Pet environmental health early warning system based on electrochemical sensor and cloud platform

Abstract

The invention relates to the technical field of pet health monitoring, and discloses a pet environmental health early warning system based on an electrochemical sensor and a cloud platform. The system comprises an environmental parameter data acquisition module, an environmental health index quantification module, an early warning judgment module and an environmental parameter adjustment module. Wherein the environment parameter data acquisition module monitors a pet environment in real time through an electrochemical sensor and obtains key environment parameters; the environment health index quantification module quantifies the association degree of the environment and pet health based on the collected data, and generates an environment health index; the early warning judgment module judges whether health early warning is triggered or not according to the index; if early warning is triggered, the environment parameter adjusting module adjusts parameters and then collects data again, and if not triggered, the data are directly collected. The system is combined with a cloud platform to realize data management, can accurately monitor the environment, visually present the health association degree, timely perform early warning and perform automatic adjustment, and create a healthy environment for pets.

Description

Technical Field

[0001] This invention relates to the field of pet health monitoring technology, specifically a pet environmental health early warning system based on electrochemical sensors and a cloud platform. Background Technology

[0002] As people's living standards improve, pets are gradually becoming important family members, and their health is receiving increasing attention. The quality of a pet's living environment directly affects its physiological state and quality of life; however, most pet owners still have significant shortcomings in environmental management. Traditional pet environment management methods rely heavily on the owner's subjective observation and experience, which has significant limitations. Owners find it difficult to accurately perceive subtle changes in environmental parameters, such as the concentration of harmful gases in the air and humidity fluctuations. These subtle changes can pose potential threats to the pet's health.

[0003] Currently, while there are some devices on the market for environmental monitoring, most of these devices are single-function, capable of detecting only a single environmental parameter and unable to perform comprehensive analysis of multiple parameters. Furthermore, these devices lack the ability to correlate data with pet health conditions, and therefore cannot determine whether the environment poses a threat to pet health based on the detected parameters, thus failing to provide timely warnings to owners.

[0004] Existing environmental monitoring equipment typically lacks the ability to automatically adjust environmental parameters. When abnormal parameters are detected, owners must manually adjust them, which not only increases their workload but also risks harming the pet's health if they fail to detect abnormalities or make timely adjustments. Furthermore, these devices have weak data storage and management capabilities, making it difficult to effectively store and analyze long-term monitoring data, thus hindering the provision of valuable information for subsequent environmental management and pet health care.

[0005] With the widespread application of cloud technology, existing pet environment monitoring equipment has failed to fully utilize the advantages of cloud platforms, and cannot achieve remote data transmission, real-time sharing, and cloud-based analysis. Pet owners cannot understand the condition of their pets' environment anytime, anywhere, nor can they obtain professional environmental adjustment suggestions through the cloud. This greatly limits the convenience and scientific nature of pet environment management. Summary of the Invention

[0006] The purpose of this invention is to provide a pet environmental health early warning system based on electrochemical sensors and a cloud platform to solve the problems mentioned in the background art.

[0007] To achieve the above objectives, the present invention provides a pet environmental health early warning system based on electrochemical sensors and a cloud platform, the system comprising:

[0008] Environmental parameter data acquisition module, environmental health index quantification module, early warning judgment module, and environmental parameter adjustment module:

[0009] The environmental parameter data acquisition module is used to monitor changes in the pet's environment in real time through an electrochemical sensor and acquire environmental parameter data.

[0010] The environmental health index quantification module is used to quantify the correlation between the environment and pet health through environmental parameter data to obtain the environmental health index.

[0011] The early warning judgment module is used to determine whether to issue a health warning based on the obtained environmental health index.

[0012] The environmental parameter adjustment module is used to re-acquire environmental parameter data after adjusting the environmental parameters if a health warning is issued; otherwise, it directly acquires the environmental parameter data.

[0013] Preferably, the environmental parameter data includes temperature deviation, humidity deviation, harmful gas concentration, and pet activity frequency;

[0014] The environmental health index quantification module is also used to obtain the health index threshold and health index correction amount from the cloud platform database.

[0015] The health index thresholds include temperature deviation threshold, humidity deviation standard value, harmful gas concentration threshold, and pet activity frequency standard value;

[0016] The health index correction includes temperature deviation correction, humidity deviation correction, harmful gas concentration correction, and pet activity frequency correction.

[0017] Preferably, the specific steps for obtaining the environmental health index are as follows:

[0018] The temperature deviation impact value is obtained by correcting the temperature deviation threshold and the ratio analysis results of temperature deviation by the temperature deviation correction amount.

[0019] The humidity deviation impact value is obtained by correcting the ratio of the humidity deviation standard value to the degree of humidity deviation through the humidity deviation correction amount.

[0020] The influence value of harmful gas concentration is obtained by correcting the proportion of the harmful gas concentration threshold and the degree of deviation of the harmful gas concentration through the harmful gas concentration correction amount.

[0021] The pet activity frequency impact value is obtained by correcting the percentage of deviation between the standard value and the pet activity frequency by adjusting the pet activity frequency correction factor.

[0022] The environmental health index is obtained by coupling the influence values ​​of temperature deviation, humidity deviation, harmful gas concentration, and pet activity frequency.

[0023] The environmental health index represents quantitative data on the combined effects of temperature deviation, humidity deviation, harmful gas concentration, and pet activity frequency on environmental health.

[0024] Preferably, the specific steps for determining whether to issue a health warning based on the obtained environmental health index are as follows:

[0025] The environmental health index is compared with the preset health index threshold obtained from the cloud platform database. If the environmental health index is greater than or equal to the preset health index threshold obtained from the cloud platform database, no health warning is issued; otherwise, a health warning is issued.

[0026] The specific steps for issuing a health warning are as follows:

[0027] If the environmental health index is within the preset health index safety range, then environmental parameters will be adjusted. The preset health index safety range represents the range between the environmental health index being greater than the preset health index safety threshold and less than the preset health index threshold.

[0028] If the environmental health index is lower than or equal to the preset health index safety threshold, the initial airflow in the pet environment ventilation area is compensated by the obtained airflow adjustment amount, which is obtained by mapping the harmful gas concentration and environmental health index into the cloud platform database.

[0029] Preferably, the specific process for adjusting the environmental parameters is as follows:

[0030] Determine whether to humidify based on the obtained humidity value. If so, determine whether to adjust the temperature after humidification. Otherwise, determine whether to adjust the temperature directly.

[0031] If the temperature value is within the preset safe temperature range, no temperature adjustment will be performed. The preset safe temperature range represents the range where the temperature value is greater than or equal to the preset minimum temperature and less than or equal to the preset maximum temperature.

[0032] If the temperature value is greater than the preset maximum temperature, the temperature value will be compensated by the obtained cooling amount;

[0033] If the temperature value is lower than the preset minimum temperature, the temperature value will be compensated by the obtained heating amount.

[0034] Preferably, the specific steps for determining whether to implement humidification measures based on the obtained humidity value are as follows:

[0035] If the humidity value is less than or equal to the preset safe humidity threshold, the initial start-up time of the humidifier is corrected by the obtained start-up time adjustment amount;

[0036] If the humidity value is within the safe humidity range, the initial water mist flow rate is corrected by the obtained water mist flow rate adjustment amount. The safe humidity range represents the range where the humidity value is greater than the preset safe humidity threshold and less than the preset humidity threshold.

[0037] If the humidity value is greater than or equal to the preset humidity threshold, a prompt to turn off the humidifier will be sent.

[0038] Preferably, the system further includes a sensor time synchronization module:

[0039] The sensor time synchronization module is used to acquire the current time information and current location information of the electrochemical sensor cluster, which includes at least two electrochemical sensors.

[0040] The clock error is obtained based on the master station clock and the device clock;

[0041] Clock synchronization of the electrochemical sensor cluster based on clock error;

[0042] In response to the clock synchronization completion signal, data acquisition information is generated based on the current time and current location information.

[0043] Preferably, the specific steps for generating the data acquisition information are as follows:

[0044] Based on the synchronization location and current location information, an initial data acquisition path is generated;

[0045] Based on the initial data acquisition path and current time information, predict the data acquisition status of the electrochemical sensor;

[0046] Determine whether there are data collection conflicts based on the data collection situation;

[0047] If it does not exist, the current time information and the initial data acquisition path will be output as data acquisition information;

[0048] If present, identify the first and second electrochemical sensors that cause the conflict.

[0049] The current time information of the target electrochemical sensor is updated to obtain the updated time information;

[0050] The update time information and the initial data acquisition path are output as data acquisition information.

[0051] Preferably, the system further includes a warning threshold optimization module:

[0052] The early warning threshold optimization module is used to perform pattern recognition processing on environmental parameter data to obtain representative environmental parameter data;

[0053] The environmental parameter representative data are processed by signal decomposition to obtain high-frequency fluctuation data and low-frequency trend data;

[0054] A hierarchical environmental health early warning model is constructed based on high-frequency fluctuation data and low-frequency trend data. The hierarchical environmental health early warning model includes an upper-level model and a lower-level model.

[0055] High-frequency fluctuation data and low-frequency trend data are input into the upper and lower level models, and iterative solutions are performed according to the constraints to obtain the optimized health warning threshold.

[0056] Preferably, the upper-level model of the hierarchical environmental health early warning model is used to minimize environmental volatility;

[0057] The lower-level model of the hierarchical environmental health early warning model is used to maximize the system's operational efficiency;

[0058] The constraints include temperature safety range constraints, humidity safety range constraints, harmful gas concentration safety range constraints, and pet activity frequency safety range constraints.

[0059] The specific steps of the iterative solution are as follows: input high-frequency fluctuation data and low-frequency trend data into the upper-level model to perform volatility analysis and obtain preliminary early warning threshold results;

[0060] The preliminary warning threshold results are input into the lower-level model and corrected to obtain the optimized health warning threshold.

[0061] Compared with the prior art, the beneficial effects of the present invention are:

[0062] The environmental parameter data acquisition module uses electrochemical sensors to monitor changes in the pet's environment in real time and acquire parameter data. Compared with traditional single-parameter detection devices, it can capture a variety of key parameters in the pet's environment more comprehensively and accurately. Whether it is the concentration of harmful gases or parameters such as temperature and humidity, it can achieve real-time and accurate monitoring, allowing owners to clearly understand the true condition of the pet's environment and avoid ignoring potential environmental risks due to the limitations of single-parameter monitoring.

[0063] The Environmental Health Index Quantification Module quantifies the correlation between the environment and pet health through environmental parameter data to obtain the Environmental Health Index. This index can transform the originally scattered and abstract environmental parameter data into intuitive and easy-to-understand indicators, enabling pet owners to quickly judge the matching degree between the current environment and pet health without having professional environmental monitoring knowledge. It breaks the traditional model of relying on subjective experience to judge, making the judgment of the impact of the environment on pet health more objective and scientific, and helping pet owners to promptly discover environmental problems that are not easily detected but may affect pet health.

[0064] The early warning judgment module determines whether to issue a health warning based on the obtained environmental health index. It can issue warning signals at the early stage when the environment may have an adverse effect on the pet's health. Compared with the traditional method that requires owners to discover environmental abnormalities on their own, it greatly shortens the time for abnormality discovery, gives owners more time to take countermeasures, effectively avoids the situation where pet health is harmed due to failure to discover environmental problems in time, and reduces the probability of pets getting sick due to environmental problems.

[0065] The environmental parameter adjustment module can re-acquire environmental parameter data after adjustments are made when health alerts are needed, eliminating the need for manual adjustments by pet owners. This reduces their workload, making environmental management more convenient and efficient, especially for busy owners who cannot constantly monitor their pets' environment. Furthermore, re-acquiring parameter data after adjustments allows for timely verification of the effectiveness of the measures, ensuring that environmental parameters quickly return to a range conducive to pet health. This forms a closed-loop management system of "monitoring-early warning-adjustment-re-monitoring," continuously safeguarding the health of the pet's environment.

[0066] By integrating a cloud platform, the system enables remote transmission and real-time sharing of environmental parameter data. Regardless of their location, pet owners can access real-time monitoring of their pets' environment parameters, health indices, and alerts via their devices, significantly enhancing the convenience of pet environment management. The cloud platform also stores and analyzes long-term monitoring data. By analyzing historical data, it can identify patterns in environmental parameter changes and their long-term impact on pet health, providing owners with more targeted environmental management recommendations. This helps owners create a more suitable living environment for their pets, further improving their quality of life and health. Furthermore, the cloud platform provides excellent scalability, allowing for the addition of more functional modules to continuously improve system performance and better meet the diverse environmental management needs of pet owners. Attached Figure Description

[0067] Figure 1This is a timing diagram of the pet environmental health early warning system based on electrochemical sensors and a cloud platform as described in this invention.

[0068] Figure 2 A flowchart for calculating the environmental health index;

[0069] Figure 3 Flowchart for adjusting environmental parameters;

[0070] Figure 4 A flowchart for sensor time synchronization;

[0071] Figure 5 A flowchart for iteratively solving the optimal health warning threshold. Detailed Implementation

[0072] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0073] Please see Figure 1 This invention provides a pet environmental health early warning system based on electrochemical sensors and a cloud platform, the system comprising:

[0074] The system uses electrochemical sensors to monitor changes in the pet's environment in real time, acquiring environmental parameter data. This data is then used to quantify the correlation between the environment and pet health, resulting in an environmental health index. Based on this index, a health alert is issued. If an alert is issued, environmental parameter data is acquired again after adjustments are made; otherwise, the data is retrieved directly.

[0075] Example 1: See Figure 2The environmental parameter data acquisition module continuously monitors changes in the physical environment through multiple electrochemical sensors deployed in the pet's living area. These sensors periodically collect data on temperature, humidity, and the concentration levels of specific harmful gases (such as ammonia and hydrogen sulfide), while simultaneously capturing pet activity frequency data through auxiliary infrared motion sensors. All raw data undergoes preliminary filtering and analog-to-digital conversion to form a digital environmental parameter dataset. This dataset contains four core parameters: temperature deviation, humidity deviation, harmful gas concentration, and pet activity frequency. Temperature deviation refers to the difference between the measured temperature and a preset reference temperature; humidity deviation is the difference between the measured humidity and the reference humidity; harmful gas concentration is directly obtained from gas sensor readings; and pet activity frequency is quantified by the number of pet movements detected per unit time. Upon receiving the environmental parameter data, the environmental health index quantification module accesses a cloud platform database. This database stores reference standard values ​​derived from extensive historical data analysis and veterinary health assessments, including temperature deviation thresholds, humidity deviation standard values, harmful gas concentration thresholds, and pet activity frequency standard values. In addition, the database also provides a set of dynamically adjusted correction factors, namely temperature deviation correction, humidity deviation correction, harmful gas concentration correction, and pet activity frequency correction.

[0076] The calculation of the environmental health index is a multi-step quantitative analysis. For temperature deviation, the system first calculates the absolute ratio of the current temperature deviation to the temperature deviation threshold, obtaining an initial temperature influence coefficient. This coefficient is then combined with a temperature deviation correction amount obtained from the cloud platform, and a weighted calculation is performed to generate the final temperature deviation influence value. The calculation of the humidity deviation influence value uses a similar principle. The system analyzes the degree of deviation of the current humidity deviation from the standard humidity deviation value and converts it into a proportional relationship. After adjustment by the humidity deviation correction amount, this proportional relationship is output as the humidity deviation influence value.

[0077] The handling of harmful gas concentrations has its own unique characteristics. The system compares the real-time gas concentration values ​​with harmful gas concentration thresholds, calculates the degree to which the concentration exceeds the threshold, and generates a concentration deviation coefficient. This coefficient is then used in conjunction with a harmful gas concentration correction factor to generate a harmful gas concentration impact value. The calculation of the pet activity frequency impact value differs; the system focuses on the difference between the current activity frequency and the standard pet activity frequency. Both excessive activity and unusual inactivity may indicate environmental discomfort or health problems. The system calculates the absolute value of the frequency deviation and modulates it using a pet activity frequency correction factor to obtain the final pet activity frequency impact value. After obtaining the four independent impact values, the system uses a weighted average-based coupling algorithm to integrate them into a comprehensive environmental health index. This index, as a scalar value, comprehensively reflects the combined impact of four key environmental dimensions—temperature, humidity, air quality, and behavioral activity—on the pet's health status. The lower the index value, the more severe the deviation of the environmental parameters from the ideal state, and the greater the potential threat to the pet's health. The entire calculation process is completed within the embedded processor, and the results are transmitted to the early warning judgment module for decision-making. At the same time, the index and its corresponding raw data are synchronously uploaded to the cloud platform database for long-term trend analysis and model optimization.

[0078] Example 2: The early warning judgment module undertakes the core decision-making function of the system. It receives the environmental health index calculated by the environmental health index quantification module and determines whether to trigger a health warning based on this index value. After the early warning judgment module is activated, it first obtains a preset health index threshold from the cloud platform database. This threshold is a benchmark value determined by analyzing a large amount of historical health environment data, reflecting the comprehensive level of environmental parameters when the pet is in a healthy state. The threshold data in the cloud platform database is dynamically updated according to factors such as pet type, age, and seasonal changes to ensure its adaptability and accuracy.

[0079] The system compares and analyzes the real-time calculated environmental health index with a preset health index threshold obtained from the cloud platform. The comparison process uses a numerical comparison algorithm. If the environmental health index is greater than or equal to the preset health index threshold, it indicates that the current environmental parameters are within an acceptable range, and the system determines not to issue a health warning. At this time, the system maintains normal operation, continuing to periodically collect environmental parameter data and calculate the environmental health index. When the environmental health index falls below the preset health index threshold, the system determines that a health warning is needed. After the warning is triggered, the system enters a tiered response mode. The system first checks the numerical range of the environmental health index; this check involves comparing it with the preset health index safety threshold. The preset health index safety threshold is another important benchmark value; it defines the minimum acceptable level of environmental health, and this value is usually lower than the preset health index threshold but higher than the dangerous level.

[0080] If the environmental health index is within the preset safe range (i.e., the index value is greater than the preset safe threshold but less than the preset threshold), the system initiates an environmental parameter adjustment procedure. This range indicates that while the environmental condition has not reached an ideal level, it has not yet endangered the pet's health. The system will select appropriate adjustment strategies based on the specific deviations in environmental parameters, such as adjusting temperature, humidity, or ventilation. When the environmental health index is lower than or equal to the preset safe threshold, it indicates that the environmental parameters have reached a level requiring immediate intervention. In this case, the system will activate an emergency response mechanism, focusing on addressing potential issues such as excessive concentrations of harmful gases. The system uses a mapping algorithm to input the current harmful gas concentration value and the environmental health index value into the cloud platform database for matching and querying.

[0081] The cloud platform database stores a mapping table validated by extensive experimental data, establishing a correspondence between harmful gas concentrations, environmental health indices, and airflow adjustment amounts. The system queries this mapping table to obtain a suggested airflow adjustment value for the current environmental conditions. This airflow adjustment amount is a calculated parameter indicating the specific degree of initial airflow compensation needed for the pet environment's ventilation area. After obtaining the airflow adjustment amount, the system controls the ventilation equipment to perform corresponding airflow compensation operations. Compensation operations may include increasing ventilation volume, adjusting airflow direction, or changing ventilation duration. While performing airflow adjustments, the system continuously monitors changes in environmental parameters, particularly the decrease in harmful gas concentrations. Throughout the entire warning judgment and execution process, the system maintains real-time data interaction with the cloud platform database. Relevant data for each warning event, including environmental health index values, warning trigger time, measures taken, and their effects, are recorded and uploaded to the cloud platform. This historical data provides crucial reference for subsequent threshold optimization and warning strategy adjustments. The system also includes a warning status exit mechanism. When the environmental health index, after adjustments to environmental parameters, recovers to above the preset health index threshold, the system automatically lifts the warning and records the complete handling process of this warning event. This design ensures that the system can respond promptly to environmental improvements and avoid unnecessary continuous warnings.

[0082] Example 3: See Figure 3If the monitored humidity value is less than or equal to the preset safe humidity threshold, it indicates that the environment is too dry, which may adversely affect the respiratory mucosa and skin health of pets. At this time, the system starts the humidification program. Its control strategy is not simply to turn on the device, but rather to determine the operating parameters of the humidifier based on a calculation model. The system calculates a start-up time adjustment amount, which is determined by a lookup table method based on the deviation between the current humidity value and the target humidity, as well as factors such as the environmental volume. The system will correct the initial start-up time of the humidifier according to this adjustment amount, achieving precise humidity compensation. When the humidity value is within the safe humidity range, i.e., the humidity value is greater than the preset safe humidity threshold but less than the preset humidity threshold, the system enters the humidity fine-tuning mode. In this mode, the system calculates a water mist flow rate adjustment amount, which is used to correct the initial water mist flow rate of the humidifier. This correction can maintain the ambient humidity within the optimal range, avoiding excessively high or low humidity. The calculation of the water mist flow rate adjustment amount considers multiple factors such as the deviation between the current humidity value and the ideal humidity value, the trend of ambient temperature changes, and the pet's activity status.

[0083] If the humidity sensor detects a humidity value greater than or equal to a preset humidity threshold, it indicates that the ambient humidity is too high, which may promote microbial growth or cause discomfort to the pet. In this case, the system will not immediately activate the dehumidifier; instead, it will first send a prompt to the user terminal to turn off the humidifier, while simultaneously recording this status information in the cloud platform database. This design avoids drastic fluctuations in environmental parameters that may result from over-regulation. After completing the humidity control judgment, the system transitions to the temperature adjustment judgment process. The temperature sensor collects ambient temperature data in real time, and the system compares this data with a preset safe temperature range. The upper and lower limits of this temperature range, i.e., the preset minimum and maximum temperatures, are set based on research data on pets' thermal comfort zones. If the temperature value is within the preset safe temperature range, the system maintains the current operating status of the temperature control device without making additional adjustments. When the temperature value exceeds the preset maximum temperature, the system initiates a cooling program. The cooling amount is calculated based on factors such as the difference between the current temperature and the target temperature, the ambient heat capacity, and heat exchange efficiency. The system compensates for the temperature value by controlling the operating power and duration of cooling devices such as air conditioners or fans. This compensation process is a gradual adjustment process to avoid sudden temperature changes causing stress to the pet.

[0084] If the temperature falls below the preset minimum temperature, the system initiates the heating program. The heating amount is calculated based on the temperature difference, the space's heat loss characteristics, and the performance parameters of the heating equipment. The system gradually raises the ambient temperature to a safe range by adjusting the output power of the heating equipment. Throughout the temperature control process, the system continuously monitors temperature trends and dynamically adjusts control parameters based on real-time feedback.

[0085] The execution process of the environment parameter adjustment module can be represented as follows:

[0086]

[0087] in: It represents the environmental control quantity and is a comprehensive output parameter used to guide the operating intensity of specific actuators; This indicates the instantaneous deviation between the current temperature and the target temperature; This indicates the instantaneous deviation between the current humidity and the target humidity. This represents a personalized configuration file for your pet, containing information such as pet breed, age, and health status. This represents the environmental factor correction coefficient, taking into account environmental characteristics such as space volume and ventilation conditions. The system continuously updates control parameters through a cloud platform database and can automatically optimize control strategies based on historical control effect data. After each environmental parameter adjustment, the system restarts the environmental parameter data acquisition module to obtain new environmental parameter data, thus forming a complete closed-loop control system. This design allows the system to adapt to changes in environmental needs across different seasons, climates, and different growth stages of pets, achieving truly personalized environmental health management.

[0088] Example 4: See Figure 4 Upon system startup, the sensor time synchronization module first acquires the current time and location information of each node in the electrochemical sensor cluster. The electrochemical sensor cluster typically consists of multiple sensor nodes deployed in different areas of the pet environment, each equipped with an independent clock circuit and location marker. The current time information includes the local clock reading of each sensor node, while the current location information is obtained through the node's built-in positioning module or preset location coordinates. The module establishes a connection with the master clock via a wired or wireless communication network, typically using a high-precision Network Time Protocol (NTP) clock source. The system periodically collects the device clock readings of each sensor node, compares and analyzes them with the master clock, and calculates the clock error value for each node. The clock error calculation considers factors such as signal transmission delay and processing time delay, and improves accuracy by averaging multiple measurements. Based on the calculated clock error value, the system performs clock synchronization correction on the electrochemical sensor cluster. The synchronization process employs a gradual adjustment strategy to avoid clock jumps interfering with data acquisition. The system sends clock adjustment commands to each sensor node, and upon receiving the commands, the nodes gradually adjust their local clocks until they are synchronized with the master clock. Throughout the synchronization process, the system continuously monitors the clock status of each node to ensure the reliability and stability of the synchronization operation.

[0089] Once the system detects that all sensor nodes have completed clock synchronization, it generates a clock synchronization completion signal. In response to this signal, the module begins generating data acquisition information based on the current time and location information. This process first generates an initial data acquisition path based on the synchronized location information and the current location information. This path planning considers factors such as the spatial distribution of sensor nodes, environmental characteristics, and data transmission efficiency, aiming to optimize the order and route of data acquisition. Based on the initial data acquisition path and the synchronized time information, the system predicts the data acquisition status of each electrochemical sensor. This prediction is based on parameters such as the sampling period of the sensor nodes, the data transmission rate, and the network status, and uses simulation analysis to anticipate potential acquisition conflicts. The system establishes a data acquisition conflict judgment mechanism to evaluate and analyze the prediction results. Table 1 shows the error comparison before and after sensor clock synchronization.

[0090] Table 1: Clock Synchronization Error Adjustment Table.

[0091] Sensor node number Time error before synchronization (milliseconds) Time error after synchronization (milliseconds) Position coordinates Adjusting status SN2024001 125 3 A1 Synchronized SN2024002 87 2 B2 Synchronized SN2024003 156 4 C3 Synchronized SN2024004 203 5 D4 Synchronized SN2024005 94 3 E5 Synchronized

[0092] If the predictive analysis indicates that there are no data acquisition conflicts, the system directly outputs the final data acquisition information by combining the current time information and the initial data acquisition path. This information includes the acquisition time schedule and acquisition sequence of each sensor, providing a basis for subsequent data acquisition operations.

[0093] When the system predicts a data acquisition conflict, it activates a conflict resolution mechanism. The system first identifies the specific sensor node causing the conflict, distinguishing between the first and second electrochemical sensors. By analyzing the acquisition time parameters of the conflicting node, the system selects the target electrochemical sensor for time adjustment. The current time information of the target electrochemical sensor is updated, resulting in new updated time information. This update process considers factors such as the urgency and importance of the data acquisition, minimizing the impact on the original acquisition plan while avoiding conflicts. The updated time information and the initial data acquisition path are then recombined, outputting the final data acquisition information. Throughout this process, the system records all adjustment operations and uploads the adjustment information to the cloud platform database. Based on historical adjustment data, the cloud platform continuously optimizes the data acquisition path planning algorithm and conflict prediction model, improving the system's adaptability.

[0094] Example 5: See Figure 5When the early warning threshold optimization module starts, it first performs pattern recognition processing on the environmental parameter data. This environmental parameter data comes from historical datasets accumulated by sensors over a long period of time, including monitoring values ​​of multiple dimensions such as temperature deviation, humidity deviation, harmful gas concentration, and pet activity frequency. The pattern recognition processing uses clustering analysis to classify data points with similar characteristics and identify typical and abnormal patterns of environmental parameters. Through this processing, the system extracts representative environmental parameter data that can represent environmental characteristics from massive amounts of data. These representative data reflect the typical characteristics and changing patterns of the environmental state. After completing pattern recognition, the module performs signal decomposition processing on the representative environmental parameter data. Signal decomposition uses multi-resolution analysis to decompose the representative environmental parameter data into combinations of different frequency components. Through this processing, the system obtains two components: high-frequency fluctuation data and low-frequency trend data. High-frequency fluctuation data reflects the short-term changes and instantaneous fluctuations of the environment, including noise components and rapidly changing signals. Low-frequency trend data reflects the long-term changing trends and slow evolution of the environment, reflecting the overall direction of change of environmental parameters.

[0095] Based on the high-frequency fluctuation data and low-frequency trend data obtained from the decomposition, a hierarchical environmental health early warning model is constructed. This model adopts a two-layer structure, comprising an upper-layer model and a lower-layer model. The upper-layer model primarily focuses on the stability of the environmental state, aiming to minimize environmental volatility by controlling the fluctuation amplitude of environmental parameters to maintain environmental stability. The lower-layer model focuses on the overall performance of the system, aiming to maximize system operational efficiency by optimizing resource utilization efficiency while ensuring environmental health. The operation of the hierarchical model is subject to various constraints, including safe temperature ranges, safe humidity ranges, safe hazardous gas concentration ranges, and safe pet activity frequency ranges. Each constraint defines a safe operating range for the corresponding environmental parameters, ensuring that the optimized early warning threshold remains within a safe and controllable range. The parameter values ​​for these constraints are derived from veterinary expert knowledge and historical operational data, reflecting a thorough consideration of pet health needs.

[0096] After high-frequency fluctuation data and low-frequency trend data are input into the hierarchical model, the system begins an iterative solution process. The iterative solution first inputs the data into the upper-level model for volatility analysis. The upper-level model analyzes the statistical characteristics of the high-frequency fluctuation data, calculates the degree of fluctuation and frequency of change of environmental parameters, and generates a preliminary warning threshold result based on the objective of minimizing volatility. This result reflects the optimal warning threshold setting from the perspective of environmental stability. The preliminary warning threshold result is then input into the lower-level model for further correction. The lower-level model, from the perspective of system operational efficiency, comprehensively considers multiple factors such as energy consumption costs, equipment lifespan, and response speed to adjust and optimize the preliminary warning threshold result. The correction process uses a stepwise approximation method, searching through multiple iterations to find the optimal threshold setting that ensures both environmental stability and improves system efficiency while satisfying all constraints.

[0097] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus.

[0098] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A pet environmental health early warning system based on electrochemical sensors and a cloud platform, characterized in that, It includes an environmental parameter data acquisition module, an environmental health index quantification module, an early warning judgment module, and an environmental parameter adjustment module: The environmental parameter data acquisition module is used to monitor changes in the pet's environment in real time through an electrochemical sensor and acquire environmental parameter data. The environmental health index quantification module is used to quantify the correlation between the environment and pet health through environmental parameter data to obtain the environmental health index. The early warning judgment module is used to determine whether to issue a health warning based on the obtained environmental health index. The environmental parameter adjustment module is used to re-acquire environmental parameter data after adjusting the environmental parameters if a health warning is issued; otherwise, it directly acquires the environmental parameter data.

2. The pet environmental health early warning system based on electrochemical sensors and a cloud platform as described in claim 1, characterized in that, The environmental parameter data includes temperature deviation, humidity deviation, concentration of harmful gases, and frequency of pet activity; The environmental health index quantification module is also used to obtain the health index threshold and health index correction amount from the cloud platform database. The health index thresholds include temperature deviation threshold, humidity deviation standard value, harmful gas concentration threshold, and pet activity frequency standard value; The health index correction includes temperature deviation correction, humidity deviation correction, harmful gas concentration correction, and pet activity frequency correction.

3. The pet environmental health early warning system based on electrochemical sensors and a cloud platform as described in claim 2, characterized in that, The specific steps for obtaining the environmental health index are as follows: The temperature deviation impact value is obtained by correcting the temperature deviation threshold and the ratio analysis results of temperature deviation by the temperature deviation correction amount. The humidity deviation impact value is obtained by correcting the ratio of the humidity deviation standard value to the degree of humidity deviation through the humidity deviation correction amount. The influence value of harmful gas concentration is obtained by correcting the proportion of the harmful gas concentration threshold and the degree of deviation of the harmful gas concentration through the harmful gas concentration correction amount. The pet activity frequency impact value is obtained by correcting the percentage of deviation between the standard value and the pet activity frequency by adjusting the pet activity frequency correction factor. The environmental health index is obtained by coupling the influence values ​​of temperature deviation, humidity deviation, harmful gas concentration, and pet activity frequency. The environmental health index represents quantitative data on the combined effects of temperature deviation, humidity deviation, harmful gas concentration, and pet activity frequency on environmental health.

4. The pet environmental health early warning system based on electrochemical sensors and a cloud platform as described in claim 1, characterized in that, The specific steps for determining whether to issue a health warning based on the obtained environmental health index are as follows: The environmental health index is compared with the preset health index threshold obtained from the cloud platform database. If the environmental health index is greater than or equal to the preset health index threshold obtained from the cloud platform database, no health warning is issued; otherwise, a health warning is issued. The specific steps for issuing a health warning are as follows: If the environmental health index is within the preset health index safety range, then environmental parameters will be adjusted. The preset health index safety range represents the range between the environmental health index being greater than the preset health index safety threshold and less than the preset health index threshold. If the environmental health index is lower than or equal to the preset health index safety threshold, the initial airflow in the pet environment ventilation area is compensated by the obtained airflow adjustment amount, which is obtained by mapping the harmful gas concentration and environmental health index into the cloud platform database.

5. The pet environmental health early warning system based on electrochemical sensors and a cloud platform as described in claim 4, characterized in that, The specific process for adjusting the environmental parameters is as follows: Determine whether to humidify based on the obtained humidity value. If so, determine whether to adjust the temperature after humidification. Otherwise, determine whether to adjust the temperature directly. If the temperature value is within the preset safe temperature range, no temperature adjustment will be performed. The preset safe temperature range represents the range where the temperature value is greater than or equal to the preset minimum temperature and less than or equal to the preset maximum temperature. If the temperature value is greater than the preset maximum temperature, the temperature value will be compensated by the obtained cooling amount; If the temperature value is lower than the preset minimum temperature, the temperature value will be compensated by the obtained heating amount.

6. The pet environmental health early warning system based on electrochemical sensors and a cloud platform as described in claim 5, characterized in that, The specific steps for determining whether to implement humidification measures based on the obtained humidity value are as follows: If the humidity value is less than or equal to the preset safe humidity threshold, the initial start-up time of the humidifier is corrected by the obtained start-up time adjustment amount; If the humidity value is within the safe humidity range, the initial water mist flow rate is corrected by the obtained water mist flow rate adjustment amount. The safe humidity range represents the range where the humidity value is greater than the preset safe humidity threshold and less than the preset humidity threshold. If the humidity value is greater than or equal to the preset humidity threshold, a prompt to turn off the humidifier will be sent.

7. The pet environmental health early warning system based on electrochemical sensors and a cloud platform as described in claim 4, characterized in that, It also includes a sensor time synchronization module: The sensor time synchronization module is used to acquire the current time information and current location information of the electrochemical sensor cluster, which includes at least two electrochemical sensors. The clock error is obtained based on the master station clock and the device clock; Clock synchronization of the electrochemical sensor cluster based on clock error; In response to the clock synchronization completion signal, data acquisition information is generated based on the current time and current location information.

8. The pet environmental health early warning system based on electrochemical sensors and a cloud platform as described in claim 7, characterized in that, The specific steps for generating the data collection information are as follows: Based on the synchronization location and current location information, an initial data acquisition path is generated; Based on the initial data acquisition path and current time information, predict the data acquisition status of the electrochemical sensor; Determine whether there are data collection conflicts based on the data collection situation; If it does not exist, the current time information and the initial data acquisition path will be output as data acquisition information; If present, identify the first and second electrochemical sensors that cause the conflict. The current time information of the target electrochemical sensor is updated to obtain the updated time information; The update time information and the initial data acquisition path are output as data acquisition information.

9. The pet environmental health early warning system based on electrochemical sensors and a cloud platform as described in claim 1, characterized in that, It also includes a warning threshold optimization module: The early warning threshold optimization module is used to perform pattern recognition processing on environmental parameter data to obtain representative environmental parameter data; The environmental parameter representative data are processed by signal decomposition to obtain high-frequency fluctuation data and low-frequency trend data; A hierarchical environmental health early warning model is constructed based on high-frequency fluctuation data and low-frequency trend data. The hierarchical environmental health early warning model includes an upper-level model and a lower-level model. High-frequency fluctuation data and low-frequency trend data are input into the upper and lower level models, and iterative solutions are performed according to the constraints to obtain the optimized health warning threshold.

10. The pet environmental health early warning system based on electrochemical sensors and a cloud platform as described in claim 9, characterized in that, The upper-level model of the hierarchical environmental health early warning model is used to minimize environmental volatility. The lower-level model of the hierarchical environmental health early warning model is used to maximize the system's operational efficiency; The constraints include temperature safety range constraints, humidity safety range constraints, harmful gas concentration safety range constraints, and pet activity frequency safety range constraints. The specific steps of the iterative solution are as follows: input high-frequency fluctuation data and low-frequency trend data into the upper-level model to perform volatility analysis and obtain preliminary early warning threshold results; The preliminary warning threshold results are input into the lower-level model and corrected to obtain the optimized health warning threshold.

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