Monitoring system and method for nursing environment
Through the integrated monitoring system of environment and behavioral parameters, hierarchical judgment and dynamic adjustment algorithms are used to solve the problem of insufficient data linkage and early warning mechanisms in childcare institutions, accurate risk assessment and scientific early warning of childcare environment are realized, and safety management efficiency is improved.
Patent Information
- Application Number
- CN202510511731.0
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-04-23
- Publication Date
- 2025-07-29
- Estimated Expiration
- Not applicable · inactive patent
AI Technical Summary
Traditional childcare institutions rely on manual and monitoring equipment to ensure safety, but lack data linkage, dynamic assessment and hierarchical early warning mechanisms, resulting in inaccurate environmental and behavioral risk assessment, and ineffective identification of multiple abnormalities and prioritization of risks.
A childcare environment monitoring system is designed, integrating the acquisition module, environmental judgment module, behavior judgment module and early warning module. By monitoring environmental parameters (humidity, temperature, smoke concentration) and behavior parameters (climbing height, concentration density) in real time, hierarchical judgment and dynamic adjustment algorithm are used to generate an abnormal comprehensive final value for level warning.
It realizes accurate risk assessment of the childcare environment, reduces the misjudgment rate, improves safety management capabilities and risk warning efficiency, can dynamically capture children's dangerous behaviors and group gathering risks, provide a scientific hierarchical early warning mechanism, and optimizes the design and management of childcare environment.
Smart Images
Figure CN120387643A_ABST
Abstract
Description
Technical Field
[0001] The present invention relates to the technical field of nursery monitoring, and more particularly, to a nursery environment monitoring system and method. Background Art
[0002] With the increasing attention of society to early childhood education, the demand for nursery institutions is growing day by day, and the safety and health of the nursery environment have also become the focus of concern for parents and regulatory authorities.
[0003] Traditional nursery institutions mainly rely on manual care and environmental monitoring equipment (such as thermohygrometers, smoke alarms, etc.) to ensure the safety of children. Environmental parameters and behavior data are separated, and no linkage analysis mechanism is formed; behavior risk assessment relies on fixed rules and lacks a dynamic adjustment logic; the warning mechanism is single and cannot distinguish the priority of risks.
[0004] Therefore, it is necessary to provide a nursery environment monitoring system and method to solve the problem that traditional nursery institutions rely on manual labor and monitoring equipment to ensure safety, but lack data linkage, dynamic assessment, and hierarchical warning mechanisms. Summary of the Invention
[0005] In view of this, the present invention proposes a nursery environment monitoring system and method, aiming to solve the problem that traditional nursery institutions rely on manual labor and monitoring equipment to ensure safety, but lack data linkage, dynamic assessment, and hierarchical warning mechanisms.
[0006] On the one hand, the present invention proposes a nursery environment monitoring system, including:
[0007] A collection module configured to collect environmental parameters and behavior parameters; wherein, the environmental parameters include environmental humidity, environmental temperature, and smoke concentration; the behavior parameters include climbing height and aggregation density;
[0008] An environment judgment module configured to respectively judge whether the environment is abnormal according to the environmental humidity, environmental temperature, and smoke concentration. If it is judged that the environment is abnormal, an environmental abnormal value is obtained according to the abnormal judgment result;
[0009] A behavior judgment module configured to judge whether the behavior is abnormal according to the climbing height. If it is judged to be abnormal, a first behavior risk value is obtained according to the climbing height, and it is judged whether to adjust the first behavior risk value according to the climbing height change value. If it is judged that adjustment is required, the first behavior risk value is adjusted according to the climbing height change value to obtain a first behavior risk final value;
[0010] The behavior judgment module is further configured to judge behavioral anomalies according to the aggregation density. If it is judged as abnormal, a second behavioral risk value is obtained according to the aggregation density, and it is judged whether to adjust the second behavioral risk value according to the aggregation density change value. If it is judged that adjustment is needed, the second behavioral risk value is adjusted according to the aggregation density change value to obtain the final second behavioral risk value;
[0011] An early warning module, configured to calculate a comprehensive anomaly final value according to the environmental anomaly value, the final first behavioral risk value, and the final second behavioral risk value, and perform level early warning according to the comprehensive anomaly final value.
[0012] Further, when the environment judgment module is configured to judge whether the environment is abnormal according to the environmental humidity, the environmental temperature, and the smoke concentration respectively, it includes:
[0013] Respectively set the environmental humidity range value, the environmental temperature range value, and the minimum smoke concentration value;
[0014] If there is a situation where the environmental humidity is not within the environmental humidity range value, the environmental temperature is not within the environmental temperature range value, or the smoke concentration is greater than the minimum smoke concentration value, it is judged that the environment is abnormal;
[0015] Otherwise, it is judged that the environment is not abnormal.
[0016] Further, when the environment judgment module is configured to obtain an environmental anomaly value according to the anomaly judgment result, it includes:
[0017] If it is judged that the environment is abnormal, and there is one of the three conditions that the environmental humidity is not within the environmental humidity range value, the environmental temperature is not within the environmental temperature range value, and the smoke concentration is greater than the minimum smoke concentration value, the environmental anomaly value is the first environmental anomaly value; if there are two conditions, the environmental anomaly value is the second environmental anomaly value, and if there are three conditions, the environmental anomaly value is the third environmental anomaly value;
[0018] If it is judged that the environment is not abnormal, the environmental anomaly value is zero.
[0019] Further, when the behavior judgment module is configured to judge behavioral anomalies according to the climbing height, if it is judged as abnormal and a first behavioral risk value is obtained according to the climbing height, it includes:
[0020] Set the minimum climbing height. If the climbing height is greater than the minimum climbing height, it is judged that the behavior is abnormal; otherwise, it is judged that the behavior is not abnormal;
[0021] Set a first height and a second height, where the first height is less than the second height, and the first height is greater than the minimum climbing height;
[0022] If the climbing height is less than or equal to the first height and greater than the minimum climbing height, the danger value of the first behavior is the first danger value;
[0023] If the climbing height is greater than the first height and less than or equal to the second height, the danger value of the first behavior is the second danger value;
[0024] If the climbing height is greater than the second height, the danger value of the first behavior is the third danger value.
[0025] The first danger value is less than the second danger value, and the second danger value is less than the third danger value.
[0026] Further, when the behavior judgment module is configured to judge whether to adjust the first behavior danger value according to the climbing height change value, it includes:
[0027] Collect the climbing height change value within a preset time. If the climbing height change value is greater than zero, judge to adjust the first behavior danger value;
[0028] Otherwise, judge not to adjust the first behavior danger value.
[0029] Further, when the behavior judgment module is configured to adjust the first behavior danger value according to the climbing height change value to obtain the final danger value of the first behavior, it includes:
[0030] Set the first height change value and the second height change value, and the first height change value is less than the second height change value;
[0031] If the climbing height change value is less than or equal to the first height change value, adjust the first behavior danger value by the first adjustment coefficient;
[0032] If the climbing height change value is greater than the first height change value and less than or equal to the second height change value, adjust the first behavior danger value by the second adjustment coefficient;
[0033] If the climbing height change value is greater than the second height change value, adjust the first behavior danger value by the third adjustment coefficient;
[0034] The value range of the adjustment coefficient is: 1 < the first adjustment coefficient < the second adjustment coefficient < the third adjustment coefficient < 1.5, and the adjusted first behavior danger value is the product of the first behavior danger value before adjustment and the adjustment coefficient.
[0035] Further, the behavior judgment module is further configured to judge behavior anomalies based on the aggregation density. When judging as an anomaly and obtaining a second behavior risk value according to the aggregation density, it includes:
[0036] Set a minimum aggregation density. If the aggregation density is greater than the minimum aggregation density, judge the behavior as abnormal; otherwise, judge that the behavior is not abnormal;
[0037] Set a first density and a second density, where the first density is less than the second density, and the first density is greater than the minimum aggregation density;
[0038] If the aggregation density is less than or equal to the first density and greater than the minimum aggregation density, the second behavior risk value is the first aggregation risk value;
[0039] If the aggregation density is greater than the first density and less than or equal to the second density, the second behavior risk value is the second aggregation risk value;
[0040] If the aggregation density is greater than the second density, the second behavior risk value is the third aggregation risk value;
[0041] The first aggregation risk value is less than the second aggregation risk value, and the second aggregation risk value is less than the third aggregation risk value.
[0042] Further, the behavior judgment module is further configured to judge whether to adjust the second behavior risk value according to the aggregation density change value. When judging that adjustment is needed and adjusting the second behavior risk value according to the aggregation density change value to obtain the final second behavior risk value, it includes:
[0043] Collect the aggregation density change value within a preset time. If the aggregation density change value is greater than zero, judge to adjust the second behavior risk value; otherwise, judge not to adjust the second behavior risk value;
[0044] Set a first density change value and a second density change value, where the first density change value is less than the second density change value;
[0045] If the aggregation density change value is less than or equal to the first density change value, adjust the second behavior risk value through a first adjustment coefficient;
[0046] If the aggregation density change value is greater than the first density change value and less than or equal to the second density change value, adjust the second behavior risk value through a second adjustment coefficient;
[0047] If the aggregation density change value is greater than the second density change value, adjust the second behavior risk value through a third adjustment coefficient;
[0048] The value range of the adjustment coefficient is: 1 < the first adjustment coefficient < the second adjustment coefficient < the third adjustment coefficient < 1.5. The second behavior risk value after adjustment is the product of the second behavior risk value before adjustment and the adjustment coefficient.
[0049] Further, when the early warning module is configured to calculate the comprehensive anomaly final value based on the environmental anomaly value, the first behavior risk final value, and the second behavior risk final value, and perform level early warning based on the comprehensive anomaly final value, it includes:
[0050] Performing a weighted sum of the environmental anomaly value, the first behavior risk final value, and the second behavior risk final value to obtain the comprehensive anomaly final value;
[0051] Setting a first final value and a second final value, where the first final value is less than the second final value;
[0052] If the comprehensive anomaly final value is less than the first final value, a first-level early warning is performed;
[0053] If the comprehensive anomaly final value is greater than or equal to the first final value and less than or equal to the second final value, a second-level early warning is performed;
[0054] If the comprehensive anomaly final value is greater than the second final value, a third-level early warning is performed;
[0055] The early warning levels are, from low to high, the first early warning, the second-level early warning, and the third-level early warning.
[0056] Compared with the prior art, the beneficial effects of the present invention are as follows: The present invention improves the safety management ability and risk early warning efficiency of the nursery institution. First, the system's real-time monitoring of environmental humidity, temperature, and smoke concentration can accurately identify environmental anomalies (such as fire hazards, inappropriate temperature and humidity), ensuring that the environment where children are located meets health standards and avoiding respiratory diseases or safety accidents caused by environmental factors. Second, the intelligent analysis of behavior parameters (climbing height, aggregation density) can dynamically capture children's dangerous behaviors (such as the risk of falling from climbing high) or group aggregation risks (such as pushing and trampling or the spread of infectious diseases), and adjust the risk level through the change value, making the early warning more in line with the actual risk evolution trend. For example, when children continuously climb and the height increases, the system will dynamically increase the risk value, rather than relying only on single data, greatly reducing the misjudgment rate. Finally, the integrated analysis of environmental and behavior data (comprehensive anomaly final value) realizes cross-dimensional risk assessment, making the early warning level more scientific and helping childcare staff to prioritize high-risk events. Overall, the system not only reduces the omissions of manual monitoring but also can optimize the design and management strategies of the nursery environment through data backtracking, taking into account both safety and scientificity, and providing more intelligent protection for children.
[0057] On the other hand, the present application also provides a monitoring method for a childcare environment, including:
[0058] Collecting environmental parameters and behavior parameters; wherein, the environmental parameters include environmental humidity, environmental temperature, and smoke concentration; the behavior parameters include climbing height and aggregation density;
[0059] Judging whether the environment is abnormal respectively according to the environmental humidity, environmental temperature, and smoke concentration. If it is judged that the environment is abnormal, an environmental abnormality value is obtained according to the abnormality judgment result;
[0060] Judging behavior abnormality according to the climbing height. If it is judged as abnormal, a first behavior risk value is obtained according to the climbing height, and it is judged whether to adjust the first behavior risk value according to the climbing height change value. If it is judged that adjustment is needed, the first behavior risk value is adjusted according to the climbing height change value to obtain a first behavior risk final value;
[0061] Judging behavior abnormality according to the aggregation density. If it is judged as abnormal, a second behavior risk value is obtained according to the aggregation density, and it is judged whether to adjust the second behavior risk value according to the aggregation density change value. If it is judged that adjustment is needed, the second behavior risk value is adjusted according to the aggregation density change value to obtain a second behavior risk final value;
[0062] Calculating an abnormal comprehensive final value according to the environmental abnormality value, the first behavior risk final value, and the second behavior risk final value, and performing level warning according to the abnormal comprehensive final value.
[0063] It can be understood that the childcare environment monitoring system and method provided by the present application have the same beneficial effects, which will not be elaborated here. BRIEF DESCRIPTION OF THE DRAWINGS
[0064] By reading the following detailed description of the preferred embodiments, various other advantages and benefits will become clear to those of ordinary skill in the art. The drawings are only for the purpose of showing the preferred embodiments and are not considered to be a limitation of the present invention. Moreover, throughout the drawings, the same reference numerals are used to represent the same components. In the drawings:
[0065] Figure 1 is a functional block diagram of the childcare environment monitoring system provided by an embodiment of the present invention;
[0066] Figure 2 is a flowchart of the childcare environment monitoring method provided by an embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS
[0067] Exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. Although the exemplary embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure can be implemented in various forms and should not be limited by the embodiments set forth herein. On the contrary, these embodiments are provided so that the present disclosure can be more thoroughly understood and the scope of the present disclosure can be fully conveyed to those skilled in the art. It should be noted that, without conflict, the embodiments in the present invention and the features in the embodiments can be combined with each other. The present invention will be described in detail below with reference to the drawings and in combination with the embodiments.
[0068] In some embodiments of the present application, referring to Figure 1 as shown, this embodiment provides a nursery environment monitoring system, including:
[0069] An acquisition module, configured to acquire environmental parameters and behavior parameters; wherein, the environmental parameters include environmental humidity, environmental temperature, and smoke concentration; the behavior parameters include climbing height and aggregation density;
[0070] An environment judgment module, configured to respectively judge whether the environment is abnormal according to the environmental humidity, environmental temperature, and smoke concentration. If it is judged that the environment is abnormal, an environmental abnormal value is obtained according to the abnormal judgment result;
[0071] A behavior judgment module, configured to judge whether the behavior is abnormal according to the climbing height. If it is judged to be abnormal, a first behavior danger value is obtained according to the climbing height, and it is judged whether to adjust the first behavior danger value according to the climbing height change value. If it is judged that adjustment is needed, the first behavior danger value is adjusted according to the climbing height change value to obtain a first behavior danger final value;
[0072] The behavior judgment module is further configured to judge whether the behavior is abnormal according to the aggregation density. If it is judged to be abnormal, a second behavior danger value is obtained according to the aggregation density, and it is judged whether to adjust the second behavior danger value according to the aggregation density change value. If it is judged that adjustment is needed, the second behavior danger value is adjusted according to the aggregation density change value to obtain a second behavior danger final value;
[0073] An early warning module, configured to calculate an abnormal comprehensive final value according to the environmental abnormal value, the first behavior danger final value, and the second behavior danger final value, and perform level early warning according to the abnormal comprehensive final value.
[0074] It can be understood that the present invention improves the safety management ability and risk warning efficiency of childcare institutions. First, the system's real-time monitoring of environmental humidity, temperature, and smoke concentration can accurately identify environmental anomalies (such as fire hazards, inappropriate temperature and humidity), ensuring that the environment where children are located meets health standards and avoiding respiratory diseases or safety accidents caused by environmental factors. Second, the intelligent analysis of behavior parameters (climbing height, aggregation density) can dynamically capture children's dangerous behaviors (such as the risk of falling from climbing high) or group aggregation risks (such as pushing and trampling or the spread of infectious diseases), and adjust the danger level through change values, making the warning more in line with the actual risk evolution trend. For example, when a child continuously climbs and the height increases, the system will dynamically increase the danger value, rather than relying only on single data, greatly reducing the misjudgment rate. Finally, the integrated analysis of environmental and behavior data (abnormal comprehensive final value) realizes cross-dimensional risk assessment, making the warning level more scientific and helping childcare staff to prioritize high-risk events. Overall, the system not only reduces the omissions of manual monitoring, but also can optimize the design and management strategies of the childcare environment through data backtracking, taking into account both safety and science, and providing more intelligent protection for children.
[0075] In some embodiments of the present application, when the environmental judgment module is configured to judge whether the environment is abnormal respectively according to the environmental humidity, environmental temperature, and smoke concentration, it includes:
[0076] Respectively set the environmental humidity range value, environmental temperature range value, and the lowest smoke concentration value;
[0077] If there is a situation where the environmental humidity is not within the environmental humidity range value, the environmental temperature is not within the environmental temperature range value, or the smoke concentration is greater than the lowest smoke concentration value, then judge that the environment is abnormal;
[0078] Otherwise, judge that the environment is not abnormal.
[0079] In some embodiments of the present application, when the environmental judgment module is configured to obtain an environmental anomaly value according to the anomaly judgment result, it includes:
[0080] If it is judged that the environment is abnormal, and there is one of the three conditions that the environmental humidity is not within the environmental humidity range value, the environmental temperature is not within the environmental temperature range value, and the smoke concentration is greater than the lowest smoke concentration value, then the environmental anomaly value is the first environmental anomaly value; if there are two conditions, then the environmental anomaly value is the second environmental anomaly value, and if there are three conditions, then the environmental anomaly value is the third environmental anomaly value;
[0081] If it is judged that the environment is not abnormal, then the environmental anomaly value is zero.
[0082] It is understandable that by presetting the environmental humidity range value, temperature range value, and the minimum smoke concentration value as the reference thresholds, the system can quickly identify single or compound environmental anomalies, greatly improving the accuracy and comprehensiveness of environmental risk detection. By adopting a three-level environmental anomaly value division (the first to the third environmental anomaly values), the system can dynamically quantify the risk level according to the number of abnormal conditions (1 - 3), thereby accurately reflecting the comprehensive severity of environmental anomalies. This hierarchical evaluation method has the following core advantages compared with the traditional binary (normal / abnormal) judgment: First, it can distinguish the risk differences between ordinary anomalies (such as only temperature exceeding the standard) and multiple anomalies (such as simultaneous temperature and humidity anomalies accompanied by smoke), providing a more refined decision-making basis for the subsequent warning module; Second, through numerical output (from zero to the third environmental anomaly value), it realizes the quantitative expression of environmental risks, facilitating the weighted fusion calculation with the behavior danger value; Third, it effectively avoids the overreaction of the system caused by false alarms of a single parameter. For example, when only the humidity slightly exceeds the standard, only a low-level anomaly (the first environmental anomaly value) is triggered, while when multiple anomalies are superimposed, a high-level response (the third environmental anomaly value) is triggered, which not only ensures the monitoring sensitivity but also improves the system reliability.
[0083] In some embodiments of the present application, the behavior judgment module is configured to judge behavior anomalies according to the climbing height. When it is judged as abnormal and the first behavior danger value is obtained according to the climbing height, it includes:
[0084] Set the minimum climbing height. If the climbing height is greater than the minimum climbing height, judge the behavior as abnormal; otherwise, judge that the behavior is not abnormal;
[0085] Set the first height and the second height, where the first height is less than the second height, and the first height is greater than the minimum climbing height;
[0086] If the climbing height is less than or equal to the first height and greater than the minimum climbing height, the first behavior danger value is the first danger value;
[0087] If the climbing height is greater than the first height and less than or equal to the second height, the first behavior danger value is the second danger value;
[0088] If the climbing height is greater than the second height, the first behavior danger value is the third danger value,
[0089] The first danger value is less than the second danger value, and the second danger value is less than the third danger value.
[0090] In some embodiments of the present application, when the behavior judgment module is configured to judge whether to adjust the first behavior danger value according to the climbing height change value, it includes:
[0091] Collect the change value of the climbing height within the preset time. If the change value of the climbing height is greater than zero, it is determined that the danger value of the first behavior is adjusted;
[0092] Otherwise, it is determined that the danger value of the first behavior is not adjusted.
[0093] In some embodiments of the present application, when the behavior judgment module is configured to adjust the danger value of the first behavior according to the change value of the climbing height to obtain the final danger value of the first behavior, it includes:
[0094] Set a first height change value and a second height change value, where the first height change value is less than the second height change value;
[0095] If the change value of the climbing height is less than or equal to the first height change value, the danger value of the first behavior is adjusted by a first adjustment coefficient;
[0096] If the change value of the climbing height is greater than the first height change value and less than or equal to the second height change value, the danger value of the first behavior is adjusted by a second adjustment coefficient;
[0097] If the change value of the climbing height is greater than the second height change value, the danger value of the first behavior is adjusted by a third adjustment coefficient;
[0098] The value range of the adjustment coefficient is: 1 < first adjustment coefficient < second adjustment coefficient < third adjustment coefficient < 1.5, and the adjusted danger value of the first behavior is the product of the danger value of the first behavior before adjustment and the adjustment coefficient.
[0099] It is understandable that the present invention realizes intelligent safety warning for children's climbing behavior. The system uses three-level height thresholds (minimum value, first height, and second height) to initially classify the degree of danger (first to third danger values). This stepped evaluation can accurately distinguish the risk levels corresponding to different height intervals. More advancedly, the system monitors the change value of the climbing height within a preset time in real time, and dynamically weights the initial danger value by using an incremental adjustment coefficient (first to third adjustment coefficients) within the range of 1 to 1.5. This design has three major technical advantages: First, it realizes a two-dimensional risk assessment of "current height + change trend". When it detects that a child climbs rapidly (change value > second height change value), the system will significantly increase the danger level through a larger adjustment coefficient, effectively predicting the possible fall risk. Second, the incremental design of the adjustment coefficient (1 < first < second < third < 1.5) enables the system to intelligently adjust the risk weight according to the climbing speed, conforming to the children's behavior safety rule that "accelerated climbing is more dangerous than uniform climbing". Third, the final value generated through multiplication operation not only retains the basic height assessment result but also amplifies the dynamic characteristics of dangerous behaviors, enabling childcare staff to discover potential risks earlier.
[0100] In some embodiments of the present application, the behavior judgment module is further configured to judge behavioral abnormality according to the aggregation density. When judging as abnormal and obtaining the second behavior danger value according to the aggregation density, it includes:
[0101] Set a minimum aggregation density. If the aggregation density is greater than the minimum aggregation density, judge the behavior as abnormal; otherwise, judge that the behavior is not abnormal;
[0102] Set a first density and a second density, where the first density is less than the second density, and the first density is greater than the minimum aggregation density;
[0103] If the aggregation density is less than or equal to the first density and greater than the minimum aggregation density, the second behavior danger value is the first aggregation danger value;
[0104] If the aggregation density is greater than the first density and less than or equal to the second density, the second behavior danger value is the second aggregation danger value;
[0105] If the aggregation density is greater than the second density, the second behavior danger value is the third aggregation danger value;
[0106] The first aggregation danger value is less than the second aggregation danger value, and the second aggregation danger value is less than the third aggregation danger value.
[0107] In some embodiments of the present application, the behavior determination module is further configured to determine whether to adjust the second behavior risk value according to the change value of the aggregation density. If it is determined that adjustment is required, when adjusting the second behavior risk value according to the change value of the aggregation density to obtain the final value of the second behavior risk, it includes:
[0108] Collect the change value of the aggregation density within a preset time. If the change value of the aggregation density is greater than zero, it is determined to adjust the second behavior risk value; otherwise, it is determined not to adjust the second behavior risk value;
[0109] Set a first density change value and a second density change value, where the first density change value is less than the second density change value;
[0110] If the change value of the aggregation density is less than or equal to the first density change value, adjust the second behavior risk value by a first adjustment coefficient;
[0111] If the change value of the aggregation density is greater than the first density change value and less than or equal to the second density change value, adjust the second behavior risk value by a second adjustment coefficient;
[0112] If the change value of the aggregation density is greater than the second density change value, adjust the second behavior risk value by a third adjustment coefficient;
[0113] The value range of the adjustment coefficient is: 1 < first adjustment coefficient < second adjustment coefficient < third adjustment coefficient < 1.5, and the adjusted second behavior risk value is the product of the second behavior risk value before adjustment and the adjustment coefficient.
[0114] It is understandable that the present invention realizes the intelligent safety monitoring of the gathering behavior of children. The system uses three-level density thresholds (minimum value, first density, and second density) to initially quantify and classify the gathering risks (first to third gathering danger values). This hierarchical assessment can accurately reflect the degree of potential safety hazards at different density levels. The system monitors the density change trend within a preset time in real time and dynamically amplifies the initial danger value by using an incremental adjustment coefficient (first to third adjustment coefficients) within the range of 1 to 1.5. The present invention realizes a two-dimensional risk assessment of "static density + dynamic change". When it detects that children gather rapidly (change value > second density change value), the system will significantly increase the danger level through the highest third adjustment coefficient, effectively predicting possible risks of stampedes or the spread of infectious diseases. Second, the incremental design of the adjustment coefficient (1 < first < second < third < 1.5) enables the system to intelligently adjust the risk weight according to the gathering speed, conforming to the group behavior characteristic that "accelerated gathering is more dangerous than uniform gathering". Third, the final value generated through multiplication operation not only retains the basic density assessment result but also strengthens the warning value of the gathering trend, enabling childcare staff to take evacuation measures in advance. This mechanism is particularly suitable for preventing group safety accidents in childcare institutions and provides scientific decision-making support for safety management through dynamic risk assessment.
[0115] In some embodiments of the present application, when the warning module is configured to calculate an abnormal comprehensive final value based on the environmental outlier, the first behavior danger final value, and the second behavior danger final value, and perform a level warning according to the abnormal comprehensive final value, it includes:
[0116] Weighted summation of the environmental outlier, the first behavior danger final value, and the second behavior danger final value to obtain the abnormal comprehensive final value;
[0117] Set a first final value and a second final value, where the first final value is less than the second final value;
[0118] If the abnormal comprehensive final value is less than the first final value, a first-level warning is issued;
[0119] If the abnormal comprehensive final value is greater than or equal to the first final value and less than or equal to the second final value, a second-level warning is issued;
[0120] If the abnormal comprehensive final value is greater than the second final value, a third-level warning is issued;
[0121] The warning levels are, from low to high, the first warning, the second warning, and the third warning.
[0122] It is understandable that the present invention realizes the intelligent management of the safety risks in the childcare environment. The system uses a weighted summation algorithm to comprehensively analyze the environmental outliers, the final dangerous value of climbing behavior, and the final dangerous value of aggregation density to generate a comprehensive final abnormal value. This data fusion method can comprehensively evaluate the complex risks in the childcare environment. By setting two key thresholds, namely the first final value and the second final value, the system divides the risks into three warning levels (Level 1 to Level 3). This hierarchical warning mechanism has three significant advantages: First, it realizes the quantitative evaluation and visual presentation of risks, enabling childcare personnel to intuitively understand the current safety status; second, the hierarchical response mechanism can take different response measures according to the severity of the risks; third, the algorithm design of multi-parameter weighted fusion fully considers the correlation between environmental factors and behavioral risks, making the comprehensive evaluation results more scientific and accurate. This intelligent warning system is particularly suitable for use in childcare institutions, and can achieve accurate risk identification and hierarchical response in complex environments, significantly improving the safety management efficiency and emergency handling capabilities.
[0123] On the other hand, referring to Figure 2 as shown, the present application also provides a childcare environment monitoring method, which is applied to the above-mentioned childcare environment monitoring system and includes the following steps:
[0124] S100. Collect environmental parameters and behavioral parameters; wherein, the environmental parameters include environmental humidity, environmental temperature, and smoke concentration; the behavioral parameters include climbing height and aggregation density;
[0125] S200. Respectively judge whether the environment is abnormal according to the environmental humidity, environmental temperature, and smoke concentration. If it is judged that the environment is abnormal, obtain the environmental abnormal value according to the abnormal judgment result;
[0126] S300. Judge whether the behavior is abnormal according to the climbing height. If it is judged to be abnormal, obtain the first behavior dangerous value according to the climbing height, judge whether to adjust the first behavior dangerous value according to the climbing height change value. If it is judged that adjustment is needed, adjust the first behavior dangerous value according to the climbing height change value to obtain the first behavior dangerous final value;
[0127] S400. Judge whether the behavior is abnormal according to the aggregation density. If it is judged to be abnormal, obtain the second behavior dangerous value according to the aggregation density, judge whether to adjust the second behavior dangerous value according to the aggregation density change value. If it is judged that adjustment is needed, adjust the second behavior dangerous value according to the aggregation density change value to obtain the second behavior dangerous final value;
[0128] S500. Calculate the comprehensive final abnormal value according to the environmental abnormal value, the first behavior dangerous final value, and the second behavior dangerous final value, and conduct level warning according to the comprehensive final abnormal value.
[0129] It is understandable that the present invention first realizes the all-round monitoring of the nursery environment by synchronously collecting environmental parameters (humidity, temperature, smoke concentration) and behavior parameters (climbing height, aggregation density); secondly, a hierarchical judgment mechanism is adopted to quantitatively evaluate environmental anomalies and behavior anomalies respectively, and the risk assessment result is made more in line with the actual situation through a dynamic adjustment algorithm (based on the height change value and density change value); finally, an abnormal comprehensive final value is generated through weighted fusion calculation, and a three-level early warning mechanism is implemented. The advantages of this method are as follows: First, it realizes the dual guarantee of "environmental safety + behavior safety" and can identify single risk factors and compound risks; second, the dynamic adjustment mechanism enables the system to perceive the change trend of risks and achieve early warning; third, the hierarchical response strategy not only ensures the timely handling of high-risk events but also avoids overreaction to low-risk events. This method significantly improves the risk identification ability and safety management efficiency of nursery institutions and provides more intelligent safety protection for infants and young children.
[0130] Those skilled in the art should understand that the embodiments of the present application can be provided as methods, systems, or computer program products. Therefore, the present application can take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present application can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.
[0131] The present application is described with reference to the flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each flow and / or block in the flowchart and / or block diagram can be implemented by computer program instructions, and the combination of the flows and / or blocks in the flowchart and / or block diagram can also be implemented by computer program instructions. These computer program instructions can be provided to the processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing devices to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing devices generate means for realizing the functions specified in Figure 1 one flow or multiple flows and / or blocks Figure 1 one block or multiple blocks.
[0132] These computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer-readable memory generate a manufactured article including instruction means, and the instruction means realizes the functions specified in Figure 1 one flow or multiple flows and / or blocks Figure 1 one block or multiple blocks.
[0133] These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus, so that a series of operation steps are executed on the computer or other programmable apparatus to produce a computer-implemented process, thereby providing instructions for implementing the steps of the process Figure 1 in one process or a plurality of processes and / or blocks Figure 1 or a plurality of blocks to specify the functions of the steps.
[0134] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit them. Although the present invention has been described in detail with reference to the above embodiments, those of ordinary skill in the art should understand that: the specific implementation manners of the present invention can still be modified or equivalently replaced, and any modification or equivalent replacement that does not depart from the spirit and scope of the present invention shall be covered by the protection scope of the claims of the present invention.
Claims
1. A monitoring system for a childcare environment, characterized in that, Including: A collection module configured to collect environmental parameters and behavior parameters; wherein, the environmental parameters include environmental humidity, environmental temperature, and smoke concentration; the behavior parameters include climbing height and aggregation density; An environment judgment module configured to respectively judge whether the environment is abnormal according to the environmental humidity, environmental temperature, and smoke concentration. If it is judged that the environment is abnormal, an environmental abnormal value is obtained according to the abnormal judgment result; A behavior judgment module configured to judge whether the behavior is abnormal according to the climbing height. If it is judged to be abnormal, a first behavior danger value is obtained according to the climbing height, and it is judged whether to adjust the first behavior danger value according to the climbing height change value. If it is judged that adjustment is needed, the first behavior danger value is adjusted according to the climbing height change value to obtain a first behavior danger final value; The behavior judgment module is further configured to judge whether the behavior is abnormal according to the aggregation density. If it is judged to be abnormal, a second behavior danger value is obtained according to the aggregation density, and it is judged whether to adjust the second behavior danger value according to the aggregation density change value. If it is judged that adjustment is needed, the second behavior danger value is adjusted according to the aggregation density change value to obtain a second behavior danger final value; An early warning module configured to calculate an abnormal comprehensive final value according to the environmental abnormal value, the first behavior danger final value, and the second behavior danger final value, and perform level early warning according to the abnormal comprehensive final value.
2. The childcare environment monitoring system according to claim 1, characterized in that, When the environment judgment module is configured to respectively judge whether the environment is abnormal according to the environmental humidity, environmental temperature, and smoke concentration, it includes: Respectively setting an environmental humidity range value, an environmental temperature range value, and a minimum smoke concentration value; If there is a situation where the environmental humidity is not within the environmental humidity range value, the environmental temperature is not within the environmental temperature range value, or the smoke concentration is greater than the minimum smoke concentration value, it is judged that the environment is abnormal; Otherwise, it is judged that the environment is not abnormal.
3. The monitoring system for the childcare environment according to claim 2, characterized in that, When the environment judgment module is configured to obtain an environmental abnormal value according to the abnormal judgment result, it includes: If it is judged that the environment is abnormal, and there is one of the three conditions that the environmental humidity is not within the environmental humidity range value, the environmental temperature is not within the environmental temperature range value, and the smoke concentration is greater than the minimum smoke concentration value, the environmental abnormal value is the first environmental abnormal value; if there are two conditions, the environmental abnormal value is the second environmental abnormal value; if there are three conditions, the environmental abnormal value is the third environmental abnormal value; If it is judged that the environment is not abnormal, the environmental abnormal value is zero.
4. The monitoring system for the childcare environment according to claim 3, wherein When the behavior judgment module is configured to judge whether the behavior is abnormal according to the climbing height, and if it is judged to be abnormal, obtain a first behavior danger value according to the climbing height, it includes: Setting a minimum climbing height. If the climbing height is greater than the minimum climbing height, it is judged that the behavior is abnormal; otherwise, it is judged that the behavior is not abnormal; Setting a first height and a second height, where the first height is less than the second height, and the first height is greater than the minimum climbing height; If the climbing height is less than or equal to the first height and greater than the minimum climbing height, the first behavior danger value is the first danger value; If the climbing height is greater than the first height and less than or equal to the second height, the danger value of the first behavior is the second danger value; If the climbing height is greater than the second height, the danger value of the first behavior is the third danger value. The first danger value is less than the second danger value, and the second danger value is less than the third danger value.
5. The childcare environment monitoring system according to claim 4, wherein, When the behavior judgment module is configured to judge whether to adjust the danger value of the first behavior according to the climbing height change value, it includes: Collect the climbing height change value within a preset time. If the climbing height change value is greater than zero, judge to adjust the danger value of the first behavior; Otherwise, judge not to adjust the danger value of the first behavior.
6. The monitoring system for the childcare environment according to claim 5, characterized in that, When the behavior judgment module is configured to adjust the danger value of the first behavior according to the climbing height change value to obtain the final danger value of the first behavior, it includes: Set a first height change value and a second height change value, where the first height change value is less than the second height change value; If the climbing height change value is less than or equal to the first height change value, adjust the danger value of the first behavior through a first adjustment coefficient; If the climbing height change value is greater than the first height change value and less than or equal to the second height change value, adjust the danger value of the first behavior through a second adjustment coefficient; If the climbing height change value is greater than the second height change value, adjust the danger value of the first behavior through a third adjustment coefficient; The value range of the adjustment coefficient is: 1 < first adjustment coefficient < second adjustment coefficient < third adjustment coefficient < 1.
5. The adjusted danger value of the first behavior is the product of the unadjusted danger value of the first behavior and the adjustment coefficient.
7. The childcare environment monitoring system according to claim 6, wherein The behavior judgment module is further configured to judge behavioral anomalies according to the aggregation density. When judging as abnormal and obtaining the second behavior danger value according to the aggregation density, it includes: Set a minimum aggregation density. If the aggregation density is greater than the minimum aggregation density, judge the behavior as abnormal; otherwise, judge that the behavior is not abnormal; Set a first density and a second density, where the first density is less than the second density and the first density is greater than the minimum aggregation density; If the aggregation density is less than or equal to the first density and greater than the minimum aggregation density, the second behavior danger value is the first aggregation danger value; If the aggregation density is greater than the first density and less than or equal to the second density, the second behavior danger value is the second aggregation danger value; If the aggregation density is greater than the second density, the second behavior danger value is the third aggregation danger value; The first aggregation danger value is less than the second aggregation danger value, and the second aggregation danger value is less than the third aggregation danger value.
8. The childcare environment monitoring system according to claim 7, characterized in that, The behavior judgment module is further configured to judge whether to adjust the second behavior danger value according to the aggregation density change value. When judging that adjustment is needed and adjusting the second behavior danger value according to the aggregation density change value to obtain the final danger value of the second behavior, it includes: Collect the change value of the aggregation density within the preset time. If the change value of the aggregation density is greater than zero, it is determined to adjust the danger value of the second behavior; otherwise, it is determined not to adjust the danger value of the second behavior; Set a first density change value and a second density change value, where the first density change value is less than the second density change value; If the change value of the aggregation density is less than or equal to the first density change value, adjust the danger value of the second behavior by a first adjustment coefficient; If the change value of the aggregation density is greater than the first density change value and less than or equal to the second density change value, adjust the danger value of the second behavior by a second adjustment coefficient; If the change value of the aggregation density is greater than the second density change value, adjust the danger value of the second behavior by a third adjustment coefficient; The value range of the adjustment coefficient is: 1 < first adjustment coefficient < second adjustment coefficient < third adjustment coefficient < 1.5, and the adjusted danger value of the second behavior is the product of the danger value of the second behavior before adjustment and the adjustment coefficient.
9. The childcare environment monitoring system according to claim 8, characterized in that, When the warning module is configured to calculate the comprehensive anomaly final value based on the environmental anomaly value, the final value of the first behavior danger, and the final value of the second behavior danger, and perform level warning based on the comprehensive anomaly final value, it includes: Perform a weighted sum of the environmental anomaly value, the final value of the first behavior danger, and the final value of the second behavior danger to obtain the comprehensive anomaly final value; Set a first final value and a second final value, where the first final value is less than the second final value; If the comprehensive anomaly final value is less than the first final value, issue a first-level warning; If the comprehensive anomaly final value is greater than or equal to the first final value and less than or equal to the second final value, issue a second-level warning; If the comprehensive anomaly final value is greater than the second final value, issue a third-level warning; The warning levels are, from low to high, the first warning, the second warning, and the third warning.
10. A monitoring method for a childcare environment, applied to the childcare environment monitoring system according to any one of claims 1-9, characterized in that, It includes: Collect environmental parameters and behavior parameters; among them, the environmental parameters include environmental humidity, environmental temperature, and smoke concentration; the behavior parameters include climbing height and aggregation density; Respectively judge whether the environment is abnormal according to the environmental humidity, environmental temperature, and smoke concentration. If it is judged that the environment is abnormal, obtain the environmental anomaly value according to the abnormal judgment result; Judge whether the behavior is abnormal according to the climbing height. If it is judged to be abnormal, obtain the first behavior danger value according to the climbing height, judge whether to adjust the first behavior danger value according to the climbing height change value. If it is judged that adjustment is required, adjust the first behavior danger value according to the climbing height change value to obtain the first behavior danger final value; Judge whether the behavior is abnormal according to the aggregation density. If it is judged to be abnormal, obtain the second behavior danger value according to the aggregation density, judge whether to adjust the second behavior danger value according to the aggregation density change value. If it is judged that adjustment is required, adjust the second behavior danger value according to the aggregation density change value to obtain the second behavior danger final value; Calculate the comprehensive anomaly final value according to the environmental anomaly value, the first behavior danger final value, and the second behavior danger final value, and perform level warning according to the comprehensive anomaly final value.