pre-stored, and community mobile personnel are classified into two types of communities and non-community according to pre-stored identity registration information, wherein the community mobile personnel comprise community health personnel and community health abnormal personnel, and the non-community mobile personnel comprise non-community health personnel and non-community health abnormal personnel; the community health personnel and the community health abnormal personnel are respectively marked as first-class households and second-class households according to life energy self-care and non-self-care.
5. The community cloud platform health management method according to claim 1, wherein each time a community mobile person enters a channel to be treated and a review channel, the community management person receives information of manual processing notification; the total number of manual processing notices to be treated and rechecked is recorded and counted respectively, after the notice number of manual processing on a single day reaches a preset number warning value, the statistics of the manual processing notices are transmitted to street management personnel managing the community, and then the notice number of manual processing on the same day is recorded continuously.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202310364567.6A CN116090691B (en) | 2023-04-07 | 2023-04-07 | Community cloud platform health management method |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202310364567.6A CN116090691B (en) | 2023-04-07 | 2023-04-07 | Community cloud platform health management method |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN116090691A CN116090691A (en) | 2023-05-09 |
| CN116090691B true CN116090691B (en) | 2023-07-14 |
Family
ID=86187257
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202310364567.6A Active CN116090691B (en) | 2023-04-07 | 2023-04-07 | Community cloud platform health management method |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN116090691B (en) |
Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2021238693A1 (en) * | 2020-05-26 | 2021-12-02 | 京东数科海益信息科技有限公司 | Blockchain-based public health and safety management and control method and apparatus |
| WO2023273834A1 (en) * | 2021-06-29 | 2023-01-05 | 华为技术有限公司 | Detection method and related device |
Family Cites Families (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112530599A (en) * | 2020-08-03 | 2021-03-19 | 湖南安舒智能科技有限公司 | Epidemic prevention control system and method based on cloud platform |
| CN112233300A (en) * | 2020-09-26 | 2021-01-15 | 杭州雅观科技有限公司 | Community passing epidemic prevention monitoring joint defense system and method based on artificial intelligence |
| CN112489275A (en) * | 2020-11-09 | 2021-03-12 | 北京澎思科技有限公司 | Access control method and device, electronic equipment and storage medium |
| CN113055491A (en) * | 2021-03-24 | 2021-06-29 | 安徽超视野智能科技有限公司 | Face and health code verification and identification system and method for smart community |
| CN217086146U (en) * | 2021-11-18 | 2022-07-29 | 南京纽喔网络科技有限公司 | Epidemic prevention nucleic acid detects with current data identification equipment |
| CN114203310A (en) * | 2021-11-18 | 2022-03-18 | 江苏润和软件股份有限公司 | Epidemic group correlation flow regulation method |
| CN114783553A (en) * | 2022-03-07 | 2022-07-22 | 中国电信集团工会上海市委员会 | Rework epidemic prevention passing system and method by combining health code data with temperature measuring equipment |
| CN115565284A (en) * | 2022-10-26 | 2023-01-03 | 北京国基科技股份有限公司 | Identity verification method and device based on security scene |
-
2023
- 2023-04-07 CN CN202310364567.6A patent/CN116090691B/en active Active
Patent Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2021238693A1 (en) * | 2020-05-26 | 2021-12-02 | 京东数科海益信息科技有限公司 | Blockchain-based public health and safety management and control method and apparatus |
| WO2023273834A1 (en) * | 2021-06-29 | 2023-01-05 | 华为技术有限公司 | Detection method and related device |
Also Published As
| Publication number | Publication date |
|---|---|
| CN116090691A (en) | 2023-05-09 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN107145704B (en) | Community-oriented health medical monitoring and evaluating system and method | |
| WO2021208091A1 (en) | Neural network-based virus screening and epidemic prevention system and method | |
| CN111081379B (en) | Disease probability decision method and system thereof | |
| CN109247936A (en) | A kind of abnormal brain electricity behavior monitoring system and method for full night sleep monitor | |
| CN111916196A (en) | Wound and skin pressure injury auxiliary diagnosis system based on artificial intelligence technology | |
| CN113889252B (en) | Remote internet big data intelligent medical system based on vital sign big data clustering core algorithm and block chain | |
| CN112863685A (en) | Infectious disease coping method based on big data artificial intelligence and robot | |
| CN113256859A (en) | Epidemic prevention channel system | |
| CN116978510A (en) | Nursing grading evaluation system for home cerebral apoplexy patient | |
| CN116090691B (en) | Community cloud platform health management method | |
| CN114155943A (en) | Medical emergency information management system | |
| Kauppinen et al. | Health and hazard surveillance—needs and perspectives | |
| CN116681281A (en) | Sudden public health event acquisition system and method based on context awareness | |
| CN115083584A (en) | Single-disease-category full-course management system based on big data | |
| CN113470797A (en) | Intelligent hospital management system | |
| CN114822793A (en) | Isolation ward human resource management system | |
| CN114121239A (en) | Operating room abnormal condition processing method and device and electronic equipment | |
| CN110752017A (en) | Community doctor scheduling method and system based on deep learning | |
| CN109376635B (en) | A kind of nursing quality checking system and security incident report method | |
| CN110399401A (en) | Data analysis system and method based on government data | |
| CN113724890A (en) | Health information verification method, device, system and medium based on block chain | |
| CN109903850A (en) | Method of the use information technology to perioperative operation safety management | |
| CN112233807A (en) | Intelligent health quarantine big data analysis system for exit-entry epidemic situation | |
| Eagar | Defining an episode of care: a study of five case types | |
| CN117542498B (en) | Gynecological nursing management system and method based on big data analysis |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |