Natural resource monitoring method based on multi-modal data

Abstract

The application discloses a natural resource monitoring method based on multi-modal data and relates to the technical field of natural resource monitoring. By dividing a monitoring area and setting a monitoring time point, plant electric signal parameters and animal nerve electric signal parameters can be collected in a targeted manner, subtle changes in an ecological system are deeply understood from a biological aspect, and plant electric signal evaluation values and animal nerve electric signal evaluation values in different regions and at different time periods are accurately analyzed, thereby providing accurate data support for biological state evaluation of the ecological system. Human activity influence parameters such as construction land expansion area, vegetation damage ratio, atmospheric pollutant emission amount and water body pollution area are comprehensively considered, the interference of human activities on natural resources is comprehensively reflected, a human activity influence factor is obtained by means of a special calculation formula, quantitative evaluation of the impact of human activities is realized, and the overall monitoring is more comprehensive and scientific.

Description

Technical Field

[0001] This invention relates to the field of natural resource monitoring technology, and more specifically to a natural resource monitoring method based on multimodal data. Background Technology

[0002] With the rapid development of the global economy and the continuous growth of the population, human activities have increasingly intensified the exploitation of natural resources, placing unprecedented pressure and challenges on these resources. On the one hand, large-scale urbanization has led to the continuous expansion of construction land, resulting in the felling and destruction of vast amounts of vegetation, directly altering the ecological structure of the earth's surface and impacting the habitats of organisms. On the other hand, air pollutants emitted from industrial production and agricultural activities, along with untreated or substandard wastewater, have flowed indiscriminately into water bodies, causing serious environmental problems such as air and water pollution, further threatening the balance of ecosystems.

[0003] Existing technologies, such as the invention application patent with publication number CN118607967A, disclose a method and system for monitoring the effectiveness of ecological restoration based on big data analysis, which relates to the field of ecological management technology. This method includes: identifying missing areas in a target region and collecting basic ecological monitoring data from normal areas; supplementing the ecological monitoring data of the missing areas to obtain supplementary ecological monitoring data; collecting missing feature information of the missing areas, performing ecological impact analysis, and obtaining ecological impact parameters; indexing multiple neighboring ecological monitoring data based on the location information of the missing areas, performing ecological impact analysis of the missing areas on normal areas, correcting the ecological impact parameters, generating missing ecological monitoring data, and obtaining the monitoring results of ecological restoration effectiveness. This invention achieves the technical effect of improving the completeness and accuracy of ecological restoration effectiveness monitoring and analysis when there are missing areas where ecological data cannot be collected.

[0004] Regarding the above-mentioned solutions, the inventors of this application have discovered at least the following technical problems: 1. Existing technologies often focus on single-type data collection, such as only focusing on environmental physical indicators or traditional ecological survey data, lacking the capture of microscopic information at the biological level, such as bioelectrical signals. This results in the inability to understand the health status of the ecosystem in real time and accurately from the perspective of changes in the physiological state of organisms themselves, making it difficult to detect the stress response of organisms in the early stages of environmental changes, leading to a lag in the discovery of ecological problems. In most cases, it simply counts the intuitive appearance of human activities, such as simply recording the increase or decrease in the area of ​​construction land, without comprehensively considering the synergistic effects of vegetation destruction, air and water pollution, etc., and without quantifying the impact factors of human activities through scientific weighted calculations. As a result, it is impossible to accurately measure the overall impact intensity of human activities on natural resources, lacking precise basis when formulating control strategies, and easily leading to a one-sided governance situation of "treating the symptoms rather than the root cause."

[0005] 2. Existing technologies typically employ standardized, universal ecological restoration and protection measures, neglecting the differences in natural characteristics such as water conservation, soil erosion, and water vapor flux across different regions. This means that in some ecologically fragile areas or areas with unique landforms, the implemented protection plans may be unsuitable, failing to address the root causes. This not only wastes resources but also makes it difficult to achieve the desired ecological restoration results. Due to untimely data collection and lengthy analysis processes, it is difficult to integrate multifaceted biological and environmental information to provide real-time assessment results. When the ecosystem is already approaching the edge of imbalance, existing technologies may not have issued a warning, preventing managers from responding quickly and missing the optimal intervention window, leading to further deterioration of ecological problems and a significant increase in subsequent restoration costs.

[0006] 3. Existing technologies lack comprehensive insights into biological and human activities and their interactions. Decision-makers, relying solely on fragmented and localized data, struggle to coordinate the needs of ecological protection and resource development. This leads to a tendency to overlook certain aspects when planning long-term development paths and formulating scientific policies and regulations, hindering the sustainable management and utilization of natural resources and preventing the proactive mitigation of potential ecological risks. Summary of the Invention

[0007] To address the aforementioned technical shortcomings, the purpose of this invention is to provide a natural resource monitoring method based on multimodal data.

[0008] To solve the above-mentioned technical problems, the present invention adopts the following technical solution: The present invention provides a natural resource monitoring method based on multimodal data, comprising:

[0009] Step 1: Installation of bioelectric monitoring equipment: Divide the target natural environment into several monitoring areas, install bioelectric monitoring equipment in each monitoring area, and set several monitoring time points. Then, collect the corresponding plant electrical signal parameters and animal neural electrical signal parameters in each monitoring area at each monitoring time point.

[0010] Step 2: Analysis of electrical signal evaluation values: Based on the plant electrical signal parameters and animal neural electrical signal parameters collected at each monitoring time point for each monitoring area, the plant electrical signal evaluation values ​​and animal neural electrical signal evaluation values ​​for each monitoring area at each monitoring time point are analyzed.

[0011] Step 3: Obtaining the impact factors of human activities: Collect the human activity impact parameters corresponding to each monitoring area at each monitoring time point. The human activity impact parameters include the area of ​​construction land expansion, the proportion of vegetation destruction, the amount of air pollutant emissions, and the area of ​​water pollution. In this way, the human activity impact factors corresponding to each monitoring area at each monitoring time point are analyzed.

[0012] Step 4: Recommendation of ecological restoration strategies; assess whether the interaction between organisms and the environment in each monitoring area at each monitoring time point is stable and coordinated. If the interaction between organisms and the environment in a certain monitoring area at a certain monitoring time point is unstable and coordinated, analyze the ecological restoration strategies corresponding to that monitoring area at that monitoring time point and make early warning recommendations.

[0013] Preferably, the installation of bioelectric monitoring equipment in each monitoring area is carried out as follows:

[0014] Obtain the water conservation capacity, soil erosion modulus, and water vapor flux corresponding to each monitoring area. Compare the water conservation capacity, soil erosion modulus, and water vapor flux corresponding to each monitoring area with the corresponding models and quantities of bioelectric monitoring equipment in the database. If the water conservation capacity, soil erosion modulus, and water vapor flux corresponding to a certain monitoring area are the same as the corresponding models and quantities of a certain bioelectric monitoring equipment in the database, then use the corresponding models and quantities of bioelectric monitoring equipment in the database as the corresponding models and quantities of bioelectric monitoring equipment in that monitoring area, and install the corresponding models and quantities of bioelectric monitoring equipment in each monitoring area.

[0015] Preferably, the plant electrical signal parameters include the electrical signal amplitude, electrical signal frequency, and electrical signal conduction velocity corresponding to various types of plants, and the animal neural electrical signal parameters include the nerve impulse firing frequency, brain electrical signal power spectral density, and neural electrical signal phase-locking value corresponding to various types of animal groups.

[0016] Preferably, the analysis yields plant electrical signal evaluation values ​​and animal neural electrical signal evaluation values ​​for each monitoring area at each monitoring time point. The specific analysis process is as follows:

[0017] A1. Input the amplitude, frequency and conduction speed of the electrical signals of various types of plants in each monitoring area at each monitoring time point into the plant electrical signal evaluation value analysis model, and output the plant electrical signal evaluation values ​​of each monitoring area collected at each monitoring time point.

[0018] A2. Input the nerve impulse firing frequency, EEG power spectral density, and nerve electrical signal phase-locked value of each animal species in each monitoring area at each monitoring time point into the animal nerve electrical signal evaluation value analysis model, and output the animal nerve electrical signal evaluation value collected at each monitoring time point for each monitoring area.

[0019] Preferably, the analysis yields the human activity impact factors corresponding to each monitoring area at each monitoring time point. The specific analysis process is as follows:

[0020] The area of ​​construction land expansion, the proportion of vegetation destruction, the amount of air pollutant emissions, and the area of ​​water pollution corresponding to each monitoring area at each monitoring time point are respectively denoted as follows: , , and Let m represent the number corresponding to each monitoring time point, where m is a positive integer, and k represent the number corresponding to each monitoring area, where k is a positive integer. Substitute these values ​​into the calculation formula: In this process, the human activity impact factors corresponding to each monitoring time point and each monitoring area were obtained. ,in, , , , These are the standard construction land expansion area, standard vegetation destruction ratio, standard air pollutant emissions, and standard water pollution area corresponding to the designated monitoring areas. , , , These are the weighting factors corresponding to the expansion area of ​​construction land in the designated monitoring area, the weighting factor corresponding to the proportion of vegetation destruction, the weighting factor corresponding to the amount of air pollutant emissions, and the weighting factor corresponding to the area of ​​water pollution. , , , These are the adjustment factors corresponding to the expansion area of ​​construction land in the designated monitoring area, the adjustment factor corresponding to the proportion of vegetation destruction, the adjustment factor corresponding to the amount of air pollutant emissions, and the adjustment factor corresponding to the area of ​​water pollution.

[0021] Preferably, the evaluation process for determining whether the interaction between organisms and the environment in each monitoring area at each monitoring time point is stable and coordinated is as follows:

[0022] The plant electrical signal assessment values, animal neural electrical signal assessment values, and human activity impact factors corresponding to each monitoring area at each monitoring time point are input into the resource improvement assessment value assessment model, and the biological and environmental interaction status assessment value results corresponding to each monitoring area at each monitoring time point are output.

[0023] The evaluation results of the biological-environment interaction status include values ​​of 1 and -1. When the evaluation result of the biological-environment interaction status for a certain monitoring area at a certain monitoring time point is 1, the biological-environment interaction status for that monitoring area at that monitoring time point is assessed as unstable and coordinated. Conversely, when the evaluation result of the biological-environment interaction status for a certain monitoring area at a certain monitoring time point is -1, the biological-environment interaction status for that monitoring area at that monitoring time point is assessed as stable and coordinated.

[0024] Preferably, the expression for the resource improvement assessment value evaluation model is: In the formula, This represents the assessment results of the biological-environment interaction status for each monitoring area at each monitoring time point, where... , These represent the plant electrical signal evaluation values ​​and animal neural electrical signal evaluation values ​​collected at each monitoring time point for each monitoring area. , These represent the standard plant electrical signal evaluation values ​​and the standard animal neural electrical signal evaluation values ​​corresponding to the designated monitoring areas, respectively. , These represent the weighting factors corresponding to the plant electrical signal evaluation values ​​and the animal neural electrical signal evaluation values ​​in the designated monitoring area, respectively. The threshold value for assessing the interaction state between organisms and the environment is set. This represents the collection corresponding to each monitoring area.

[0025] Preferably, if the interaction between organisms and the environment in a certain monitoring area at a certain monitoring time point is unstable and uncoordinated, then the ecological restoration strategy corresponding to that monitoring area at that monitoring time point is analyzed. The specific analysis process is as follows:

[0026] If the interaction between organisms and the environment in a certain monitoring area at a certain monitoring time point is unstable and uncoordinated, then the environmental interaction status assessment value of the monitoring area at that monitoring time point will be compared with the environmental interaction status assessment value range of each ecological restoration strategy in the database. If the environmental interaction status assessment value of the monitoring area at that monitoring time point is within the environmental interaction status assessment value range of a certain ecological restoration strategy in the database, then the ecological restoration strategy in the database will be used as the ecological restoration strategy for the monitoring area at that monitoring time point.

[0027] The beneficial effects of this invention are as follows: 1. In the embodiments of this invention, by dividing monitoring areas and setting monitoring time points, targeted collection of plant electrical signal parameters and animal neural electrical signal parameters can be achieved. This allows for in-depth insight into subtle changes in the ecosystem at the biological level, and precise analysis of plant electrical signal assessment values ​​and animal neural electrical signal assessment values ​​in different regions and at different times, providing accurate data support for the assessment of the biological status of the ecosystem. 2. By comprehensively considering parameters related to human activities such as the area of ​​construction land expansion, the proportion of vegetation destruction, air pollutant emissions, and the area of ​​water pollution, the invention comprehensively reflects the interference of human activities on natural resources. Using specialized calculation formulas, the invention derives human activity impact factors, achieving a quantitative assessment of the impact of human activities, making the overall monitoring more comprehensive and scientific.

[0028] 2. In this embodiment of the invention, based on the unique water conservation capacity, soil erosion modulus, and water vapor flux of each monitoring area, the appropriate model and quantity of bioelectric monitoring equipment are matched to ensure that the deployment of monitoring equipment aligns with the actual ecological characteristics of the area, thereby improving the accuracy and effectiveness of monitoring. Once an area with unstable and uncoordinated interaction between organisms and the environment is identified, the most suitable restoration plan for the area can be quickly selected by comparing it with a rich database of ecological restoration strategies. This achieves personalized ecological restoration, avoids "one-size-fits-all" protection measures, and maximizes the optimization of restoration effects.

[0029] 3. In this embodiment of the invention, multimodal data is collected and analyzed in real time at regular intervals, enabling rapid output of assessment results on the interaction status between organisms and the environment. Once the results indicate unstable coordination, an early warning is immediately triggered, allowing managers to grasp ecological crisis information at the first opportunity and take timely countermeasures to prevent problems before they occur. Continuous monitoring and repeated assessments form a dynamic management closed loop, tracking the implementation effectiveness of ecological restoration strategies and flexibly adjusting subsequent management measures based on feedback data. This ensures the ecosystem continues to develop in a stable and coordinated direction, providing natural resource management departments with comprehensive data insights covering organisms, human activities, and their interaction status. This helps them formulate long-term plans, policies, and regulations based on detailed and accurate information, finding a precise balance between resource development and ecological protection, and promoting the sustainable use of natural resources. Through the accumulation and in-depth analysis of multimodal data, the patterns and trends of ecosystem changes are uncovered, potential risks are predicted in advance, providing a strong basis for forward-looking decision-making and guiding ecological protection work from passive response to proactive prevention. Attached Figure Description

[0030] To more clearly illustrate the technical solutions in the embodiments of the present invention 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 the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0031] Figure 1 This is a flowchart illustrating the implementation steps of the method of the present invention. Detailed Implementation

[0032] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0033] Examples of embodiments of the present invention Figure 1As shown, the natural resource monitoring method based on multimodal data includes:

[0034] Step 1: Installation of bioelectric monitoring equipment: Divide the target natural environment into several monitoring areas, install bioelectric monitoring equipment in each monitoring area, and set several monitoring time points. Then, collect the corresponding plant electrical signal parameters and animal neural electrical signal parameters in each monitoring area at each monitoring time point.

[0035] In one specific embodiment, the installation of bioelectric monitoring equipment in each monitoring area is carried out as follows:

[0036] Obtain the water conservation capacity, soil erosion modulus, and water vapor flux corresponding to each monitoring area. Compare the water conservation capacity, soil erosion modulus, and water vapor flux corresponding to each monitoring area with the corresponding models and quantities of bioelectric monitoring equipment in the database. If the water conservation capacity, soil erosion modulus, and water vapor flux corresponding to a certain monitoring area are the same as the corresponding models and quantities of a certain bioelectric monitoring equipment in the database, then use the corresponding models and quantities of bioelectric monitoring equipment in the database as the corresponding models and quantities of bioelectric monitoring equipment in that monitoring area, and install the corresponding models and quantities of bioelectric monitoring equipment in each monitoring area.

[0037] It should be noted that multiple rainfall monitoring stations are set up within the monitoring area to accurately record precipitation data over a specific time period (e.g., one year). Simultaneously, water level gauges and flow meters are installed around rivers, lakes, and other water bodies, combined with groundwater monitoring wells to monitor groundwater level changes, acquiring surface runoff and groundwater runoff data. Standardized runoff plots are established within the monitoring area according to different terrains, soil types, vegetation cover, and other conditions. Runoff plots generally have a certain area (e.g., 10m × 20m), with embankments around them to prevent runoff from flowing in from the outside, and an impermeable layer at the bottom to guide and collect the runoff. After each rainfall event, the sediment content collected at the runoff plot outlet is measured. Combined with meteorological data such as rainfall intensity and duration, as well as the plot area, the soil erosion modulus is calculated. Eddy covariance instruments, which include ultrasonic anemometers and fast-response water vapor concentration sensors, are installed in open areas with relatively uniform vegetation within the monitoring area. The ultrasonic anemometers can measure three-dimensional wind speed fluctuations in real time, while the water vapor concentration sensor simultaneously measures changes in water vapor concentration. By collecting high-frequency (e.g., 10Hz or 20Hz) data using instruments, and based on the principle of eddy covariance, the covariance between wind speed fluctuations and water vapor concentration fluctuations is calculated to obtain the water vapor flux. This method can directly obtain the water vapor exchange flux between the ecosystem and the atmosphere.

[0038] Step 2: Analysis of electrical signal evaluation values: Based on the plant electrical signal parameters and animal neural electrical signal parameters collected at each monitoring time point for each monitoring area, the plant electrical signal evaluation values ​​and animal neural electrical signal evaluation values ​​for each monitoring area at each monitoring time point are obtained through analysis.

[0039] In one specific embodiment, the plant electrical signal parameters include the electrical signal amplitude, electrical signal frequency, and electrical signal conduction velocity corresponding to various types of plants, and the animal neural electrical signal parameters include the nerve impulse firing frequency, brain electrical signal power spectral density, and neural electrical signal phase-locking value corresponding to various types of animal groups.

[0040] It is important to note that a suitable microelectrode must be selected. The microelectrode is carefully inserted into the plant's stem, leaves, or other tissues, typically near the vascular bundles. This is a crucial pathway for electrical signal transmission within the plant, ensuring accurate signal capture. The microelectrode is connected to a high-sensitivity bioelectric amplifier, which amplifies the weak plant electrical signals. The amplified signal is then input to a data acquisition card, which discretizes the signal at a high sampling frequency, recording the amplitude at different times. Through continuous long-term acquisition, a large amount of data is accumulated, including electrical signal amplitude, frequency, and conduction velocity.

[0041] It should also be noted that, based on the animal species and body size, an appropriate size EEG electrode cap is selected. The electrodes on the cap are laid out according to the international standard 10-20 system or its derivative animal-specific electrode layout, precisely covering all brain regions of the animal to ensure comprehensive acquisition of EEG signals. For specific research needs, electrode density can be increased in specific brain regions. After the EEG signals are acquired by the electrode cap, they are transmitted to an EEG signal processing workstation. The workstation first preprocesses the signals, including removing artifacts such as electrooculography (EOG) and electromyography (EMG). Then, it uses spectral analysis algorithms such as Fast Fourier Transform (FFT) to convert the time-domain EEG signals into frequency-domain signals, calculating the power spectral density of different frequency bands (such as delta waves, theta waves, alpha waves, beta waves, gamma waves, etc.). Acquisition and analysis are repeated at each monitoring time point to obtain the neural impulse firing frequency, EEG signal power spectral density, and neural signal phase-locking values ​​corresponding to various animal species and groups.

[0042] In another specific embodiment, the analysis yields the plant electrical signal evaluation values ​​and animal neural electrical signal evaluation values ​​for each monitoring area at each monitoring time point. The specific analysis process is as follows:

[0043] A1. Input the amplitude, frequency and conduction speed of the electrical signals of various types of plants in each monitoring area at each monitoring time point into the plant electrical signal evaluation value analysis model, and output the plant electrical signal evaluation values ​​of each monitoring area collected at each monitoring time point.

[0044] It should be noted that the analysis process for the plant electrical signal evaluation values ​​collected at each monitoring time point for each monitoring area is as follows: The amplitude, frequency, and conduction velocity of the electrical signals corresponding to various plant species in each monitoring area at each monitoring time point are normalized, and the processed amplitude, frequency, and conduction velocity of the electrical signals corresponding to various plant species in each monitoring area at each monitoring time point are respectively denoted as... , and Substitute into the analysis formula The evaluation values ​​of plant electrical signals corresponding to each monitoring area were obtained at each monitoring time point. Here, m represents the number corresponding to each monitoring time point, and m is a positive integer; k represents the number corresponding to each monitoring area, and k is a positive integer; u represents the number corresponding to each type of plant, and u is a positive integer.

[0045] A2. Input the nerve impulse firing frequency, EEG power spectral density, and nerve electrical signal phase-locked value of each animal species in each monitoring area at each monitoring time point into the animal nerve electrical signal evaluation value analysis model, and output the animal nerve electrical signal evaluation value collected at each monitoring time point for each monitoring area.

[0046] It should be noted that the animal neural electrical signal evaluation values ​​collected at each monitoring time point for each monitoring area were obtained by analyzing the plant electrical signal evaluation values ​​collected at each monitoring time point for each monitoring area.

[0047] Step 3: Obtaining the impact factors of human activities: Collect the human activity impact parameters corresponding to each monitoring area at each monitoring time point. The human activity impact parameters include the area of ​​construction land expansion, the proportion of vegetation destruction, the amount of air pollutant emissions, and the area of ​​water pollution. In this way, the human activity impact factors corresponding to each monitoring area at each monitoring time point are analyzed.

[0048] In a specific embodiment, the analysis yields the human activity impact factors corresponding to each monitoring area at each monitoring time point. The specific analysis process is as follows:

[0049] The area of ​​construction land expansion, the proportion of vegetation destruction, the amount of air pollutant emissions, and the area of ​​water pollution corresponding to each monitoring area at each monitoring time point are respectively denoted as follows: , , and Let m represent the number corresponding to each monitoring time point, where m is a positive integer, and k represent the number corresponding to each monitoring area, where k is a positive integer. Substitute these values ​​into the calculation formula: In this process, the human activity impact factors corresponding to each monitoring time point and each monitoring area were obtained. ,in, , , , These are the standard construction land expansion area, standard vegetation destruction ratio, standard air pollutant emissions, and standard water pollution area corresponding to the designated monitoring areas. , , , These are the weighting factors corresponding to the expansion area of ​​construction land in the designated monitoring area, the weighting factor corresponding to the proportion of vegetation destruction, the weighting factor corresponding to the amount of air pollutant emissions, and the weighting factor corresponding to the area of ​​water pollution. , , , These are the adjustment factors corresponding to the expansion area of ​​construction land in the designated monitoring area, the adjustment factor corresponding to the proportion of vegetation destruction, the adjustment factor corresponding to the amount of air pollutant emissions, and the adjustment factor corresponding to the area of ​​water pollution.

[0050] It should be noted that, , , , All are greater than 0 and less than 1.

[0051] It should also be noted that, using models such as ecological footprint and InVEST, the maximum development scale to maintain ecological functions is calculated based on regional ecological conditions such as soil, water resources, and biodiversity. Simultaneously, control indicators are defined according to the region's overall planning and land use planning, based on its regional positioning (e.g., ecological protection zone, industrial new area). First, vegetation types are identified, and ecological assessment software combined with field surveys is used to determine the scale based on the ecological function weights of vegetation. Alternatively, vegetation cover data from the past few decades can be collected, referencing the average proportion during periods without ecological problems, and then fine-tuned based on development pressures. Based on national or local air quality standards, and referring to atmospheric diffusion models such as AERMOD and CALPUFF, combined with topography, meteorology, and pollution sources, the regional air quality functional zones are used to infer the optimal emission standards. Furthermore, industry production processes and pollution control technologies can be investigated, and the best feasible emission benchmarks can be used to set the standards. Based on local water environment functional zoning and the "Surface Water Environmental Quality Standards," the maximum pollution area for ensuring water quality was calculated using a water quality model combined with watershed hydrological and pollution source data. Simultaneously, based on aquatic ecosystem health indicator monitoring, and using the critical value indicating degradation as the standard, a team of experts in ecology, environment, and planning compared the importance of four indicators to the ecosystem in pairs to construct a judgment matrix. After consistency testing, the eigenvectors were solved and normalized to obtain the weights. A large amount of regional ecological environment and auxiliary data was collected, standardized, and the covariance matrix was calculated and decomposed to obtain eigenvalues ​​and vectors. Principal components were selected based on cumulative contribution rates, and the loading coefficients were normalized to obtain weights. A single-factor sensitivity analysis was performed on each indicator using the Comprehensive Environmental Assessment Model (CEAM). Values ​​were assigned based on the sensitivity of each indicator to the comprehensive ecosystem evaluation indicators, with high sensitivity assigned to 1.5-2 and low sensitivity assigned to 0.8-1. The region's economic development stage was defined; for example, during the rapid industrialization stage, the adjustment factor for the area of ​​construction land expansion was appropriately reduced; during the ecological restoration and transformation stage, the adjustment factor for the area of ​​vegetation destruction and water pollution was increased.

[0052] Step 4: Recommendation of ecological restoration strategies; assess whether the interaction between organisms and the environment in each monitoring area at each monitoring time point is stable and coordinated. If the interaction between organisms and the environment in a certain monitoring area at a certain monitoring time point is unstable and coordinated, analyze the ecological restoration strategies corresponding to that monitoring area at that monitoring time point and make early warning recommendations.

[0053] In one specific embodiment, the evaluation process for determining whether the interaction between organisms and the environment in each monitoring area at each monitoring time point is stable and coordinated is as follows:

[0054] The plant electrical signal assessment values, animal neural electrical signal assessment values, and human activity impact factors corresponding to each monitoring area at each monitoring time point are input into the resource improvement assessment value assessment model, and the biological and environmental interaction status assessment value results corresponding to each monitoring area at each monitoring time point are output.

[0055] The evaluation results of the biological-environment interaction status include values ​​of 1 and -1. When the evaluation result of the biological-environment interaction status for a certain monitoring area at a certain monitoring time point is 1, the biological-environment interaction status for that monitoring area at that monitoring time point is assessed as unstable and coordinated. Conversely, when the evaluation result of the biological-environment interaction status for a certain monitoring area at a certain monitoring time point is -1, the biological-environment interaction status for that monitoring area at that monitoring time point is assessed as stable and coordinated.

[0056] In another specific embodiment, the expression for the resource improvement assessment value evaluation model is: In the formula, This represents the assessment results of the biological-environment interaction status for each monitoring area at each monitoring time point, where... , These represent the plant electrical signal evaluation values ​​and animal neural electrical signal evaluation values ​​collected at each monitoring time point for each monitoring area. , These represent the standard plant electrical signal evaluation values ​​and the standard animal neural electrical signal evaluation values ​​corresponding to the designated monitoring areas, respectively. , These represent the weighting factors corresponding to the plant electrical signal evaluation values ​​and the animal neural electrical signal evaluation values ​​in the designated monitoring area, respectively. The threshold value for assessing the interaction state between organisms and the environment is set. This represents the collection corresponding to each monitoring area.

[0057] It should be noted that, , All are greater than 0 and less than 1.

[0058] It should also be noted that specialized equipment is used to collect data on the amplitude and conduction velocity of electrical signals at the vascular bundles of plant stems and leaves. This data is then organized by plant species and time period, and converted into evaluation values ​​using data analysis models and reference signal strengths. For example, low values ​​are given when the electrical signals of herbaceous plants are abnormal. A baseline database of electrical signals is established by long-term collection from ecologically stable areas with similar plants. Normal amplitude and velocity averages are obtained through analysis, and adjustments are made according to plant growth and seasons. For example, trees have normal amplitude ranges during their peak season. Surface electrodes are implanted in small animals and attached to large animals to capture nerve impulse frequencies and EEG power spectral density. These data are recorded in real time by the acquisition system, denoised, and analyzed according to animal behavior and scenarios to obtain evaluation values. For example, abnormal frequencies during the breeding season indicate ecological problems. In ecologically sound areas, long-term monitoring of target animals is used to build health models, defining standard neural electrical signal patterns for each stage and activity. These models are used to assess whether the monitored area is abnormal. Ecological and zoological experts are invited to compare the plant characteristics reflected in the plant electrical signals with the animal characteristics associated with the animal electrical signals, constructing a judgment matrix. After verification, the weights are normalized, with higher weights for animals that are sensitive to certain signals.

[0059] In another specific embodiment, if the interaction between organisms and the environment in a certain monitoring area at a certain monitoring time point is unstable and uncoordinated, then the ecological restoration strategy corresponding to that monitoring area at that monitoring time point is analyzed. The specific analysis process is as follows:

[0060] If the interaction between organisms and the environment in a certain monitoring area at a certain monitoring time point is unstable and uncoordinated, then the environmental interaction status assessment value of the monitoring area at that monitoring time point will be compared with the environmental interaction status assessment value range of each ecological restoration strategy in the database. If the environmental interaction status assessment value of the monitoring area at that monitoring time point is within the environmental interaction status assessment value range of a certain ecological restoration strategy in the database, then the ecological restoration strategy in the database will be used as the ecological restoration strategy for the monitoring area at that monitoring time point.

[0061] The above description is merely an example and illustration of the concept of the present invention. Those skilled in the art can make various modifications or additions to the specific embodiments described or use similar methods to replace them, as long as they do not deviate from the concept of the invention or exceed the scope defined in this specification, they should all fall within the protection scope of the present invention.

Claims

1. A method for natural resource monitoring based on multi-modal data, characterized in that, The method comprises the following steps: Step 1, installation of bioelectricity monitoring equipment: divide the target natural environment into a plurality of monitoring areas, install bioelectricity monitoring equipment in each monitoring area, and set a plurality of monitoring time points, and then collect plant electrical signal parameters and animal neural electrical signal parameters corresponding to each monitoring area at each monitoring time point; The plant electrical signal parameters include the electrical signal amplitude, the electrical signal frequency and the electrical signal conduction speed corresponding to various types of plants, and the animal neural electrical signal parameters include the neural impulse emission frequency, the electroencephalogram power spectrum density corresponding to various types of animals, and the neural electrical signal phase locking value corresponding to various types of animal groups; Step 2, analysis of electrical signal evaluation value: according to the plant electrical signal parameters and the animal neural electrical signal parameters collected at each monitoring time point, the plant electrical signal evaluation value and the animal neural electrical signal evaluation value corresponding to each monitoring area at each monitoring time point are analyzed and obtained; Step 3, obtaining of human activity influence factor: obtaining the human activity influence parameters corresponding to each monitoring area at each monitoring time point, the human activity influence parameters including the construction land expansion area, the vegetation damage ratio, the atmospheric pollutant emission amount and the water pollution area, so as to analyze and obtain the human activity influence factor corresponding to each monitoring area at each monitoring time point; Step 4, recommendation of ecological restoration strategy; Evaluating whether the biological and environmental interaction state corresponding to each monitoring area at each monitoring time point is stable and coordinated, if the biological and environmental interaction state corresponding to a monitoring area at a monitoring time point is not stable and coordinated, analyzing the ecological restoration strategy corresponding to the monitoring area at the monitoring time point, and performing early warning recommendation; Inputting the plant electrical signal evaluation value, the animal neural electrical signal evaluation value and the human activity influence factor corresponding to each monitoring area at each monitoring time point into a resource improvement evaluation value evaluation model, and outputting the biological and environmental interaction state evaluation value result corresponding to each monitoring area at each monitoring time point; The expression of the resource improvement evaluation value evaluation model is: , wherein, represents the biological and environmental interaction state evaluation value result corresponding to each monitoring area at each monitoring time point, wherein m represents the number corresponding to each monitoring time point, m is a positive integer, k represents the number corresponding to each monitoring area, k is a positive integer, , respectively represent the plant electrical signal evaluation value and the animal nerve electrical signal evaluation value of each monitoring area collected at each monitoring time point, , respectively represent the standard plant electrical signal evaluation value and the standard animal nerve electrical signal evaluation value corresponding to the set monitoring area, , respectively represent the weight factor corresponding to the plant electrical signal evaluation value and the weight factor corresponding to the animal nerve electrical signal evaluation value of the set monitoring area, is the set biological and environmental interaction state evaluation value threshold, represents the set corresponding to each monitoring area.

2. The method for natural resource monitoring based on multi-modal data of claim 1, wherein, The bioelectricity monitoring equipment is installed in each monitoring area, and the specific installation process is as follows: Obtaining the water source conservation amount, the soil erosion modulus and the water vapor flux corresponding to each monitoring area, and comparing the water source conservation amount, the soil erosion modulus and the water vapor flux corresponding to each monitoring area with the type and quantity of each bioelectricity monitoring equipment in the database, if the water source conservation amount, the soil erosion modulus and the water vapor flux corresponding to a monitoring area are the same as the type and quantity of a bioelectricity monitoring equipment in the database, the type and quantity of the bioelectricity monitoring equipment in the database are taken as the type and quantity of the bioelectricity monitoring equipment in the monitoring area, and each monitoring area is installed according to the corresponding type and quantity of the bioelectricity monitoring equipment.

3. The method for natural resource monitoring based on multi-modal data of claim 1, wherein, The specific analysis process of obtaining the plant electrical signal evaluation value and the animal neural electrical signal evaluation value corresponding to each monitoring area at each monitoring time point is as follows: A1, inputting the electrical signal amplitude, the electrical signal frequency and the electrical signal conduction speed corresponding to various types of plants corresponding to each monitoring area at each monitoring time point into a plant electrical signal evaluation value analysis model, and outputting the plant electrical signal evaluation value corresponding to each monitoring area at each monitoring time point; A2, input the nerve impulse frequency corresponding to various animals in each monitoring area at each monitoring time point, the power spectrum density of the electroencephalogram signal, and the phase-locked value of the neural electrical signal corresponding to various animal groups into the animal neural electrical signal evaluation value analysis model, and output the animal neural electrical signal evaluation value of each monitoring area at each monitoring time point.

4. The method for natural resource monitoring based on multi-modal data of claim 3, wherein, The analysis obtains human activity influence factors corresponding to each monitoring area at each monitoring time point, and the specific analysis process is as follows: The construction land expansion area, the vegetation destruction ratio, the atmospheric pollutant emission amount and the water pollution area corresponding to each monitoring area at each monitoring time point are respectively recorded as 、 、 and , m represents the number corresponding to each monitoring time point, m is a positive integer, k represents the number corresponding to each monitoring area, k is a positive integer, and the calculation formula is substituted into , to obtain the human activity influence factor of each monitoring area at each monitoring time point, wherein 、 、 、 are respectively the standard construction land expansion area, the standard vegetation destruction ratio, the standard atmospheric pollutant emission amount and the standard water pollution area corresponding to the set monitoring area, 、 、 、 are respectively the weight factor corresponding to the construction land expansion area of the set monitoring area, the weight factor corresponding to the vegetation destruction ratio, the weight factor corresponding to the atmospheric pollutant emission amount and the weight factor corresponding to the water pollution area, 、 、 、 are respectively the adjustment factor corresponding to the construction land expansion area of the set monitoring area, the adjustment factor corresponding to the vegetation destruction ratio, the adjustment factor corresponding to the atmospheric pollutant emission amount and the adjustment factor corresponding to the water pollution area.

5. The method for natural resource monitoring based on multi-modal data of claim 4, wherein, The evaluation of whether the biological and environmental interaction state corresponding to each monitoring area at each monitoring time point is stable and coordinated is as follows: The biological and environmental interaction state evaluation value result includes the values of 1 and -1. When the biological and environmental interaction state evaluation value result corresponding to a certain monitoring area at a certain monitoring time point is 1, it is evaluated that the biological and environmental interaction state corresponding to the monitoring area at the monitoring time point is unstable and coordinated, and vice versa. When the biological and environmental interaction state evaluation value result corresponding to a certain monitoring area at a certain monitoring time point is -1, it is evaluated that the biological and environmental interaction state corresponding to the monitoring area at the monitoring time point is stable and coordinated.

6. The method for natural resource monitoring based on multi-modal data of claim 5, wherein, If the biological and environmental interaction state corresponding to a certain monitoring area at a certain monitoring time point is unstable and coordinated, the ecological restoration strategy corresponding to the monitoring area at the monitoring time point is analyzed, and the specific analysis process is as follows: If the biological and environmental interaction state corresponding to a certain monitoring area at a certain monitoring time point is unstable and coordinated, the environmental interaction state evaluation value corresponding to the monitoring area at the monitoring time point is compared with the environmental interaction state evaluation value interval corresponding to each ecological restoration strategy in the database. If the environmental interaction state evaluation value corresponding to the monitoring area at the monitoring time point is located in the environmental interaction state evaluation value interval corresponding to a certain ecological restoration strategy in the database, the ecological restoration strategy in the database is taken as the ecological restoration strategy corresponding to the monitoring area at the monitoring time point.

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