Adaptive gas detection method based on initial response dynamic feedback, electronic device and storage medium
By using real-time ambient temperature sensing and dynamic adjustment of heating duration and sampling frequency, an initial response curve is generated and signal characteristic parameters are extracted. This solves the problems of high cost and high power consumption in sensor array solutions, and achieves accurate gas detection and adaptive capability in complex environments.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- Filing Date
- 2026-05-27
- Publication Date
- 2026-08-04
AI Technical Summary
Existing sensor array solutions are costly and power-consuming in gas detection, making it difficult to achieve accurate gas detection.
The ambient temperature is acquired in real time by a temperature sensor, the heating time and sampling frequency are dynamically adjusted, an initial response curve is generated and signal feature parameters are extracted, and the target environmental scenario type is determined based on the signal feature parameters.
By ensuring the sensor operates at its optimal state, it can accurately identify clear scenarios such as clean and high-risk environments, and effectively distinguish between single known gases, unknown or mixed gases, and ambiguous boundary situations, thereby reducing costs and power consumption.
Smart Images
Figure CN122282883B_ABST
Abstract
Description
Technical Field
[0001] This application relates to the field of sensor technology, specifically to an adaptive gas detection method, electronic device, and storage medium based on dynamic feedback of initial response. Background Technology
[0002] Typically, for situations involving multiple different gases, a sensor array approach is used to distinguish and detect different gas types. The main principle is to use an array of multiple sensors that have cross-sensitivities to different gases. During actual detection, when a gas comes into contact with the sensor array, each sensor will produce a different response. These responses form a unique "response fingerprint." By collecting the steady-state response values of all sensors in the sensor array, a multi-dimensional feature vector is constructed. Based on this multi-dimensional feature vector, the gas type is classified and identified. However, because multiple sensors are required and they operate simultaneously, this approach is costly and consumes a lot of power.
[0003] Therefore, the problem of reducing cost and power consumption while achieving accurate gas detection urgently needs to be solved. Summary of the Invention
[0004] This application provides an adaptive gas detection method, electronic device, and storage medium based on dynamic feedback of initial response. It can dynamically adjust the heating time and sampling frequency based on ambient temperature to effectively compensate for the impact of ambient temperature changes on the sensitivity and response speed of semiconductor gas sensors, ensuring that the semiconductor gas sensors operate in optimal condition. Furthermore, it can generate an initial response curve based on an optimized sampling strategy and extract signal feature parameters, including signal strength. Based on these signal feature parameters, it determines the final target environmental scenario type. This not only accurately identifies clear scenarios such as clean and high-risk environments but also effectively distinguishes between single known gases, unknown or mixed gases, and ambiguous boundary conditions. Thus, it can achieve accurate gas detection while reducing cost and power consumption.
[0005] In a first aspect, embodiments of this application provide an adaptive gas detection method based on dynamic feedback of initial response, applied to an adaptive gas detection system, the adaptive gas detection system comprising: a semiconductor gas sensor, a heating module, and a temperature sensor; the method comprising: The ambient temperature is obtained through the temperature sensor. The first preset duration and the first specified sampling frequency are determined based on the ambient temperature. The heating module heats the first preset duration; Within the first preset time period, sampling is performed according to the first specified sampling frequency to obtain the concentration values of multiple gases, and an initial response curve is generated based on the concentration values of the multiple gases. The horizontal axis of the initial response curve is time, and the vertical axis is the gas concentration value. The signal characteristic parameters are determined based on the initial response curve; the signal characteristic parameters include signal strength. The target environmental scenario type is determined based on the signal characteristic parameters. The target environmental scenario type includes any of the following: clean environment type, high-concentration hazardous type, and medium-concentration hazardous type. The medium-concentration hazardous type includes any of the following: single known gas type, specified gas type, and fuzzy boundary gas type. The specified gas type includes any of the following: unknown gas type and mixed gas type.
[0006] Secondly, embodiments of this application provide an electronic device, which includes a processor, a memory, and one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the processor, and the programs include instructions for performing the steps in the first aspect of embodiments of this application.
[0007] Thirdly, embodiments of this application provide a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program for electronic data interchange, wherein the computer program causes a computer to perform some or all of the steps described in the first aspect of embodiments of this application.
[0008] Fourthly, embodiments of this application provide a computer program product, wherein the computer program product includes a non-transitory computer-readable storage medium storing a computer program, the computer program being operable to cause a computer to perform some or all of the steps described in the first aspect of embodiments of this application. The computer program product may be a software installation package.
[0009] Implementing the embodiments of this application has the following beneficial effects: As can be seen, the adaptive gas detection method, electronic device, and storage medium based on initial response dynamic feedback described in this application embodiment can, firstly, acquire the ambient temperature in real time through a temperature sensor and dynamically adjust the heating time and sampling frequency based on the ambient temperature. This effectively compensates for the impact of ambient temperature changes on the sensitivity and response speed of the semiconductor gas sensor, ensuring that the semiconductor gas sensor operates in its optimal working state, thereby improving the accuracy and consistency of data acquisition. Secondly, an initial response curve can be generated based on the optimized sampling strategy, and signal feature parameters, including signal strength, can be extracted. This allows for the comprehensive capture of dynamic changes in the gas adsorption and reaction process, providing a rich and high signal-to-noise ratio data foundation for subsequent gas classification. Finally, the final target environmental scenario type can be determined based on the signal feature parameters. This not only accurately identifies clear scenarios such as clean and high-risk environments but also effectively distinguishes between single known gases, unknown or mixed gases, and ambiguous boundary situations. This significantly enhances the system's adaptability, anti-interference ability, and gas identification specificity in complex and variable environments, while reducing false alarm and false negative rates.
[0010] In addition, since semiconductor gas sensors can include a single sensor, that is, a single sensor can detect environmental clean types, hazardous high concentration types, single known gas types, unknown gas types, mixed gas types, and fuzzy boundary gas types, thereby reducing costs and power consumption while achieving accurate gas detection. Attached Figure Description
[0011] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.
[0012] Figure 1 This is a schematic diagram of the structure of an adaptive gas detection system based on dynamic feedback of initial response provided in an embodiment of this application; Figure 2 This is another structural schematic diagram of an adaptive gas detection system based on dynamic feedback of initial response provided in an embodiment of this application; Figure 3 This is a schematic flowchart of an adaptive gas detection method based on dynamic feedback of initial response provided in an embodiment of this application; Figure 4 This is another flowchart illustrating an adaptive gas detection method based on dynamic feedback of initial response provided in this application embodiment; Figure 5This is another schematic flowchart of an adaptive gas detection method based on dynamic feedback of initial response provided in an embodiment of this application; Figure 6 This is another schematic flowchart of an adaptive gas detection method based on dynamic feedback of initial response provided in an embodiment of this application; Figure 7 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application. Detailed Implementation
[0013] To enable those skilled in the art to better understand the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present application, and not all embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of the present application.
[0014] The terms "first," "second," etc., in the specification, claims, and accompanying drawings of this application are used to distinguish different objects, not to describe a specific order. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or apparatus that includes a series of steps or units is not limited to the listed steps or units, but may optionally include steps or units not listed, or may optionally include other steps or units inherent to these processes, methods, products, or apparatuses.
[0015] It should be understood that the term "and / or" in this document is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, or B existing alone. Additionally, the character " / " in this document indicates that the preceding and following related objects are in an "or" relationship. In the embodiments of this application, "multiple" refers to two or more.
[0016] In the embodiments of this application, "at least one item" or its similar expression refers to any combination of these items, including any combination of a single item or a plurality of items. "One or more" means one or more, while "multiple" means two or more. For example, "at least one item" of a, b, or c can represent the following seven cases: a, b, c; a and b; a and c; b and c; a, b, and c. Each of a, b, and c can be an element or a set containing one or more elements.
[0017] In this application, the term "connection" refers to various connection methods, such as direct connection or indirect connection, to achieve communication between devices. This application does not impose any limitations on this.
[0018] In this document, the term "embodiment" means that a particular feature, structure, or characteristic described in connection with an embodiment may be included in at least one embodiment of this application. The appearance of this phrase in various places throughout the specification does not necessarily refer to the same embodiment, nor is it a separate or alternative embodiment mutually exclusive with other embodiments. It will be explicitly and implicitly understood by those skilled in the art that the embodiments described herein can be combined with other embodiments.
[0019] The following describes the relevant content, concepts, meanings, technical issues, technical solutions, and beneficial effects involved in the embodiments of this application.
[0020] First, let me explain some of the technical terms used in this application: Among them, semiconductor gas sensors, also known as metal-oxide-semiconductor (MOS) gas sensors, exhibit different changes in conductivity (or resistance) when in contact with different gases. Analyzing these change patterns based on the data allows for the differentiation of gas types.
[0021] Please see Figure 1 , Figure 1 This is a schematic diagram of the structure of an adaptive gas detection system based on dynamic feedback of initial response provided in an embodiment of this application, as shown below. Figure 1 As shown, the adaptive gas detection system includes: a semiconductor gas sensor, a heating module, and a temperature sensor, wherein the semiconductor gas sensor, the heating module, and the temperature sensor are communicatively connected.
[0022] Semiconductor gas sensors are used to detect target gases, such as gas type and concentration. Target gases may include at least one of the following: methane, liquefied petroleum gas, ethanol, carbon monoxide, hydrogen sulfide, ammonia, chlorine, carbon dioxide, ozone, etc.
[0023] The heating module is used to implement the heating function. It can be integrated into the semiconductor gas sensor or not. Specifically, heating enhances the activity of the sensitive material in the semiconductor gas sensor, accelerating the chemical adsorption process between the measured gas and the sensitive material. This improves detection sensitivity and response speed. Simultaneously, the high temperature can ablate contaminants (such as oil, dust, etc.) adhering to the surface of the semiconductor gas sensor, keeping the surface clean and preventing sensitivity loss or false alarms due to contamination.
[0024] Among them, the temperature sensor can detect the ambient temperature.
[0025] Among them, such as Figure 2 As shown, the adaptive gas detection system may also include: a control module (e.g., a microcontroller (MCU)), a heating control circuit, a signal acquisition circuit, and a power management module, etc. The control module is essentially the "brain" of the adaptive gas detection system, responsible for issuing commands and processing corresponding algorithms. The heating control circuit controls the heating parameters of the heating module, which may include at least one of the following: heating duration, heating mode, heating power, heating current, heating voltage, etc. The signal acquisition circuit acquires gas signals. The power management module supplies power to all modules within the adaptive gas detection system.
[0026] Please see Figure 3 , Figure 3 This is a flowchart illustrating an adaptive gas detection method based on dynamic feedback of initial response provided in an embodiment of this application, as shown below. Figure 1 As shown, this is applied to an adaptive gas detection system, which includes a semiconductor gas sensor, a heating module, and a temperature sensor. The adaptive gas detection method based on initial response dynamic feedback includes: S301: Obtain the ambient temperature through the temperature sensor.
[0027] The adaptive gas detection system may include a semiconductor gas sensor, a heating module, and a temperature sensor. The temperature sensor can acquire the ambient temperature at preset time intervals, which can be pre-set or be the system default.
[0028] S302: Determine the first preset duration and the first specified sampling frequency based on the ambient temperature.
[0029] In practical implementation, different temperatures can be set with different heating durations and sampling frequencies. Specifically, a pre-stored mapping relationship between preset temperatures and heating durations can be used to determine a first preset duration corresponding to the ambient temperature. Similarly, a pre-stored mapping relationship between preset temperatures and sampling frequencies can be used to determine a first specified sampling frequency corresponding to the ambient temperature. This ensures that the heating and sampling effects are appropriate for the ambient temperature, which helps improve detection sensitivity and response speed.
[0030] Heating can enhance the activity of the sensitive material in a semiconductor gas sensor, accelerating the chemical adsorption process between the gas being measured and the sensitive material. This improves the sensitivity and response speed of the detection. At the same time, high temperatures can also ablate contaminants (such as oil and dust) adhering to the surface of the semiconductor gas sensor, keeping the surface clean and preventing decreased sensitivity or false alarms due to contamination.
[0031] Both the first preset duration and the first specified sampling frequency can be preset or set by system default. For example, the first preset duration is 1.5 seconds.
[0032] S303: The first preset duration is heated by the heating module.
[0033] In practice, the heating module heats for a first preset duration based on preset operating parameters. These preset operating parameters may include at least one of the following: heating mode, heating power, heating current, heating voltage, etc. The preset operating parameters can be pre-set or set by system default.
[0034] S304: Within the first preset time period, sampling is performed according to the first specified sampling frequency to obtain the concentration values of multiple gases, and an initial response curve is generated based on the concentration values of the multiple gases, wherein the horizontal axis of the initial response curve is time and the vertical axis is the gas concentration value.
[0035] In specific implementation, within the first preset time period, sampling can be performed according to the first specified sampling frequency to obtain the concentration values of multiple gases. Then, the initial response curve is obtained by fitting the concentration values of multiple gases. The concentration value of each gas can correspond to a sampling time. The concentration values of multiple gases and their corresponding sampling times can be regarded as multiple coordinate points. The initial response curve is obtained by fitting the multiple coordinate points. The horizontal axis of the initial response curve is time, and the vertical axis is the gas concentration value.
[0036] S305: Determine signal characteristic parameters based on the initial response curve; the signal characteristic parameters include signal strength.
[0037] The signal characteristic parameters may include signal strength. The area of the initial response curve within a first preset time period can be obtained, and then the theoretical maximum area corresponding to the full-scale output of the sensor can be obtained. The ratio between the two can be calculated to obtain the signal strength.
[0038] For example, taking a preset duration of 1.5 seconds, the normalized area S of the initial response curve within the 0-1.5 second range can be calculated. Specifically, S = (area under the measured initial response curve) / (theoretical maximum area corresponding to the sensor's full-scale output). The S value (expressed as a percentage) directly and roughly reflects the concentration level of the target gas in the environment. A larger S value indicates a higher gas concentration. The target gas can be understood as the gas that the semiconductor gas sensor can detect.
[0039] S306: Determine the target environmental scenario type based on the signal characteristic parameters. The target environmental scenario type includes any one of the following: clean environment type, high-concentration hazardous type, medium-concentration hazardous type; the medium-concentration hazardous type includes any one of the following: single known gas type, specified gas type, fuzzy boundary gas type; the specified gas type includes any one of the following: unknown gas type, mixed gas type.
[0040] The target environmental scenario type includes any of the following: clean environment, high-concentration hazardous environment, and medium-concentration hazardous environment. A target environmental scenario type can be understood as having no effective target gas. A high-concentration hazardous environment can be understood as having a high concentration of the target gas, allowing for hazard warnings. A medium-concentration hazardous environment can be understood as having a moderate concentration of the target gas.
[0041] The concentration type in hazardous situations can include any of the following: single known gas type, specified gas type, and fuzzy boundary gas type. A single known gas type can be understood as having only one target gas, while a fuzzy boundary gas type can be understood as where it is impossible to accurately determine whether it is one or multiple target gases. A specified gas type can include any of the following: unknown gas type and mixed gas type. An unknown gas type can be understood as a gas type that semiconductor gas sensors cannot detect. A mixed gas type can be understood as a gas type where multiple gases are mixed together.
[0042] In this context, "fuzzy boundary gas type" can be understood as an uncertain gas type, meaning it might resemble a known gas type, but is not actually that known gas type. "Unknown / mixed gas type" can be understood as a gas type whose type is explicitly unknown.
[0043] For example, when cooking in the kitchen, someone else is mopping the floor with disinfectant, and the food gets burnt (producing smoke). This smoke mixes with the disinfectant gas. At this time, the signal received by the semiconductor gas sensor indicates that the mixed gas is a gas type that is clearly not in the known gas library, and it belongs to the unknown / mixed gas type.
[0044] To illustrate further, for the fuzzy boundary type, for example, in winter, when the temperature is very low, if you open a bottle of wine in the kitchen, the amount of ethanol that evaporates is relatively small. However, due to the limitations of the semiconductor gas sensor in the cold winter (low temperature environment), the detection capability is weakened, and the detected gas is close to ethanol, but the value does not match the value in the database. This case is the fuzzy boundary gas type.
[0045] In practice, further judgments can be made based on signal characteristic parameters to determine the target environmental scenario type.
[0046] As can be seen, the adaptive gas detection method based on initial response dynamic feedback described in this application embodiment firstly acquires the ambient temperature in real time through a temperature sensor and dynamically adjusts the heating time and sampling frequency based on the ambient temperature. This effectively compensates for the impact of ambient temperature changes on the sensitivity and response speed of the semiconductor gas sensor, ensuring that the semiconductor gas sensor operates in its optimal working state, thereby improving the accuracy and consistency of data acquisition. Secondly, an initial response curve can be generated based on the optimized sampling strategy, and signal feature parameters, including signal strength, can be extracted. This allows for the comprehensive capture of dynamic changes in the gas adsorption and reaction process, providing a rich and high signal-to-noise ratio data foundation for subsequent gas classification. Finally, the final target environmental scenario type can be determined based on the signal feature parameters. This not only accurately identifies clear scenarios such as clean and high-risk environments but also effectively distinguishes between single known gases, unknown or mixed gases, and ambiguous boundary situations. This significantly enhances the system's adaptability, anti-interference ability, and gas identification specificity in complex and variable environments, while reducing false alarm and false negative rates.
[0047] In some possible examples, step S306 above, determining the target environmental scenario type based on the signal characteristic parameters, may include the following steps: When the signal strength is less than a first threshold, the environmental cleanliness type is determined as the target environmental scenario type; When the signal strength is greater than the second threshold, the dangerous high concentration type is determined as the target environmental scenario type; the second threshold is greater than the first threshold; When the signal strength is greater than or equal to the first threshold and less than or equal to the second threshold, the concentration type of the hazard is determined as the target environmental scenario type.
[0048] In practice, both the first and second thresholds can be preset or set by system default. The second threshold is greater than the first threshold. Signal strength can be expressed as a percentage; for example, the first threshold could be 2% and the second threshold could be 60%.
[0049] Specifically, when the signal strength is less than the first threshold, it indicates that the target gas signal strength is weak, and it can be determined that there is no effective target gas, that is, the clean environment type can be identified as the target environmental scenario type; when the signal strength is greater than the second threshold, it indicates that the target gas signal is strong, and the high-concentration hazardous type can be identified as the target environmental scenario type; when the signal strength is greater than or equal to the first threshold and less than or equal to the second threshold, it indicates that the target gas signal strength is moderate, and the medium-concentration hazardous type can be identified as the target environmental scenario type.
[0050] In this example, firstly, when the detected signal strength is less than the first threshold, it indicates that the concentration of the target gas in the environment is at a low level, insufficient to pose a potential risk, and the environmental scenario can be classified as "clean environment type". Then, when the signal strength is greater than the second threshold, it indicates that the concentration of the target gas has significantly increased, reaching a level that may pose a serious threat to health or safety; at this point, the environmental scenario can be classified as "high-risk concentration type". Secondly, when the signal strength is between the first and second thresholds, the concentration of the target gas is at a moderate level. Although it has not reached a high-risk level, there is still a potential risk, requiring attention, and it can be classified as "medium-risk concentration type". This three-level classification enables accurate, hierarchical identification and categorized response to environmental scenarios based on continuous changes in signal strength, contributing to improved environmental safety.
[0051] In some possible examples, the signal characteristic parameters further include a first feature set; the method further includes: The first feature set is matched with each initial response feature set in the preset initial response feature library to obtain k first matching values; the preset initial response feature library includes k initial response feature sets, each initial response feature set corresponding to a known gas type; k is an integer greater than 1; Select the maximum and second largest values from the k first matching values; The first confidence level is determined based on the maximum value and the second largest value; The concentration type of the hazard is determined based on the first confidence level.
[0052] The signal characteristic parameters also include a first feature set, which may include at least one feature. For example, the feature may include: the average slope within a specified time period, the instantaneous value at a specified time, the envelope shape feature, the peak feature, the gradient feature, the valley feature, etc. Taking a first preset duration of 1.5 seconds as an example, the feature may include the average slope from 0 to 1 second and the instantaneous value at 1.5 seconds. The specified time period and the specified time can be preset or defaulted by the system.
[0053] The adaptive gas detection system may also include a memory, and a preset initial response feature library may be stored in the memory in advance. The preset initial response feature library may include k sets of initial response feature sets, each set of initial response feature sets corresponding to a gas type, where k is an integer greater than 1.
[0054] In practice, the first feature set can be matched with each set of initial response features in the preset initial response feature library to obtain k first matching values. Then, the maximum and second largest values among the k first matching values can be selected, and the first confidence level can be determined based on the maximum and second largest values. For example, the first confidence level = the maximum value - the second largest value. Then, based on the first confidence level, one of the following gas types can be determined: a single known gas type, a specified gas type, or a fuzzy boundary gas type.
[0055] Each initial response feature set stored in the preset initial response feature library can be used to characterize the mathematical feature vector of the sensor's initial response curve corresponding to a known gas type under standard conditions. This feature vector can consist of a set of multiple feature values extracted from the initial response curve. For example, the features may include, but are not limited to: the average slope within a specified time window, the instantaneous concentration value at a specified sampling point or time, the rising edge time constant of the response curve, the falling edge feature, or a set of multiple feature points after normalization. During the matching process, the similarity or distance metric between the currently extracted first feature set and the feature set of the i-th known gas in the preset initial response feature library is calculated to quantify the closeness between the two, thereby obtaining a matching score, which is used as the first matching value. Specific matching algorithms may include: similarity metric methods, distance metric methods, pattern recognition model methods, etc.
[0056] For example, a lightweight feature set (e.g., the average slope from 0-1 seconds, the instantaneous value at 1.5 seconds) can be extracted from the initial response curve at 1.5 seconds and quickly matched with the "initial response feature set in the preset initial response feature library for various gases" stored in the MCU. For instance, assuming the similarity score for the best matching type is M_max and the score for the second best matching type is M_second, a confidence discriminant value can be defined as: C_s = M_max - M_second. A larger C_s value indicates a closer match between the current initial response curve and the features of a known gas (the gas corresponding to M_max), suggesting a more specific and singular gas type. A smaller C_s value indicates a more ambiguous curve feature, potentially representing an unknown gas or a mixture of gases.
[0057] In this example, firstly, by matching the first feature set with k sets of initial response features corresponding to different gas types in the preset initial response feature library one by one, the similarity between the signal to be tested and each known gas type can be comprehensively quantified, thereby obtaining k first matching values that reflect the quality of the matching. Then, the maximum and second largest values can be selected as key criteria, which can effectively focus on the most likely target gas type and its main interference items or second-best options, eliminate noise interference from other low-correlation gases, and simplify the subsequent calculation complexity. Secondly, the first confidence level can be determined based on the relative relationship between the maximum and second largest values, which can dynamically evaluate the reliability of the classification results, especially providing a more robust judgment basis when distinguishing gases with similar properties. Finally, the precise and specific hazardous concentration type can be accurately determined based on the first confidence level. In this way, the accuracy of gas identification, anti-interference ability, and response speed of the entire system can be significantly improved.
[0058] In some possible examples, the above steps, determining the concentration type of the hazard based on the first confidence level, include: When the first confidence level is greater than the first set value, the single known gas type is determined as the concentration type in the hazard. When the first confidence level is less than the second set value, the specified gas type is determined to be the hazardous concentration type; the second set value is less than the first set value; When the first confidence level is greater than or equal to the second set value and less than or equal to the first set value, the fuzzy boundary gas type is determined as the concentration type in the danger zone.
[0059] Both the first and second settings can be preset or set by system default, with the second setting being less than the first setting. For example, the first setting is 0.5 and the second setting is 0.2.
[0060] In this example, firstly, a higher first setpoint can be set as the upper limit of confidence, and the result will only be judged as a single known gas type when the confidence is extremely high. This ensures the accuracy and reliability of the classification results in high-determinism scenarios and effectively avoids false alarms. Secondly, a lower second setpoint can be used as the lower limit of confidence. When the first confidence is lower than this threshold, it points to a specific gas type. This can promptly capture potential risks or specific abnormal states under low matching degree and prevent missed detections due to weak signals or interference. Then, for the fuzzy boundary region where the confidence is between the second setpoint and the first setpoint, it is classified as a fuzzy boundary gas type. This can properly handle uncertainties such as feature overlap or insufficient discrimination and avoid erroneous decisions caused by forced classification. In this way, a hierarchical and highly robust gas identification mechanism can be built, thereby significantly improving the system's adaptability and judgment accuracy in complex environments.
[0061] In some possible examples, such as Figure 4 As shown, when the concentration type in the hazard includes the single known gas type, the following steps may also be included: A1. Obtain the target gas type corresponding to the maximum value; A2. Determine the second preset duration corresponding to the target gas type; A3. Activate the target concentration quantitative mode corresponding to the target gas type; A4. Heat for the second preset duration using the heating module; A5. Within the second preset time period, determine the target concentration value corresponding to the target gas type based on the target concentration quantitative mode.
[0062] Since each matching value corresponds to a gas type, when the concentration type in a hazardous situation includes a single known gas type, the target gas type corresponding to the maximum value can be obtained. Furthermore, a pre-stored mapping relationship between preset gas types and heating times can be used to determine a second preset time corresponding to the target gas type. This second preset time can be pre-set or set by system default.
[0063] Next, a pre-stored mapping relationship between preset gas types and concentration quantitative modes can be stored. Then, based on this mapping relationship, the target concentration quantitative mode corresponding to the target gas type can be determined and the target concentration quantitative mode can be started. Then, the heating module heats for a second preset time. Within the second preset time, the target concentration value corresponding to the target gas type can be determined based on the target concentration quantitative mode.
[0064] In this example, firstly, by obtaining the target gas type with the highest value (i.e., the highest matching degree) and determining its corresponding second preset duration, adaptive temperature control based on the physicochemical properties of a specific gas can be achieved, thereby optimizing the response conditions of the semiconductor gas sensor and significantly improving the selectivity and sensitivity of detection. Then, a target concentration quantitative mode for the target gas can be activated and precise heating can be performed, which can eliminate cross-interference from non-target gases and ensure that the semiconductor gas sensor operates in the optimal linear range, laying the foundation for high-precision quantitative analysis. Secondly, the target concentration value can be determined based on the optimized heating conditions and the dedicated quantitative mode, which can effectively overcome the measurement error caused by temperature mismatch in the general detection mode, greatly improve the accuracy and repeatability of gas concentration measurement, and achieve a seamless connection from qualitative identification of gas type to precise quantification.
[0065] In some possible examples, such as Figure 5 As shown, when the concentration type in the hazard includes the specified gas type, the following steps may also be included: B1. Activate the refined qualitative mode and determine the third preset duration; B2. Within the third preset time period, sampling is performed according to the first specified sampling frequency to obtain a first sampling result, and a first response curve is generated based on the first sampling result. The horizontal axis of the first response curve is time, and the vertical axis is the gas concentration value. B3. Determine the second feature set based on the first response curve; B4. Match the second feature set with each initial response feature set in the preset initial response feature library to obtain k second matching values; B5. Determine the second confidence level based on the k second matching values; B6. Determine the concentration type of the hazard based on the second confidence level.
[0066] The third preset duration can be set in advance or set by the system default. For example, the third preset duration can be 5 seconds.
[0067] In practice, when the concentration type in the hazardous area includes a specified gas type, it indicates that it is an unknown gas type or a mixed gas type. This means that the judgment result is ambiguous, and it may be a new gas or a mixture. In this case, the fine qualitative mode can be activated, and a third preset duration can be determined. For example, the fine qualitative mode can be activated, and the sample can be heated for 5 seconds to perform full feature analysis and identification or mixture identification.
[0068] Specifically, sampling can be performed at a first specified sampling frequency within a third preset time period to obtain a first sampling result (such as multiple gas concentration values). Then, a first response curve is generated based on the first sampling result, that is, the first response curve is obtained by fitting the multiple gas concentration values at this time. The horizontal axis of the first response curve is time, and the vertical axis is the gas concentration value.
[0069] Next, a second feature set can be determined based on the first response curve. The second feature set may include at least one feature. For example, the feature may include the average slope within a set time period, the instantaneous value at a set time, etc. Taking a third preset duration of 5 seconds as an example, the feature may include the average slope from 0 to 3 seconds and the instantaneous value at 4 seconds. The set time period and the set time can be preset or defaulted to by the system.
[0070] Next, the second feature set can be matched with each initial response feature set in the preset initial response feature library to obtain k second matching values. Correspondingly, the second confidence level is determined based on the k second matching values. For example, the maximum and second largest values among the k second matching values are obtained, and the second confidence level is calculated based on the maximum and second largest values. Second confidence level = maximum value - second largest value. Further, the concentration type in the hazard can be determined based on the second confidence level. For example, the second confidence level can be compared with the first set value and the second set value, and the concentration type in the hazard can be determined based on the comparison result as one of the following: single known gas type, specified gas type, or fuzzy boundary gas type.
[0071] In this example, firstly, a high-frequency sampling can be performed by activating the fine qualitative mode and setting a third preset duration to obtain the first response curve, thereby capturing more subtle dynamic changes in the gas reaction process and providing a high-quality data foundation for subsequent analysis. Then, a second feature set can be extracted based on the first response curve and matched with a preset initial response feature library to calculate the second confidence level. This allows for in-depth analysis of gas types using richer feature dimensions, effectively distinguishing between single known gases, unknown gases, or mixed gases with similar properties, significantly improving the granularity and accuracy of identification. Secondly, the specific subclass of the concentration type in the hazard can be accurately determined based on the second confidence level, achieving a leap from coarse classification to fine qualitative analysis, enhancing the system's ability to identify specific risk gases in complex interference environments and reducing the false judgment rate.
[0072] In some possible examples, such as Figure 6 As shown, when the concentration type in the hazard includes the fuzzy boundary gas type, the following steps may also be included: C1. Start the enhanced scanning mode and determine the fourth preset duration and the second specified sampling frequency; C2. Within the fourth preset time period, sampling is performed according to the second specified sampling frequency to obtain a second sampling result, and a second response curve is generated based on the second sampling result. The horizontal axis of the second response curve is time, and the vertical axis is the gas concentration value. C3. Determine the third feature set based on the second response curve; C4. Match the third feature set with each initial response feature set in the preset initial response feature library to obtain k third matching values; C5. Determine the third confidence level based on the k third matching values; C6. Determine the concentration type of the hazard based on the third confidence level.
[0073] The fourth preset duration can be preset or set by the system default. The second specified sampling frequency can also be preset or set by the system default, and the second specified sampling frequency can be greater than the first specified sampling frequency.
[0074] In practical implementation, when the concentration type in the hazardous area includes gas types with ambiguous boundaries, an enhanced scanning mode can be activated to determine a fourth preset duration and a second specified sampling frequency. Specifically, the difference between a first set value and a second set value can be determined to obtain a second difference; the difference between a first confidence level and a second set value can be determined to obtain a third difference; and the ratio between the third difference and the second difference can be determined to obtain a target ratio. Furthermore, a preset mapping relationship between the ratio and the heating duration can be pre-stored, and the fourth preset duration corresponding to the target ratio can be determined based on this mapping relationship. Correspondingly, a preset mapping relationship between the ratio and the sampling frequency can be pre-stored. The mapping relationship is used to determine the second specified sampling frequency corresponding to the target ratio. In this way, the target ratio can be dynamically calculated and mapped to the optimal detection parameters (fourth preset duration and second specified sampling frequency), which enables adaptive optimization of the detection mode. Within the fourth preset duration, sampling is performed according to the second specified sampling frequency to obtain the second sampling result (i.e., the concentration values of multiple gases). The second response curve is generated based on the second sampling result. Specifically, the second response curve can be obtained by fitting the concentration values of the multiple gases. The horizontal axis of the second response curve is time, and the vertical axis is the gas concentration value.
[0075] Next, a third feature set can be determined based on the second response curve. The third feature set can include at least one feature. For example, the feature can include the average slope within a preset time period, the instantaneous value at a preset time, etc. Taking a fourth preset duration of 6 seconds as an example, the feature can include the average slope from 0 to 4 seconds and the instantaneous value at 5 seconds. The preset time period and preset time can be preset or defaulted to by the system.
[0076] Next, the third feature set can be matched with each initial response feature set in the preset initial response feature library to obtain k third matching values. Correspondingly, the third confidence level is determined based on the k third matching values. For example, the maximum and second largest values among the k third matching values are obtained, and the third confidence level is calculated based on the maximum and second largest values. The third confidence level = maximum value - second largest value. Furthermore, the concentration type in the hazard can be determined based on the third confidence level. For example, the second confidence level can be compared with the first set value and the second set value. Based on the comparison result, the concentration type in the hazard can be determined to be one of the following: a single known gas type, a specified gas type, or a fuzzy boundary gas type.
[0077] In this example, firstly, by activating the enhanced scanning mode, the fourth preset duration and the second specified sampling frequency can be dynamically determined. This allows for optimization of the data acquisition strategy based on the current environmental conditions, resulting in higher density or more suitable second sampling results. Consequently, a second response curve with richer details and a higher signal-to-noise ratio is generated, providing a high-quality data foundation for feature extraction. Then, a third feature set can be extracted based on this response curve and deeply matched with a preset initial response feature library to calculate the third confidence level. By utilizing the subtle dynamic differences captured in the enhanced scanning mode, the problem of identifying similar gases or weak signals that are difficult to distinguish in the conventional mode can be further effectively solved, significantly improving the discriminativeness and robustness of feature matching. Secondly, based on the high-precision third confidence level, the concentration type in the hazard is finally determined, achieving a refined qualitative judgment of complex gas components and significantly reducing the false alarm rate and false negative rate in critical states or interference environments.
[0078] In some possible examples, the adaptive gas detection system further includes a control module and may also include the following steps: When the target environment scenario type includes the environmental cleaning type, the heating module is shut off. Determine the difference between the first threshold and the signal strength to obtain the first difference; The first sleep parameter is determined based on the first difference; The control module is controlled to enter sleep mode based on the first sleep parameter.
[0079] The first sleep parameter may include at least one of the following: sleep duration, wake-up sensitivity, etc., which are not limited here.
[0080] In specific implementation, when the target environment scenario type includes an environmental cleanliness type, the heating module can be shut off. Then, the difference between the first threshold and the signal strength is determined to obtain the first difference, for example, first threshold - signal strength = first difference. A preset mapping relationship between the difference and sleep parameters can also be stored in advance. Based on this mapping relationship, the first sleep parameter corresponding to the first difference is determined, and then the control module is controlled to enter sleep mode according to the first sleep parameter.
[0081] In this example, firstly, when the target environment scenario type is clean, the power supply to the heating module can be cut off, which immediately stops unnecessary energy consumption and avoids power consumption caused by continuous heating in an environment without gas interference. Then, the cleanliness of the current environment is quantified by calculating the difference between the first threshold and the signal strength (i.e., the first difference). The larger the difference, the less pollution there is. Secondly, the first sleep parameter is dynamically determined based on the first difference, and the control module is controlled to enter the corresponding sleep mode using the first sleep parameter. This allows the system to maintain a fast response capability while minimizing standby power consumption (e.g., reducing it to the microampere level), greatly extending battery life.
[0082] In this embodiment, by introducing an intelligent decision-making loop based on quantitative data, the single-function but efficient detection method is transformed into an intelligent detection system capable of coping with the complexities of the real world. It solves the three major pain points of pattern misuse, energy waste, and rigid response, and achieves a synergistic improvement in reliability, energy efficiency, and practicality. It completes the leap from "detection tool" to "detection solution" and makes detection more reliable, energy-saving, and more reasonable in response.
[0083] Specifically, the adaptive gas detection system in this application embodiment no longer blindly performs a single detection. Instead, it first uses an ultra-low power consumption rapid scan to perceive the environment, and then intelligently determines the optimal accurate detection strategy. Combining the two key quantitative indicators of confidence and signal strength, it automatically determines whether the current environment belongs to different scenarios such as "no target gas", "single known low concentration gas", "dangerous high concentration gas" or "unknown / mixed gas". It can also adaptively switch to different working modes such as deep sleep, rapid quantitative detection, alarm and fine identification, or fine qualitative identification. While achieving accurate gas detection, it reduces cost and power consumption and improves system intelligence.
[0084] Consistent with the above embodiments, please refer to Figure 7 , Figure 7 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application. As shown in the figure, the electronic device includes a processor, a memory, a communication interface, and one or more programs. The one or more programs are stored in the memory and configured to be executed by the processor. In this embodiment, the electronic device may include an adaptive gas detection system, which includes a semiconductor gas sensor, a heating module, and a temperature sensor. The program includes instructions for performing the following steps: The ambient temperature is obtained through the temperature sensor. The first preset duration and the first specified sampling frequency are determined based on the ambient temperature. The heating module heats the first preset duration; Within the first preset time period, sampling is performed according to the first specified sampling frequency to obtain the concentration values of multiple gases, and an initial response curve is generated based on the concentration values of the multiple gases. The horizontal axis of the initial response curve is time, and the vertical axis is the gas concentration value. The signal characteristic parameters are determined based on the initial response curve; the signal characteristic parameters include signal strength. The target environmental scenario type is determined based on the signal characteristic parameters. The target environmental scenario type includes any of the following: clean environment type, high-concentration hazardous type, and medium-concentration hazardous type. The medium-concentration hazardous type includes any of the following: single known gas type, specified gas type, and fuzzy boundary gas type. The specified gas type includes any of the following: unknown gas type and mixed gas type.
[0085] In some possible examples, in determining the target environmental scenario type based on the signal characteristic parameters, the above procedure includes instructions for performing the following steps: When the signal strength is less than a first threshold, the environmental cleanliness type is determined as the target environmental scenario type; When the signal strength is greater than the second threshold, the dangerous high concentration type is determined as the target environmental scenario type; the second threshold is greater than the first threshold; When the signal strength is greater than or equal to the first threshold and less than or equal to the second threshold, the concentration type of the hazard is determined as the target environmental scenario type.
[0086] In some possible examples, the signal characteristic parameters further include a first feature set; the above procedure also includes instructions for performing the following steps: The first feature set is matched with each initial response feature set in the preset initial response feature library to obtain k first matching values; the preset initial response feature library includes k initial response feature sets, each initial response feature set corresponding to a known gas type; k is an integer greater than 1; Select the maximum and second largest values from the k first matching values; The first confidence level is determined based on the maximum value and the second largest value; The concentration type of the hazard is determined based on the first confidence level.
[0087] In some possible examples, regarding the determination of the concentration type of the hazard based on the first confidence level, the above procedure includes instructions for performing the following steps: When the first confidence level is greater than the first set value, the single known gas type is determined as the concentration type in the hazard. When the first confidence level is less than the second set value, the specified gas type is determined to be the hazardous concentration type; the second set value is less than the first set value; When the first confidence level is greater than or equal to the second set value and less than or equal to the first set value, the fuzzy boundary gas type is determined as the concentration type in the danger zone.
[0088] In some possible examples, where the concentration type of the hazard includes the single known gas type, the above procedure also includes instructions for performing the following steps: Obtain the target gas type corresponding to the maximum value; Determine the second preset duration corresponding to the target gas type; Activate the target concentration quantitative mode corresponding to the target gas type; The heating module heats the second preset duration; Within the second preset time period, the target concentration value corresponding to the target gas type is determined based on the target concentration quantitative mode.
[0089] In some possible examples, when the concentration type in the hazard includes the specified gas type, the above procedure also includes instructions for performing the following steps: Initiate the refined qualitative mode and determine the third preset duration; Within the third preset time period, sampling is performed according to the first specified sampling frequency to obtain a first sampling result, and a first response curve is generated based on the first sampling result. The horizontal axis of the first response curve is time, and the vertical axis is the gas concentration value. Determine the second feature set based on the first response curve; The second feature set is matched with each initial response feature set in the preset initial response feature library to obtain k second matching values; The second confidence level is determined based on the k second matching values; The concentration type of the hazard is determined based on the second confidence level.
[0090] In some possible examples, when the concentration type in the hazard includes the ambiguous boundary gas type, the above procedure also includes instructions for performing the following steps: Activate enhanced scanning mode, determine the fourth preset duration and the second specified sampling frequency; Within the fourth preset time period, sampling is performed according to the second specified sampling frequency to obtain a second sampling result, and a second response curve is generated based on the second sampling result. The horizontal axis of the second response curve is time, and the vertical axis is the gas concentration value. Determine the third feature set based on the second response curve; The third feature set is matched with each initial response feature set in the preset initial response feature library to obtain k third matching values; The third confidence level is determined based on the k third matching values; The concentration type of the hazard is determined based on the third confidence level.
[0091] In some possible examples, the adaptive gas detection system also includes a control module, and the above program further includes instructions for performing the following steps: When the target environment scenario type includes the environmental cleaning type, the heating module is shut off. Determine the difference between the first threshold and the signal strength to obtain the first difference; The first sleep parameter is determined based on the first difference; The control module is controlled to enter sleep mode based on the first sleep parameter.
[0092] This application also provides a computer storage medium storing a computer program for electronic data interchange, which causes a computer to perform some or all of the steps of any of the methods described in the above method embodiments.
[0093] This application also provides a computer program product, which includes a non-transitory computer-readable storage medium storing a computer program operable to cause a computer to perform some or all of the steps of any of the methods described in the above method embodiments. This computer program product can be a software installation package.
[0094] It should be noted that, for the sake of simplicity, the foregoing method embodiments are all described as a series of actions. However, those skilled in the art should understand that this application is not limited to the described order of actions, as some steps may be performed in other orders or simultaneously according to this application. Furthermore, those skilled in the art should also understand that the embodiments described in the specification are preferred embodiments, and the actions and modules involved are not necessarily essential to this application.
[0095] In the above embodiments, the descriptions of each embodiment have different focuses. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions in other embodiments.
[0096] In the several embodiments provided in this application, it should be understood that the disclosed apparatus can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of the units described above is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between devices or units may be electrical or other forms.
[0097] The units described above as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.
[0098] Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.
[0099] If the aforementioned integrated units are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage device (CMD). Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a memory and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned memory includes various media capable of storing program code, such as USB flash drives, read-only memory (ROM), random access memory (RAM), portable hard drives, magnetic disks, or optical disks.
[0100] Those skilled in the art will understand that all or part of the steps in the various methods of the above embodiments can be implemented by a program instructing related hardware. The program can be stored in a computer-readable storage device, which may include: a flash drive, a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk, etc.
[0101] The embodiments of this application have been described in detail above. Specific examples have been used to illustrate the principles and implementation methods of this application. The description of the above embodiments is only for the purpose of helping to understand the method and core ideas of this application. At the same time, for those skilled in the art, there will be changes in the specific implementation methods and application scope based on the ideas of this application. Therefore, the content of this specification should not be construed as a limitation of this application.
Claims
1. An adaptive gas detection method based on dynamic feedback of initial response, characterized in that, An adaptive gas detection system is applied, the adaptive gas detection system comprising: a semiconductor gas sensor, a heating module, and a temperature sensor; the method comprises: The ambient temperature is obtained through the temperature sensor. The first preset duration and the first specified sampling frequency are determined based on the ambient temperature. The heating module heats the first preset duration; Within the first preset time period, sampling is performed according to the first specified sampling frequency to obtain the concentration values of multiple gases, and an initial response curve is generated based on the concentration values of the multiple gases. The horizontal axis of the initial response curve is time, and the vertical axis is the gas concentration value. The signal characteristic parameters are determined based on the initial response curve; the signal characteristic parameters include signal strength. The target environmental scenario type is determined based on the signal characteristic parameters. The target environmental scenario type includes any one of the following: clean environment type, high-concentration hazardous type, and medium-concentration hazardous type. The medium-concentration hazardous type includes any one of the following: single known gas type, specified gas type, and fuzzy boundary gas type. The specified gas type includes any one of the following: unknown gas type and mixed gas type. The step of determining the target environmental scenario type based on the signal feature parameters includes: When the signal strength is less than a first threshold, the environmental cleanliness type is determined as the target environmental scenario type; When the signal strength is greater than the second threshold, the dangerous high concentration type is determined as the target environmental scenario type; the second threshold is greater than the first threshold; When the signal strength is greater than or equal to the first threshold and less than or equal to the second threshold, the concentration type of the hazard is determined as the target environmental scenario type; The signal feature parameters further include a first feature set; the method further includes: The first feature set is matched with each initial response feature set in the preset initial response feature library to obtain k first matching values; the preset initial response feature library includes k initial response feature sets, each initial response feature set corresponding to a known gas type; k is an integer greater than 1; Select the maximum and second largest values from the k first matching values; The first confidence level is determined based on the maximum value and the second largest value; The concentration type of the hazard is determined based on the first confidence level; The step of determining the concentration type of the hazard based on the first confidence level includes: When the first confidence level is greater than the first set value, the single known gas type is determined as the concentration type in the hazard. When the first confidence level is less than the second set value, the specified gas type is determined to be the hazardous concentration type; the second set value is less than the first set value; When the first confidence level is greater than or equal to the second set value and less than or equal to the first set value, the fuzzy boundary gas type is determined as the concentration type in the danger zone.
2. The method according to claim 1, characterized in that, When the concentration type in the hazard includes the single known gas type, the method further includes: Obtain the target gas type corresponding to the maximum value; Determine the second preset duration corresponding to the target gas type; Activate the target concentration quantitative mode corresponding to the target gas type; The heating module heats the second preset duration; Within the second preset time period, the target concentration value corresponding to the target gas type is determined based on the target concentration quantitative mode.
3. The method according to claim 1, characterized in that, When the concentration type in the hazard includes the specified gas type, the method further includes: Initiate the refined qualitative mode and determine the third preset duration; Within the third preset time period, sampling is performed according to the first specified sampling frequency to obtain a first sampling result, and a first response curve is generated based on the first sampling result. The horizontal axis of the first response curve is time, and the vertical axis is the gas concentration value. Determine the second feature set based on the first response curve; The second feature set is matched with each initial response feature set in the preset initial response feature library to obtain k second matching values; The second confidence level is determined based on the k second matching values; The concentration type of the hazard is determined based on the second confidence level.
4. The method according to claim 1, characterized in that, When the concentration type in the hazard includes the fuzzy boundary gas type, the method further includes: Activate enhanced scanning mode, determine the fourth preset duration and the second specified sampling frequency; Within the fourth preset time period, sampling is performed according to the second specified sampling frequency to obtain a second sampling result, and a second response curve is generated based on the second sampling result. The horizontal axis of the second response curve is time, and the vertical axis is the gas concentration value. Determine the third feature set based on the second response curve; The third feature set is matched with each initial response feature set in the preset initial response feature library to obtain k third matching values; The third confidence level is determined based on the k third matching values; The concentration type of the hazard is determined based on the third confidence level.
5. The method according to any one of claims 1-4, characterized in that, The adaptive gas detection system further includes a control module, and the method further includes: When the target environment scenario type includes the environmental cleaning type, the heating module is shut off. Determine the difference between the first threshold and the signal strength to obtain the first difference; The first sleep parameter is determined based on the first difference; The control module is controlled to enter sleep mode based on the first sleep parameter.
6. An electronic device, characterized in that, The electronic device includes a processor and a memory for storing one or more programs and configured to be executed by the processor, the programs including instructions for performing the steps of the method as described in any one of claims 1-5.
7. A computer-readable storage medium, characterized in that, A computer program for storing electronic data interchange is provided, wherein the computer program causes a computer to perform the method as described in any one of claims 1-5.