Environmental protection knowledge searching system and method

Abstract

The invention provides an environmental protection knowledge searching system and method, and relates to the technical field of environmental protection data processing, and the environmental protection knowledge searching system comprises an obtaining unit which is used for obtaining environmental protection strategy updating data in environmental protection knowledge; the analysis unit is used for carrying out data analysis on the environmental protection strategy updating data and evaluating environmental protection parameters related to the environmental protection strategy updating data and the attention degree of each environmental protection parameter; the prediction unit is used for carrying out conservative prediction on the environmental protection parameters according to the attention degree, the environmental protection knowledge data and the enterprise capacity data to obtain parameter optimization thresholds matched with the enterprise capacity requirements; and the output unit is used for taking the parameter optimization threshold value and other parameter threshold values as search results so as to carry out threshold value monitoring management on enterprise environmental protection. The search system and method provided by the invention can respond to and perfect the enterprise environmental protection system in advance before the relevant environmental protection policy is really implemented.

Description

Technical Field

[0001] This invention relates to the field of environmental data processing technology, and in particular to an environmental knowledge search system and method. Background Technology

[0002] Environmental knowledge can be obtained through various platforms and systems, such as a company's own environmental knowledge database, or systematic information, practical experience, and environmental policy information related to natural ecology, pollution prevention and control, resource utilization, and sustainable development published by relevant organizations through online platforms.

[0003] Current environmental knowledge search processes only allow users to view the latest environmental policy announcements to understand the current environmental requirements and indicators. However, this manual search method is prone to missing important information points in environmental policy messages, making it difficult to respond promptly and accurately to environmental policies and optimize internal environmental deployment strategies. This results in a weak internal environmental decision-making system, hindering the rapid response and updating of environmental parameter-related information in current environmental knowledge. Summary of the Invention

[0004] This invention provides an environmental knowledge search system and method to solve the problem that the existing technology, which relies on manual searching, is prone to missing important information points in environmental policy messages, thus failing to respond to environmental policies in a timely and accurate manner to optimize the company's internal environmental deployment strategy. This results in a weak internal environmental decision-making system that cannot quickly respond to and update environmental parameter-related information in the current environmental knowledge.

[0005] To achieve the above and other related objectives, this invention provides an environmental knowledge search system, comprising: an acquisition unit for acquiring updated environmental strategy data from environmental knowledge; a parsing unit for parsing the updated environmental strategy data and evaluating the environmental parameters involved in the updated data and the degree of importance of each environmental parameter; a prediction unit for making conservative predictions of the environmental parameters based on the degree of importance, environmental knowledge data, and the company's own production capacity data, to obtain parameter optimization thresholds that match the company's own production capacity requirements; and an output unit for using the parameter optimization thresholds and other parameter thresholds as search results to monitor and manage the company's environmental protection at thresholds.

[0006] In one embodiment of the present invention, the acquisition unit includes: an address receiving subunit for receiving a preset environmental knowledge system address; and a data monitoring subunit for monitoring environmental knowledge of each corresponding environmental knowledge system according to the environmental knowledge system address, so as to obtain environmental strategy update data in the environmental knowledge.

[0007] In one embodiment of the present invention, the parsing unit includes: a splitting subunit, used to split the environmental protection strategy update data into several split keywords; a conversion subunit, used to convert the data dimensions of different environmental protection parameter dimensions according to the split keywords, to obtain the dimension guidance data corresponding to different environmental protection parameter dimensions; and an analysis subunit, used to perform parameter analysis and selection according to the dimension guidance data, and to evaluate and obtain the environmental protection parameters involved in the environmental protection strategy update data and the degree of importance of each environmental protection parameter.

[0008] In one embodiment of the present invention, the environmental parameter dimensions include known environmental parameter dimensions and newly added environmental parameter dimensions. The dimension guidance data includes first dimension guidance data corresponding to the known environmental parameter dimensions and second dimension guidance data corresponding to the newly added environmental parameter dimensions. The conversion subunit includes: an extraction and detection module, used to extract and detect known environmental parameter dimensions from the split keywords; a first output module, used to, when the first environmental dimension keyword corresponding to the known environmental parameter dimension is extracted, to perform keyword recombination corresponding to the known environmental parameter dimension through an environmental knowledge big data model to obtain first dimension guidance data, wherein the environmental knowledge big data model is constructed through environmental knowledge big data; and a second output module, used to, when the first environmental dimension keyword corresponding to the known environmental parameter dimension is not extracted, to generate a new environmental parameter dimension based on the split keywords, extract keywords from the split keywords based on the new environmental parameter dimension to obtain second environmental dimension keywords, and perform keyword recombination corresponding to the new environmental parameter dimension through an environmental knowledge big data model to obtain second dimension guidance data.

[0009] In one embodiment of the present invention, the second output module includes: a search submodule for searching for parameter sentiment information in the split keywords; an object extraction submodule for extracting the target of the parameter sentiment information from the split keywords when parameter sentiment information is found, to obtain the corresponding target keywords; a comparison calculation submodule for calculating the environmental relevance between the target keywords and environmental knowledge in the environmental knowledge base, and comparing the calculated environmental relevance with a relevance threshold; a dimension generation submodule for converting the target keywords into environmental parameter targets when the environmental relevance is greater than the relevance threshold, to generate new environmental parameters and corresponding new environmental parameter dimensions; and a data recombination submodule for extracting keywords from the split keywords according to the new environmental parameter dimensions, obtaining second environmental dimension keywords associated with the new environmental parameter dimensions, and recombining the second environmental dimension keywords with keywords corresponding to the new environmental parameter dimensions through an environmental knowledge big language model to obtain second dimension guidance data.

[0010] In one embodiment of the present invention, the comparison calculation submodule includes: an adjustment value calculation module, used to find the standard keyword that is semantically closest to the function keyword, calculate the semantic similarity value between the function keyword and the standard keyword based on the standard keyword and the function keyword, and calculate the relevance adjustment value based on the semantic similarity value and the relevance adjustment factor. The semantic similarity value includes a semantic similarity value that approaches the standard keyword and a semantic strengthening value that is located between the standard keyword and the corresponding environmental knowledge in the environmental knowledge base. The relevance adjustment factor includes a relevance reduction factor corresponding to the semantic similarity value and a relevance strengthening factor corresponding to the semantic strengthening value; and a fusion calculation module, used to fuse the baseline relevance of the standard keyword and the corresponding environmental knowledge in the environmental knowledge base and the relevance adjustment value to obtain the environmental relevance. The calculation formula for the environmental relevance is: , Represented as the correlation reduction factor, Represented as the correlation strengthening factor, This indicates the semantic similarity value between the functional keyword and the standard keyword. Indicates the baseline correlation degree.

[0011] In one embodiment of the present invention, the environmental parameter dimension includes a known environmental parameter dimension and a newly added environmental parameter dimension. The dimension guidance data includes a first dimension guidance data corresponding to the known environmental parameter dimension and a second dimension guidance data corresponding to the newly added environmental parameter dimension. The analysis subunit includes: a first analysis module, used to perform semantic sentiment sentence pattern detection of the known environmental parameter corresponding to the known environmental parameter dimension based on the first dimension guidance data when the dimension guidance data is the first dimension guidance data; when there is a first semantic sentiment sentence pattern of the known environmental parameter, extract the first semantic sentiment sentence pattern and compare it with the standard sentiment sentence pattern in the sentiment sentence pattern library to obtain the optimal standard sentiment sentence pattern that is closest to the first semantic sentiment sentence pattern, and the first degree of closeness between the first semantic sentiment sentence pattern and the optimal standard sentiment sentence pattern; when the first degree of closeness is less than the corresponding first degree threshold, the known environmental parameter is taken as the first target environmental parameter, and the first degree of importance corresponding to the first target environmental parameter is obtained based on the first degree of closeness and the first standard degree of importance corresponding to the optimal standard sentiment sentence pattern. The first degree of closeness includes a first positive degree of closeness and a first negative degree of closeness, and the calculation formula of the first degree of importance is: , This indicates the level of importance attached to the primary standard. Indicates the first positive proximity. This represents the positive conversion factor of the first level of importance corresponding to the first level of positive proximity. Indicates the degree of first negative proximity. This section describes a first negative proximity level, representing the first level of importance's negative conversion factor. When no known first semantic sentiment expression for an environmental parameter exists, the baseline importance level corresponding to the known environmental parameter is used as the first level of importance. A second analysis module is used to extract semantic sentiment expressions for new environmental parameters corresponding to the new environmental parameter dimension when the dimension guidance data is the second dimension guidance data. The extracted second semantic sentiment expression is compared with the standard sentiment expression in the sentiment expression library to obtain the optimal standard sentiment expression that is closest to the second semantic sentiment expression, and the second proximity level between the second semantic sentiment expression and the optimal standard sentiment expression. The second proximity level is then fused with the environmental relevance corresponding to the new environmental parameter dimension to obtain the fused proximity level. When the fused proximity level is less than the corresponding second level threshold, the new environmental parameter is used as the second target environmental parameter. Based on the second proximity level and the second standard importance level corresponding to the optimal standard sentiment expression, the second level of importance corresponding to the second target environmental parameter is obtained. The second proximity level includes the second positive proximity level and the second negative proximity level. The calculation formula for the second level of importance is: , This indicates the level of importance attached to the second standard. Indicates the second positive proximity. This represents the positive conversion factor of the second degree of importance corresponding to the second positive proximity. Indicates the degree of proximity to the second negative sign. This represents the negative conversion factor of the second degree of importance corresponding to the second negative proximity. Indicates the degree of environmental relevance. This indicates the correlation setting value. This indicates the maximum impact of the proximity in the formation of environmental relevance. This indicates the degree of fusion proximity corresponding to the second positive proximity level. This indicates the degree of fusion proximity corresponding to the second negative proximity level.

[0012] In one embodiment of the present invention, the prediction unit includes: a benchmark extraction subunit, used to extract external benchmark thresholds corresponding to each environmental protection parameter from environmental knowledge data; a numerical adjustment subunit, used to select optimization coefficients for the external benchmark thresholds according to the degree of importance, and adjust the external benchmark thresholds according to the optimization coefficients to obtain an adjustment threshold; and an optimization processing subunit, used to select the corresponding conservative threshold increment according to the difference between the enterprise's own production capacity data and the set production capacity threshold when the enterprise's own production capacity data is greater than the set production capacity threshold, and adjust the adjustment threshold according to the conservative threshold increment to obtain a parameter optimization threshold that matches the enterprise's own production capacity requirements. The calculation formula for the parameter optimization threshold is expressed as: , This represents the maximum value among the parameter optimization thresholds. This represents the minimum value among the parameter optimization thresholds. This represents the maximum value among the external benchmark thresholds. This represents the minimum value among the external benchmark thresholds. Represents the optimization coefficient. This represents the conservative increment of the threshold.

[0013] In one embodiment of the present invention, the output unit includes: a result selection subunit, used to receive the selection result of the environmental protection parameter corresponding to the parameter optimization threshold as the optimization monitoring parameter; and a result update subunit, used to use the parameter optimization threshold corresponding to the optimization monitoring parameter and other parameter thresholds as search results to update the threshold monitoring management of enterprise environmental protection.

[0014] To achieve the above and other related objectives, the present invention also provides an environmental knowledge search method, comprising: acquiring environmental strategy update data from environmental knowledge through an acquisition unit; parsing the environmental strategy update data through a parsing unit to evaluate and determine the environmental parameters involved in the environmental strategy update data and the degree of importance of each environmental parameter; making conservative predictions of the environmental parameters based on the degree of importance, environmental knowledge data, and the enterprise's own production capacity data through a prediction unit to obtain parameter optimization thresholds that match the enterprise's own production capacity requirements; and outputting the parameter optimization thresholds and other parameter thresholds as search results through an output unit to perform threshold monitoring and management of the enterprise's environmental protection.

[0015] The beneficial effects of this invention: The environmental knowledge search system and method proposed in this invention, through searching environmental strategy update data, enables real-time management of environmental parameters that enterprises need to monitor. This helps enterprises respond promptly to adjustments in environmental strategies and dynamically adjust their monitoring strategies for environmental parameters. Specifically, it first acquires environmental strategy update data published by various environmental knowledge strategy platform systems, and then further analyzes the data content to obtain relevant environmental parameters of concern in the environmental strategy update data, as well as the degree of importance they receive. Based on this degree of importance, it can combine environmental knowledge data and the enterprise's own production capacity data to conservatively predict the threshold values ​​of environmental parameters. The predicted parameter optimization thresholds are then provided to relevant enterprises to achieve parameter optimization thresholds based on relevant environmental parameters, enabling early monitoring and ensuring the flexibility of enterprises' environmental decision-making. This allows for proactive responses to and improvements to the enterprise's environmental system before relevant environmental policies are actually implemented. Attached Figure Description

[0016] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application. It is obvious that the drawings described below are merely some embodiments of this application, and those skilled in the art can obtain other drawings based on these drawings without any inventive effort.

[0017] In the attached diagram: Figure 1 This is a structural block diagram of the environmental knowledge search system provided in an embodiment of the present invention; Figure 2 The diagram shown is a flowchart illustrating an environmental knowledge search method provided in an embodiment of the present invention.

[0018] The attached figures are labeled as follows: Acquisition unit 111; parsing unit 112; prediction unit 113; output unit 114. Detailed Implementation

[0019] The following specific examples illustrate the implementation of the present invention. Those skilled in the art can easily understand other advantages and effects of the present invention from the content disclosed in this specification. The present invention can also be implemented or applied through other different specific embodiments. Various details in this specification can also be modified or changed based on different viewpoints and applications without departing from the spirit of the present invention. In the absence of conflict, the following embodiments and features in the embodiments can be combined with each other.

[0020] It should be noted that the illustrations provided in the following embodiments are only schematic representations of the basic concept of the present invention. The drawings only show the components related to the present invention and are not drawn according to the actual number, shape and size of the components in the actual implementation. In the actual implementation, the form, quantity and proportion of each component can be arbitrarily changed, and the layout of the components may also be more complex.

[0021] In the following description, numerous details are explored to provide a more thorough explanation of embodiments of the invention. However, it will be apparent to those skilled in the art that embodiments of the invention may be practiced without these specific details. In other embodiments, well-known structures and devices are shown in block diagram form rather than in detail to avoid obscuring embodiments of the invention.

[0022] Please see Figure 1This invention provides an environmental knowledge search system, comprising: an acquisition unit 111 for acquiring updated environmental strategy data from environmental knowledge; a parsing unit 112 for parsing the updated environmental strategy data and evaluating the environmental parameters involved in the updated environmental strategy data and the degree of importance of each environmental parameter; a prediction unit 113 for making conservative predictions of the environmental parameters based on the degree of importance, environmental knowledge data, and the company's own production capacity data, to obtain parameter optimization thresholds that match the company's own production capacity requirements; and an output unit 114 for using the parameter optimization thresholds and other parameter thresholds as search results to monitor and manage the company's environmental protection.

[0023] As can be seen from the above, in the process of environmental knowledge monitoring, the environmental knowledge search system of this invention can realize the search of environmental strategy update data, and manage the environmental parameters that enterprises need to monitor in real time. This helps enterprises respond promptly to adjustments in environmental strategies and dynamically adjust their monitoring strategies for environmental parameters. Specifically, the acquisition unit 111 first acquires environmental strategy update data published by various environmental knowledge strategy platform systems. Of course, this environmental strategy update data can also be set by the enterprise itself according to its needs. After acquiring the environmental strategy update data, the parsing unit 112 further parses the data content to obtain the relevant environmental parameters that are of concern in the environmental strategy update data, as well as the degree of importance they receive. Based on the degree of importance, the prediction unit 113, combined with environmental knowledge data and the enterprise's own production capacity data, can conservatively predict the threshold values ​​of the environmental parameters. The predicted parameter optimization threshold is then provided to the relevant enterprises through the output unit 114, realizing parameter optimization thresholds based on relevant environmental parameters for early prediction and monitoring. This ensures the flexibility of the enterprise's environmental decision-making and allows for early response and improvement of the enterprise's environmental system before the relevant environmental policies are actually implemented.

[0024] In the environmental knowledge search system of the present invention, the acquisition unit 111 includes: an address receiving subunit for receiving a preset environmental knowledge system address; and a data monitoring subunit for monitoring the environmental knowledge of each corresponding environmental knowledge system according to the environmental knowledge system address, so as to obtain environmental strategy update data in the environmental knowledge.

[0025] Before the acquisition unit 111 acquires the environmental protection strategy update data, it can manually upload the preset environmental protection knowledge system address to the address receiving subunit. The data monitoring subunit then uses the environmental protection knowledge system address to control the search for environmental protection knowledge that needs to be updated in the corresponding environmental protection knowledge system, thereby obtaining the environmental protection strategy update data that exists in the environmental protection knowledge. Based on the environmental protection strategy update data, the environmental parameters involved in the environmental protection strategy update data and the degree of importance of each environmental parameter can be further evaluated.

[0026] In the environmental knowledge search system of the present invention, the parsing unit 112 includes: a splitting subunit, used to split environmental strategy update data into keywords to obtain several split keywords; a conversion subunit, used to convert data dimensions of different environmental parameters according to the split keywords to obtain dimension guidance data corresponding to different environmental parameter dimensions; and an analysis subunit, used to perform parameter analysis and selection according to the dimension guidance data, and evaluate the environmental parameters involved in the environmental strategy update data and the degree of importance of each environmental parameter.

[0027] When parsing environmental parameters, the parsing unit 112 can first perform keyword splitting on the environmental strategy update data through a splitting subunit to extract multiple split keywords. Then, the transformation subunit uses the split keywords in combination with different environmental parameter dimensions to achieve data dimension transformation, thereby deriving the dimension guidance data corresponding to each environmental parameter dimension. This weakens the other content mixed in with each environmental parameter dimension, ensuring that the data information corresponding to the environmental parameter dimension can be centrally expressed. This facilitates the accuracy of the assessment of the importance of the corresponding environmental parameter dimension, further improving the accuracy of the predicted parameter optimization threshold.

[0028] Preferably, the environmental protection parameter dimensions include known environmental protection parameter dimensions and newly added environmental protection parameter dimensions, and the dimension guidance data includes first dimension guidance data corresponding to the known environmental protection parameter dimensions and second dimension guidance data corresponding to the newly added environmental protection parameter dimensions.

[0029] The transformation subunit may further include: an extraction and detection module for extracting and detecting known environmental parameter dimensions from the split keywords; a first output module for, when a first environmental dimension keyword corresponding to a known environmental parameter dimension is extracted, recombining the first environmental dimension keyword with the corresponding known environmental parameter dimension through an environmental knowledge big data model to obtain first dimension guidance data, wherein the environmental knowledge big data model is constructed through environmental knowledge big data; and a second output module for, when no first environmental dimension keyword corresponding to a known environmental parameter dimension is extracted, generating a new environmental parameter dimension based on the split keywords, extracting keywords from the split keywords based on the new environmental parameter dimension to obtain second environmental dimension keywords, and recombining the second environmental dimension keywords with the corresponding new environmental parameter dimension through an environmental knowledge big data model to obtain second dimension guidance data.

[0030] In the process of transforming and deriving dimensional guidance data based on the decomposed keywords, the transformation subunit uses an extraction and detection module to sequentially extract and detect the decomposed keywords based on pre-set known environmental parameter dimensions. This yields all first decomposed keywords associated with the known environmental parameter dimensions. Based on these first decomposed keywords, second decomposed keywords with strong correlations are then identified. These first and second decomposed keywords are used as the first environmental dimension keywords. After obtaining the first environmental dimension keywords, the first output module further uses an environmental knowledge language model to recombine the first environmental dimension keywords with keywords corresponding to the known environmental parameter dimensions, thereby obtaining new data content—the first dimension guidance data that accurately expresses the known environmental parameter dimensions.

[0031] In addition, when extracting and testing known environmental parameter dimensions from the split keywords, there may be cases where the split keywords do not contain keywords corresponding to the known environmental parameter dimensions. In this case, the second output module can be used to further utilize the split keywords to determine whether a new environmental parameter dimension related to environmental knowledge can be derived. If so, based on the corresponding new environmental parameter dimension, keyword extraction is performed on the split keywords to extract the second environmental dimension keywords that meet the requirements of the new environmental parameter dimension. Finally, the second environmental dimension keywords are recombined with keywords corresponding to the new environmental parameter dimension through the environmental knowledge big language model, thereby realizing the recombination of data to obtain second dimension guidance data that can accurately express the new environmental parameter dimension.

[0032] Next, the second output module may further include: a search submodule for searching for parameter sentiment information on the split keywords; an object extraction submodule for extracting the objects affected by the parameter sentiment information from the split keywords when parameter sentiment information is found, thus obtaining the corresponding affected keywords; a comparison calculation submodule for calculating the environmental relevance between the affected keywords and environmental knowledge in the environmental knowledge base, and comparing the calculated environmental relevance with a relevance threshold; a dimension generation submodule for converting the affected keywords into environmental parameter targets when the environmental relevance is greater than the relevance threshold, generating new environmental parameters and corresponding new environmental parameter dimensions; and a data recombination submodule for extracting keywords from the split keywords based on the new environmental parameter dimensions, obtaining second environmental dimension keywords associated with the new environmental parameter dimensions, and recombining the second environmental dimension keywords with keywords corresponding to the new environmental parameter dimensions through the environmental knowledge big language model, thus obtaining second dimension guidance data.

[0033] When the second output module extracts second environmental protection dimension keywords and generates second-dimensional guidance data based on the split keywords, the search submodule first searches for keywords corresponding to parameter sentiment information in each split keyword. When parameter sentiment information exists in the split keywords, the object extraction submodule extracts the objects affected by the parameter sentiment information, thus obtaining the split keywords directly affected by the parameter sentiment information, which are then used as the function keywords. Based on the function keywords, the comparison calculation submodule calculates the environmental relevance between them and environmental knowledge in the environmental knowledge base. Then, the calculated environmental relevance is compared with a relevance threshold. When the environmental relevance is greater than the relevance threshold, the dimension generation submodule can use the function keyword to transform the environmental parameter label, that is, it can directly perform parameter semantic transformation on the semantics corresponding to the function keyword, thus using the resulting parameter semantics as the new environmental parameter dimension. After obtaining the new environmental parameter dimension, the data recombination submodule further uses the new environmental parameter dimension to extract keywords from the split keywords, thereby obtaining the second environmental protection dimension keywords associated with the new environmental parameter dimension. Furthermore, based on the keywords of the second environmental protection dimension, the keywords corresponding to the newly added environmental protection parameter dimension are recombined through the environmental protection knowledge big language model to obtain the second dimension guidance data corresponding to the newly added environmental protection parameter dimension. This ensures that the analysis of the split keywords is not thorough and data information is not missed because the keyword analysis is only conducted through the known environmental protection parameter dimension.

[0034] The comparison calculation submodule includes: an adjustment value calculation module, used to find the standard keyword that is semantically closest to the functioning keyword; calculating the semantic similarity value between the functioning keyword and the standard keyword based on the standard keyword and the functioning keyword; and calculating the relevance adjustment value based on the semantic similarity value and the relevance adjustment factor. The semantic similarity value includes the semantic similarity value that approaches the standard keyword and the semantic strengthening value that lies between the standard keyword and the corresponding environmental knowledge in the environmental knowledge base. The relevance adjustment factor includes the relevance reduction factor corresponding to the semantic similarity value and the relevance strengthening factor corresponding to the semantic strengthening value; and a fusion calculation module, used to fuse the baseline relevance of the standard keyword and the corresponding environmental knowledge in the environmental knowledge base, and the relevance adjustment value, to obtain the environmental relevance. The formula for calculating the environmental relevance is: , Represented as the correlation reduction factor, Represented as the correlation strengthening factor, This indicates the semantic similarity value between the functional keyword and the standard keyword. This represents the baseline correlation degree. Among them, the correlation degree reduction factor... Correlation enhancement factor All keywords were pre-calibrated manually, and each standard keyword corresponds to a baseline relevance. And the correlation of this benchmark It was also obtained through pre-calibration by humans.

[0035] When calculating the environmental relevance, the comparison calculation submodule first uses the adjustment value calculation module to find the standard keyword that is semantically closest to the function keyword in the preset standard keyword library. Then, it further calculates the semantic similarity value between the function keyword and the standard keyword. When the semantics of the function keyword are far away from both the standard keyword and the corresponding environmental knowledge in the environmental knowledge base, the semantic similarity value is the semantic proximity value. When calculating the environmental relevance, it is necessary to calculate the relevance reduction value based on the relevance reduction factor corresponding to the semantic proximity value and the corresponding semantic proximity value. Then, the fusion calculation module further combines the benchmark relevance corresponding to the standard keyword to perform difference calculation to obtain the environmental relevance corresponding to the semantic proximity value.

[0036] Of course, when the semantics of the active keyword are located between the standard keyword and the corresponding environmental knowledge in the environmental knowledge base, it means that the semantics of the active keyword are close to the corresponding environmental knowledge in the environmental knowledge base. At this time, the relevance adjustment value is the semantic strengthening value. The corresponding relevance strengthening value can be calculated by the semantic strengthening value and its corresponding relevance strengthening factor. Then, the fusion calculation module further combines the benchmark relevance corresponding to the standard keyword and performs superposition calculation to obtain the environmental relevance corresponding to the semantic proximity value.

[0037] The above method allows for dynamic selection of the calculation method for environmental relevance based on the semantic context of the keywords, thus ensuring the accuracy of the calculated environmental relevance.

[0038] Preferably, the environmental protection parameter dimensions include known environmental protection parameter dimensions and newly added environmental protection parameter dimensions, and the dimension guidance data includes first dimension guidance data corresponding to the known environmental protection parameter dimensions and second dimension guidance data corresponding to the newly added environmental protection parameter dimensions.

[0039] Additionally, the analysis subunit includes: a first analysis module, used to perform semantic sentiment sentence pattern detection for known environmental parameters corresponding to known environmental parameter dimensions based on the first dimension guidance data when the dimension guidance data is the first dimension guidance data; when a first semantic sentiment sentence pattern for known environmental parameters exists, the first semantic sentiment sentence pattern is extracted and compared with the standard sentiment sentence pattern in the sentiment sentence pattern library to obtain the optimal standard sentiment sentence pattern that is closest to the first semantic sentiment sentence pattern, and the first degree of closeness between the first semantic sentiment sentence pattern and the optimal standard sentiment sentence pattern; when the first degree of closeness is less than the corresponding first degree threshold, the known environmental parameter is taken as the first target environmental parameter, and the first degree of importance corresponding to the first target environmental parameter is obtained based on the first degree of closeness and the first standard degree of importance corresponding to the optimal standard sentiment sentence pattern. The first degree of closeness includes the first positive degree of closeness and the first negative degree of closeness, and the calculation formula for the first degree of importance is: , This indicates the level of importance attached to the primary standard. Indicates the first positive proximity. This represents the positive conversion factor of the first level of importance corresponding to the first level of positive proximity. Indicates the degree of first negative proximity. This section describes a first negative proximity level, representing the first level of importance's negative conversion factor. When no known first semantic sentiment expression for an environmental parameter exists, the baseline importance level corresponding to the known environmental parameter is used as the first level of importance. A second analysis module is used to extract semantic sentiment expressions for new environmental parameters corresponding to the new environmental parameter dimension when the dimension guidance data is the second dimension guidance data. The extracted second semantic sentiment expression is compared with the standard sentiment expression in the sentiment expression library to obtain the optimal standard sentiment expression that is closest to the second semantic sentiment expression, and the second proximity level between the second semantic sentiment expression and the optimal standard sentiment expression. The second proximity level is then fused with the environmental relevance corresponding to the new environmental parameter dimension to obtain the fused proximity level. When the fused proximity level is less than the corresponding second level threshold, the new environmental parameter is used as the second target environmental parameter. Based on the second proximity level and the second standard importance level corresponding to the optimal standard sentiment expression, the second level of importance corresponding to the second target environmental parameter is obtained. The second proximity level includes the second positive proximity level and the second negative proximity level. The calculation formula for the second level of importance is: , This indicates the level of importance attached to the second standard. Indicates the second positive proximity. This represents the positive conversion factor of the second degree of importance corresponding to the second positive proximity. Indicates the degree of proximity to the second negative sign. This represents the negative conversion factor of the second degree of importance corresponding to the second negative proximity. Indicates the degree of environmental relevance. This indicates the correlation setting value. This indicates the maximum impact of the proximity in the formation of environmental relevance. This indicates the degree of fusion proximity corresponding to the second positive proximity level. This indicates the degree of fusion proximity corresponding to the second negative proximity level.

[0040] When performing dimensional guidance data analysis through the analysis sub-unit, the analysis is mainly conducted through the first analysis module analyzing known environmental parameters of the known environmental parameter dimension and the second analysis module analyzing newly added environmental parameters of the newly added environmental parameter dimension, to calculate the corresponding first and second levels of importance. Specifically, when the first analysis module determines the dimensional guidance data as the first dimensional guidance data, it can detect the semantic sentiment patterns of the known environmental parameters corresponding to the known environmental parameter dimension through the recombined first dimensional guidance data, thereby determining whether the first dimensional guidance data contains semantic sentiment patterns. When a first semantic sentiment pattern for a known environmental parameter exists, the corresponding first semantic sentiment pattern is compared with the standard sentiment patterns in the sentiment pattern library to find the optimal standard sentiment pattern that is closest to the first semantic sentiment pattern, and the first degree of proximity from the first semantic sentiment pattern to the optimal standard sentiment pattern is obtained. If the first degree of proximity is less than the corresponding first degree threshold, it means that the optimal standard sentiment pattern can be used for further processing; otherwise, if the first degree of proximity is greater than the corresponding first degree threshold, it can be manually reviewed and calibrated. Once the first degree of proximity is determined, this known environmental parameter can be further used as the first target environmental parameter to calculate the first level of importance corresponding to the first target environmental parameter. Because when the first semantic sentiment sentence is close to the optimal standard sentiment sentence, it is divided into positive proximity and negative proximity. Positive proximity requires strengthening the semantics to approach the optimal standard sentiment sentence, while negative proximity requires reducing the semantics to approach the optimal standard sentiment sentence. Therefore, the first degree of proximity can be divided into a first positive proximity degree and a first negative proximity degree. When the first degree of proximity is a first positive proximity degree, the calculation of the first level of importance can be based on the first positive proximity degree. Positive conversion factor with the degree of importance The corresponding level of importance is calculated, and then combined with the level of importance of the first standard. The first priority level was calculated. When the first degree of proximity is the first negative degree of proximity, the calculation of the first degree of importance can be done by considering the first negative degree of proximity. Negative conversion factor of primary importance The corresponding level of importance is calculated, and then combined with the level of importance of the first standard. The first priority level was calculated. Therefore, by utilizing the first positive proximity degree and the first negative proximity degree to determine the importance of the first standard. Degree compensation is performed to ensure the accuracy of the importance calculation. The first standard importance level is pre-calibrated manually, and each first standard importance level corresponds to an optimal standard sentiment phrase. The positive and negative conversion factors of the first importance level are also obtained manually based on the positive and negative conversions of the importance level values.

[0041] In addition, when there is no known environmental parameter first semantic sentiment sentence in the first dimension guidance data, the first analysis module can directly take the benchmark importance corresponding to the known environmental parameter as the first importance. This benchmark importance can be obtained by manually calibrating each known environmental parameter in advance.

[0042] When the second analysis module determines the dimension guidance data as the second dimension guidance data, since the second dimension guidance data is derived from the predetermined parameter sentiment information before generation, there is no need to perform semantic sentiment sentence pattern detection on the second dimension guidance data. Instead, based on the parameter sentiment information corresponding to the second dimension guidance data, the semantic sentiment sentence patterns corresponding to the newly added environmental parameter dimension can be extracted to obtain the corresponding second semantic sentiment sentence patterns. These patterns are then compared with standard sentiment sentence patterns in the sentiment sentence pattern library to find the optimal standard sentiment sentence pattern that is closest to the second semantic sentiment sentence pattern, and a second degree of closeness from the second semantic sentiment sentence pattern to the optimal standard sentiment sentence pattern is obtained. Then, the environmental relevance corresponding to the newly added environmental parameter dimension is fused with the second degree of closeness to obtain a fused closeness degree. If the fused closeness degree is less than the corresponding second degree threshold, it indicates that the optimal standard sentiment sentence pattern can be used for further processing; otherwise, if the fused closeness degree is greater than the corresponding second degree threshold, manual review and calibration can be performed. Once the degree of proximity is determined, the newly added environmental parameter can be further used as the second target environmental parameter to calculate the second level of importance corresponding to the second target environmental parameter. Because when the second semantic sentiment sentence is close to the optimal standard sentiment sentence, it is divided into positive proximity and negative proximity. Positive proximity requires strengthening the semantics to approach the optimal standard sentiment sentence, while negative proximity requires reducing the semantics to approach the optimal standard sentiment sentence. Therefore, the second degree of proximity can be divided into a second positive proximity degree and a second negative proximity degree. When the second proximity degree is the second positive proximity degree, in the process of calculating the second level of importance, the correlation degree setting value is first used. Relevance to environmental protection The ratio between them, combined with the degree of proximity formed by environmental relevance, represents the maximum impact. Then, the fusion proximity corresponding to the second positive proximity level is first obtained. Then through the second positive proximity degree Positive conversion factor between second importance and importance The corresponding level of importance is calculated. Then, by combining the importance of the second standard The second level of importance was calculated. When the second degree of proximity is the second negative degree of proximity, the calculation of the second degree of importance is first performed by setting the correlation value. Relevance to environmental protection The ratio between them, combined with the degree of proximity formed by environmental relevance, represents the maximum impact. Then, the fusion proximity corresponding to the second negative proximity is first obtained. Then, through the second negative proximity degree Second importance negative conversion factor The corresponding level of importance is calculated. Then, by combining the importance of the second standard The second level of importance was calculated. Therefore, by utilizing the second positive proximity degree and the second negative proximity degree to determine the importance of the second standard. To ensure the accuracy of the importance calculation, a degree of compensation is performed. The second standard of importance is pre-calibrated manually, and each second standard of importance corresponds to an optimal standard sentiment phrase. The positive and negative conversion factors of the second degree of importance are also obtained manually based on the positive and negative conversions of the importance values; the maximum influence of the proximity formed by the correlation setpoint and the environmental correlation is also manually calibrated.

[0043] In the environmental knowledge search of this invention, the prediction unit 113 includes: a benchmark extraction subunit, used to extract external benchmark thresholds corresponding to each environmental parameter from the environmental knowledge data; a numerical adjustment subunit, used to select optimization coefficients for the external benchmark thresholds according to the degree of importance, and adjust the external benchmark thresholds according to the optimization coefficients to obtain an adjustment threshold; and an optimization processing subunit, used to select the corresponding conservative threshold increment according to the difference between the enterprise's own production capacity data and the set production capacity threshold when the enterprise's own production capacity data is greater than the set production capacity threshold, and adjust the adjustment threshold according to the conservative threshold increment to obtain a parameter optimization threshold that matches the enterprise's own production capacity requirements. The calculation formula for the parameter optimization threshold is expressed as: , This represents the maximum value among the parameter optimization thresholds. This represents the minimum value among the parameter optimization thresholds. This represents the maximum value among the external benchmark thresholds. This represents the minimum value among the external benchmark thresholds. Represents the optimization coefficient. This represents the conservative increment of the threshold.

[0044] When generating the parameter optimization thresholds for each environmental parameter, prediction unit 113 mainly relies on three aspects: the level of importance of each environmental parameter, environmental knowledge data, and the enterprise's own production capacity data. Specifically, the benchmark extraction subunit can first search the environmental knowledge data to obtain the external benchmark thresholds corresponding to each environmental parameter. This environmental knowledge data can be external data from the internet, or it can be local knowledge base data. After obtaining the external benchmark thresholds, to ensure the accuracy of the thresholds, the numerical adjustment subunit can further use the level of importance to find the optimization coefficient corresponding to the current level of importance in the optimization coefficient table, and then adjust the external benchmark thresholds based on the corresponding optimization coefficients to obtain the adjusted thresholds. The calculation formula can be expressed as follows: , This represents the maximum value among the external benchmark thresholds. This represents the minimum value among the external benchmark thresholds. The optimization coefficient table stores optimization coefficients corresponding to multiple segments with different levels of importance. After obtaining the adjustment threshold, the optimization processing subunit can further compare its value with the set capacity threshold based on the company's current capacity data. If the value is less than the set capacity threshold, the adjustment threshold can be directly adjusted. The output bit is the corresponding parameter optimization threshold. When the company's own production capacity data exceeds the set production capacity threshold, the difference between the company's own production capacity data and the set production capacity threshold is used to select the corresponding conservative threshold increment by looking up the difference table. The adjustment threshold is then adjusted based on the conservative threshold increment to obtain the parameter optimization threshold that matches the company's own production capacity requirements. The difference table stores the conservative threshold increment and the range of each difference corresponding to the conservative threshold increment. In other words, by determining the range of the difference between the company's own production capacity data and the set production capacity threshold, the corresponding conservative threshold increment can be determined.

[0045] In the environmental knowledge search of the present invention, the output unit 114 includes: a result selection subunit, used to receive the selection result of the environmental parameter corresponding to the parameter optimization threshold as the optimization monitoring parameter; and a result update subunit, used to use the parameter optimization threshold corresponding to the optimization monitoring parameter and other parameter thresholds as search results to update the threshold monitoring management of enterprise environmental protection.

[0046] When outputting search results, output unit 114 ensures that environmental parameters are applicable to the corresponding enterprises and that threatening environmental parameters are effectively monitored while unnecessary environmental parameters are deleted and optimized. Therefore, it can manually select the environmental parameters that need to be monitored from the predicted environmental parameters as optimized monitoring parameters. Then, the result update subunit uses the parameter optimization threshold corresponding to the optimized monitoring parameters and the other parameter thresholds corresponding to the historical environmental parameters as search results to perform real-time update management of enterprise environmental protection threshold monitoring. This ensures the flexibility of enterprise environmental decision-making and enables enterprises to respond and improve their environmental protection system in advance before relevant environmental policies are actually implemented.

[0047] Please see Figure 2 The present invention also provides a method for searching environmental knowledge, including: Step S10: Obtain updated data on environmental protection strategies from environmental knowledge through acquisition unit 111; Step S20: The environmental protection strategy update data is analyzed by the analysis unit 112 to evaluate and determine the environmental protection parameters involved in the environmental protection strategy update data and the importance of each environmental protection parameter; Step S30: The prediction unit 113 makes a conservative prediction of environmental parameters based on the level of importance, environmental knowledge data and the company's own production capacity data, and obtains the parameter optimization threshold that matches the company's own production capacity requirements. Step S40: The parameter optimization threshold and other parameter thresholds are used as search results through the output unit 114 to monitor and manage the enterprise's environmental protection thresholds.

[0048] In summary, the environmental knowledge search system and method disclosed in this invention, by searching for updated environmental strategy data, enables real-time management of environmental parameters that enterprises need to monitor. This helps enterprises respond promptly to adjustments in environmental strategies and dynamically adjust their monitoring strategies for environmental parameters. Specifically, it first acquires updated environmental strategy data published by various environmental knowledge and strategy platform systems, and then further analyzes the data content to obtain relevant environmental parameters of concern within the updated data, as well as the degree of importance they receive. Based on this degree of importance, combined with environmental knowledge data and the enterprise's own production capacity data, conservative predictions of parameter thresholds can be made for environmental parameters. These predicted optimal thresholds are then provided to relevant enterprises to achieve parameter optimization based on relevant environmental parameters, enabling early monitoring and ensuring the flexibility of enterprises' environmental decision-making. This allows for proactive responses to and improvements to the enterprise's environmental system before relevant environmental policies are actually implemented. Therefore, this invention effectively overcomes the various shortcomings of existing technologies and has high industrial application value.

[0049] The above embodiments are merely illustrative of the principles and effects of the present invention and are not intended to limit the invention. Any person skilled in the art can modify or alter the above embodiments without departing from the spirit and scope of the present invention. Therefore, all equivalent modifications or alterations made by those skilled in the art without departing from the spirit and technical concept disclosed in the present invention should still be covered by the claims of the present invention.

Claims

1. An environmental knowledge search system, characterized in that, include: The acquisition unit is used to acquire updated data on environmental protection strategies from environmental knowledge. The analysis unit is used to analyze the environmental protection strategy update data and evaluate the environmental protection parameters involved in the environmental protection strategy update data and the degree of importance of each environmental protection parameter. The prediction unit is used to make conservative predictions of the environmental parameters based on the level of importance, environmental knowledge data, and the company's own production capacity data, and obtain parameter optimization thresholds that match the company's own production capacity requirements. as well as The output unit is used to use the parameter optimization threshold and other parameter thresholds as search results to monitor and manage the enterprise's environmental protection thresholds.

2. The environmental knowledge search system according to claim 1, characterized in that, The acquisition unit includes: The address receiving subunit is used to receive the preset address of the environmental knowledge system; and The data monitoring subunit is used to monitor the environmental knowledge of each corresponding environmental knowledge system according to the address of the environmental knowledge system, so as to obtain the updated data of the environmental protection strategy in the environmental knowledge.

3. The environmental knowledge search system according to claim 1, characterized in that, The parsing unit includes: The splitting subunit is used to split the environmental protection strategy update data into keywords, resulting in several split keywords; The transformation subunit is used to perform data dimension transformation on different environmental protection parameter dimensions based on the split keywords, to obtain dimension guidance data corresponding to different environmental protection parameter dimensions; and The analysis subunit is used to perform parameter analysis and selection based on the data guided by the dimensions, and to evaluate the environmental protection parameters involved in the environmental protection strategy update data and the degree of importance of each environmental protection parameter.

4. The environmental knowledge search system according to claim 3, characterized in that, The environmental protection parameter dimensions include known environmental protection parameter dimensions and newly added environmental protection parameter dimensions. The dimension guidance data includes first dimension guidance data corresponding to the known environmental protection parameter dimensions and second dimension guidance data corresponding to the newly added environmental protection parameter dimensions. The conversion subunit includes: The extraction and detection module is used to extract and detect known environmental parameters from the split keywords. The first output module, when extracting the first environmental dimension keyword corresponding to the known environmental parameter dimension, reorganizes the first environmental dimension keyword according to the known environmental parameter dimension using an environmental knowledge big data model to obtain first dimension guidance data. The environmental knowledge big data model is constructed using environmental knowledge big data. The second output module is used to generate a new environmental parameter dimension based on the split keywords when no first environmental dimension keyword corresponding to the known environmental parameter dimension is extracted. Then, the split keywords are extracted based on the new environmental parameter dimension to obtain the second environmental dimension keyword. Finally, the second environmental dimension keyword is recombined with the keyword corresponding to the new environmental parameter dimension through the environmental knowledge big language model to obtain the second dimension guidance data.

5. The environmental knowledge search system according to claim 4, characterized in that, The second output module includes: The search submodule is used to search for parameter sentiment information of the split keywords; The object extraction submodule is used to extract the object of action of the parameter sentiment information from the split keywords when the parameter sentiment information is found, and obtain the corresponding action keywords. The comparison calculation submodule is used to calculate the environmental relevance between the active keywords and environmental knowledge in the environmental knowledge base, and compare the calculated environmental relevance with the relevance threshold. The dimension generation submodule is used to convert the functional keywords into environmental parameter targets and generate new environmental parameters and corresponding new environmental parameter dimensions when the environmental relevance is greater than the relevance threshold; and The data recombination submodule is used to extract keywords from the split keywords based on the newly added environmental protection parameter dimension to obtain second environmental protection dimension keywords associated with the newly added environmental protection parameter dimension, and to recombine the second environmental protection dimension keywords with the corresponding keywords of the newly added environmental protection parameter dimension through the environmental knowledge big language model to obtain second dimension guidance data.

6. The environmental knowledge search system according to claim 5, characterized in that, The comparison calculation submodule includes: An adjustment value calculation module is used to find the standard keyword that is semantically closest to the functional keyword. Based on the standard keyword and the functional keyword, it calculates the semantic similarity value between the functional keyword and the standard keyword. Then, based on the semantic similarity value and an association adjustment factor, it calculates an association adjustment value. The semantic similarity value includes a semantic similarity value that approaches the standard keyword and a semantic strengthening value that lies between the standard keyword and the corresponding environmental knowledge in the environmental knowledge base. The association adjustment factor includes an association reduction factor corresponding to the semantic similarity value and an association strengthening factor corresponding to the semantic strengthening value. The fusion calculation module is used to fuse the benchmark correlation degree between the standard keywords and the corresponding environmental knowledge in the environmental knowledge base, and the correlation degree adjustment value, to obtain the environmental correlation degree. The calculation formula for the environmental correlation degree is: , Represented as the correlation reduction factor, Represented as the correlation strengthening factor, This indicates the semantic similarity value between the functional keyword and the standard keyword. Indicates the baseline correlation degree.

7. The environmental knowledge search system according to claim 3, characterized in that, The environmental protection parameter dimensions include known environmental protection parameter dimensions and newly added environmental protection parameter dimensions. The dimension guidance data includes first dimension guidance data corresponding to the known environmental protection parameter dimensions and second dimension guidance data corresponding to the newly added environmental protection parameter dimensions. The analysis subunit includes: The first analysis module is used to, when the dimension guidance data is the first dimension guidance data, perform semantic sentiment sentence pattern detection for the known environmental parameters corresponding to the known environmental parameter dimension based on the first dimension guidance data; when a first semantic sentiment sentence pattern for the known environmental parameter exists, extract the first semantic sentiment sentence pattern and compare it with the standard sentiment sentence pattern in the sentiment sentence pattern library to obtain the optimal standard sentiment sentence pattern that is closest to the first semantic sentiment sentence pattern, and the first degree of closeness between the first semantic sentiment sentence pattern and the optimal standard sentiment sentence pattern; when the first degree of closeness is less than the corresponding first degree threshold, take the known environmental parameter as the first target environmental parameter, and obtain the first degree of importance corresponding to the first target environmental parameter based on the first degree of closeness and the first standard degree of importance corresponding to the optimal standard sentiment sentence pattern. The first degree of closeness includes a first positive degree of closeness and a first negative degree of closeness, and the calculation formula for the first degree of importance is: , This indicates the level of importance attached to the primary standard. Indicates the first positive proximity. This represents the positive conversion factor of the first level of importance corresponding to the first level of positive proximity. Indicates the degree of first negative proximity. This represents the negative conversion factor of the first level of importance corresponding to the first negative proximity; when there is no first semantic sentiment sentence pattern for the known environmental parameters, the baseline level of importance corresponding to the known environmental parameters is taken as the first level of importance; and The second analysis module is used to extract semantic sentiment phrases corresponding to the newly added environmental parameter dimension when the dimension guidance data is the second dimension guidance data; compare the extracted second semantic sentiment phrases with standard sentiment phrases in the sentiment phrase library to obtain the optimal standard sentiment phrase that is closest to the second semantic sentiment phrase, and the second degree of closeness between the second semantic sentiment phrase and the optimal standard sentiment phrase; fuse the second degree of closeness with the environmental relevance corresponding to the newly added environmental parameter dimension to obtain the fused closeness; when the fused closeness is less than the corresponding second degree threshold, the newly added environmental parameter is taken as the second target environmental parameter; and according to the second degree of closeness and the second standard degree of importance corresponding to the optimal standard sentiment phrase, the second degree of importance corresponding to the second target environmental parameter is obtained. The second degree of closeness includes a second positive degree of closeness and a second negative degree of closeness. The calculation formula for the second degree of importance is: , This indicates the level of importance attached to the second standard. Indicates the second positive proximity. This represents the positive conversion factor of the second degree of importance corresponding to the second positive proximity. Indicates the degree of proximity to the second negative sign. This represents the negative conversion factor of the second degree of importance corresponding to the second negative proximity. Indicates the degree of environmental relevance. This indicates the correlation setting value. This indicates the maximum impact of the proximity in the formation of environmental relevance. This indicates the degree of fusion proximity corresponding to the second positive proximity level. This indicates the degree of fusion proximity corresponding to the second negative proximity level.

8. The environmental knowledge search system according to claim 1, characterized in that, The prediction unit includes: The benchmark extraction subunit is used to extract the external benchmark thresholds corresponding to each of the environmental protection parameters from the environmental protection knowledge data. A numerical adjustment subunit is used to select an optimization coefficient for the external benchmark threshold based on the level of importance, and adjust the external benchmark threshold according to the optimization coefficient to obtain an adjustment threshold; and The optimization processing subunit is used to, when the enterprise's own production capacity data exceeds a set production capacity threshold, look up the corresponding conservative threshold increment in a table based on the difference between the enterprise's own production capacity data and the set production capacity threshold, and adjust the adjustment threshold according to the conservative threshold increment to obtain a parameter optimization threshold that matches the enterprise's own production capacity requirements. The calculation formula for the parameter optimization threshold is expressed as follows: , This represents the maximum value among the parameter optimization thresholds. This represents the minimum value among the parameter optimization thresholds. This represents the maximum value among the external benchmark thresholds. This represents the minimum value among the external benchmark thresholds. Represents the optimization coefficient. This represents the conservative increment of the threshold.

9. The environmental knowledge search system according to claim 1, characterized in that, The output unit includes: The result selection subunit is used to receive the selection result of the environmental protection parameter corresponding to the parameter optimization threshold, as the optimization monitoring parameter; and The result update subunit is used to take the parameter optimization threshold and other parameter thresholds corresponding to the optimized monitoring parameters as search results, so as to update the threshold monitoring and management of enterprise environmental protection.

10. A method for searching environmental knowledge, characterized in that, include: The data on updated environmental strategies from environmental knowledge is obtained through the acquisition unit; The environmental protection strategy update data is analyzed by the analysis unit to evaluate and determine the environmental protection parameters involved in the environmental protection strategy update data and the degree of importance of each environmental protection parameter. The prediction unit makes conservative predictions of the environmental parameters based on the level of importance, environmental knowledge data, and the company's own production capacity data, and obtains parameter optimization thresholds that match the company's own production capacity requirements. The output unit uses the parameter optimization threshold and other parameter thresholds as search results to monitor and manage the company's environmental protection thresholds.

Images