Dynamic identification method for ecological restoration of abandoned mine

Abstract

The invention discloses a dynamic identification method for ecological restoration of an abandoned mine, and particularly relates to the technical field of ecological restoration, and the method comprises the steps: obtaining a data inconsistency coefficient, an electromagnetic interference coefficient and a data mutation coefficient in each subinterval to obtain an error coefficient of the subinterval; and establishing an error coefficient set according to the error coefficients in all the subintervals, screening out all the error coefficients in the error coefficient set, establishing an abnormal set for all the error coefficients, calculating a standard deviation in the abnormal set, comparing the standard deviation with a preset standard deviation value, and sending an alarm signal according to a comparison result. Therefore, whether errors exist in the identified ecological data about the target abandoned mine or not can be judged in time, it is ensured that the possibility of errors occurring in the recovery progress of the dynamically identified target abandoned mine is small, and the ecological restoration effect and efficiency of the target abandoned mine cannot be affected.

Description

Technical Field

[0001] The invention relates to the technical field of ecological restoration, and in particular to a dynamic identification method for ecological restoration of abandoned mines. Background Art

[0002] The dynamic identification method of abandoned mine ecological restoration evaluates the progress of restoration by regularly acquiring and analyzing ecological data. The main steps include: first, using multi-source data fusion technology to integrate multiple data sources such as remote sensing images, meteorological data, and soil data to establish a dynamic monitoring system; then, through machine learning and data mining technology, the data is processed and analyzed to identify the changing trends of key ecological indicators; then, the model is used to predict the development trend of future ecological restoration and compare the difference between the actual restoration situation and the expected target; finally, based on the analysis results, the restoration plan is dynamically adjusted to improve the effect and efficiency of ecological restoration, while ensuring the continuous optimization of the restoration process through a regular feedback mechanism;

[0003] In order to accurately restore the ecology of abandoned mines, it is basically necessary to obtain ecological data about the target abandoned mines at fixed intervals for analysis and to formulate the next restoration plan. However, in the actual process, due to errors in the identified ecological data about the target abandoned mines, it may lead to incorrect judgments on the progress of the dynamic identification of the restoration of the target abandoned mines, thereby leading to incorrect judgments on the effect and efficiency of the ecological restoration of abandoned mines. Summary of the invention

[0004] The purpose of the present invention is to solve the problem and propose a dynamic identification method for ecological restoration of abandoned mines.

[0005] In the first aspect of the implementation of the present invention, a dynamic identification method for ecological restoration of abandoned mines is first proposed, the method comprising:

[0006] 1. A dynamic identification method for ecological restoration of abandoned mines, characterized in that it includes the following steps:

[0007] Obtaining the time interval for uploading ecological data of the target abandoned mine to obtain a target time interval, and dividing the target time interval according to a preset window to obtain a number of sub-intervals;

[0008] Obtain the consistency information of ecological data of the target abandoned mine in each sub-interval, and obtain the data inconsistency coefficient according to the consistency information;

[0009] Acquire electromagnetic interference data of the target abandoned mine in each sub-interval, and obtain an electromagnetic interference coefficient according to the electromagnetic interference data;

[0010] Obtain mutation information from ecological data of target abandoned mines in each sub-interval, and obtain data mutation coefficient according to the mutation information;

[0011] According to the data inconsistency coefficient, electromagnetic interference coefficient and data mutation coefficient in each sub-interval, the error coefficient of the sub-interval is obtained;

[0012] An error coefficient set is established based on the error coefficients in all subintervals, and all abnormal coefficients in the error coefficient set are screened out, and an abnormal set is established for all the error coefficients, and the standard deviation in the abnormal set is calculated, and the standard deviation is compared with the preset standard deviation value, and an alarm signal is issued according to the comparison result.

[0013] Optionally, the method for obtaining the data inconsistency coefficient is:

[0014] Get the total number of the same ecological data uploaded by each data source in each sub-interval and mark it as R 实 f , f represents the number of data sources, f = 1, 2, 3, 4, ..., c, c is a positive integer;

[0015] R 实 f Within the preset data upload quantity range (T 少 , T 多 ) are re-labeled as Q w , w represents R 实 f Within the preset data upload quantity range (T 少 , T 多 ) is numbered as follows: f=1, 2, 3, 4, ..., d, where d is a positive integer; the data inconsistency coefficient Nj is calculated as follows: Nj=ln(d / c+1).

[0016] Optionally, obtaining the electromagnetic interference coefficient according to the electromagnetic interference data includes:

[0017] Obtain the electromagnetic interference signals around the corresponding target abandoned mine at different times in each sub-interval when collecting ecological data of the target abandoned mine, and mark them as B(t), where t=1, 2, 3, 4, ..., Z, Z is a positive integer, and Z represents the number of electromagnetic interference signals;

[0018] Morlet wavelet is used to perform continuous wavelet transform on the electromagnetic interference signal B(t) at each moment. Through continuous wavelet transform (CWT), the electromagnetic interference signal is decomposed in the time domain and frequency domain. Continuous wavelet transform is realized through convolution, and its calculation formula is as follows: Where x(t) is the input signal, ψ(t) is the selected wavelet basis function, a is the scale parameter used to adjust the scale of the wavelet basis function; b is the translation parameter used to adjust the position of the wavelet basis function; t represents the time coordinate of the signal, that is, the position on the time axis;

[0019] For each scale a and translation b, the result calculated using wavelet transform is called wavelet coefficient;

[0020] Extract the energy characteristics of the wavelet coefficients. According to the calculated wavelet coefficients, further extract the energy characteristics, calculate the energy of the wavelet coefficients at each scale, and then perform weighted summation of the energies at different scales to obtain the comprehensive energy characteristics. The calculation formula is as follows: Where E(a) is the energy at scale a, CWT(a,b) is the wavelet coefficient;

[0021] Calculate the electromagnetic interference coefficient at each moment, and the calculation expression is: BK(t)=E(a) where BK(t) is the electromagnetic interference coefficient;

[0022] The electromagnetic interference coefficient BK(t) at different times in each sub-interval is added to obtain the electromagnetic interference coefficient. The calculation formula is: Where JM is the electromagnetic interference coefficient.

[0023] Optionally, obtaining a data mutation coefficient according to the mutation information includes:

[0024] Obtain the total number of uploaded ecological data about the target abandoned mine and the number of abnormal data from all data sources in each sub-interval, and obtain the data anomaly coefficient. The calculation formula is RF = tg / tp, where RF is the data anomaly coefficient, tg and tp are the total number of uploaded ecological data about the target abandoned mine and the number of abnormal data, respectively;

[0025] Obtain the number of tampered ecological data uploaded about the target abandoned mine from all data sources in each sub-interval, and obtain the data tampering coefficient. The calculation formula is QE = ty / tp, where QE is the data tampering coefficient and ty is ty;

[0026] Calculate the data mutation coefficient, the calculation formula is: Mgh = a1×RF+a2×QE; where Mgh is the data mutation coefficient, a1 and a2 represent the preset weight coefficients of the data anomaly quantity coefficient and the data tampering quantity coefficient, and a1 and a2 are both greater than 0.

[0027] Optionally, obtaining the error coefficient of each sub-interval according to the data inconsistency coefficient, the electromagnetic interference coefficient and the data mutation coefficient in the sub-interval includes:

[0028]

[0029] Wherein, Lgt is the error coefficient, Nj, JM and Mgh are the data inconsistency coefficient, electromagnetic interference coefficient and data mutation coefficient respectively, b1, b2 and b3 are the preset proportional coefficients of the data inconsistency coefficient, electromagnetic interference coefficient and data mutation coefficient respectively, and b1, b2 and b3 are all greater than 0.

[0030] Optionally, an error coefficient set is established based on the error coefficients in all subintervals, and all abnormal coefficients in the error coefficient set are screened out, including:

[0031] Establish an error coefficient set according to the error coefficients in all subintervals, and filter out all abnormal coefficients in the error coefficient set;

[0032] When the error coefficient is not less than the preset error coefficient reference threshold, the error coefficient is marked as an abnormal coefficient;

[0033] When the error coefficient is less than a preset error coefficient reference threshold, the error coefficient is marked as a normal coefficient.

[0034] Optionally, establishing an abnormal set for all error coefficients, calculating a standard deviation in the abnormal set, and comparing the standard deviation with a preset standard deviation value, and issuing an alarm signal according to the comparison result includes:

[0035] All error coefficients are used to establish an abnormal set, and the standard deviation in the abnormal set is calculated. The standard deviation is compared with the preset standard deviation value, and whether there is a problem with the system operation is determined based on the comparison result;

[0036] A number of error coefficients are used to establish a data set and calibrated as N, then W = {RY u}, where du = {1, 2, 3…t}, u is a positive integer;

[0037] And calculate the standard deviation of several error coefficients in the data set, and calibrate the standard deviation of several error coefficients in the data set as dsky, and compare the error coefficient standard deviation dsky with the preset error coefficient standard deviation threshold dskiy;

[0038] If the error coefficient standard deviation dsky is not less than the preset error coefficient standard deviation threshold dskiy, an alarm signal is issued;

[0039] If the error coefficient standard deviation dsky is less than the preset error coefficient standard deviation threshold dskiy, no alarm signal is issued.

[0040] Beneficial effects of the present invention:

[0041] The present invention proposes a dynamic identification method for ecological restoration of abandoned mines. The error coefficient of each sub-interval is obtained by obtaining the data inconsistency coefficient, electromagnetic interference coefficient and data mutation coefficient of the sub-interval, and an error coefficient set is established according to the error coefficients in all sub-intervals, and all abnormal coefficients in the error coefficient set are screened out, and an abnormal set is established for all the error coefficients, and the standard deviation in the abnormal set is calculated, and the standard deviation is compared with a preset standard deviation value, and an alarm signal is issued according to the comparison result. In this way, it is possible to timely judge whether there are errors in the ecological data of the target abandoned mine identified, and ensure that the possibility of errors in the dynamic identification of the restoration progress of the target abandoned mine is small, and will not affect the ecological restoration effect and efficiency of the target abandoned mine. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] The present invention will be further described below in conjunction with the accompanying drawings.

[0043] Figure 1 A flow chart of a dynamic identification method for ecological restoration of abandoned mines provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0044] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0045] Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in the field without making any creative work shall fall within the scope of protection of the present invention.

[0046] The embodiment of the present invention provides a dynamic identification method for ecological restoration of abandoned mines. Figure 1 , Figure 1 A flowchart of a dynamic identification method for ecological restoration of abandoned mines provided by an embodiment of the present invention. The method comprises the following steps:

[0047] Obtaining the time interval for uploading ecological data of the target abandoned mine to obtain a target time interval, and dividing the target time interval according to a preset window to obtain a number of sub-intervals;

[0048] Obtain the consistency information of ecological data of the target abandoned mine in each sub-interval, and obtain the data inconsistency coefficient according to the consistency information;

[0049] Acquire electromagnetic interference data of the target abandoned mine in each sub-interval, and obtain an electromagnetic interference coefficient according to the electromagnetic interference data;

[0050] Obtain mutation information from ecological data of target abandoned mines in each sub-interval, and obtain data mutation coefficient according to the mutation information;

[0051] According to the data inconsistency coefficient, electromagnetic interference coefficient and data mutation coefficient in each sub-interval, the error coefficient of the sub-interval is obtained;

[0052] An error coefficient set is established based on the error coefficients in all subintervals, and all abnormal coefficients in the error coefficient set are screened out, and an abnormal set is established for all the error coefficients, and the standard deviation in the abnormal set is calculated, and the standard deviation is compared with the preset standard deviation value, and an alarm signal is issued according to the comparison result.

[0053] In one embodiment, the method for obtaining the data inconsistency coefficient is:

[0054] Get the total number of the same ecological data uploaded by each data source in each sub-interval and mark it as R 实 f , f represents the number of data sources, f = 1, 2, 3, 4, ..., c, c is a positive integer;

[0055] R 实 f Within the preset data upload quantity range (T 少 , T 多 ) are re-labeled as Q w , w represents R 实 f Within the preset data upload quantity range (T 少 , T 多 ), f=1, 2, 3, 4, ..., d, d is a positive integer; calculate the data inconsistency coefficient Nj, the calculation formula is: Nj=ln(d / c+1);

[0056] It should be noted that the total number of the same ecological data uploaded by each data source in each sub-interval can be directly obtained through the internal monitoring system of each data source. In addition, the preset data upload quantity range is determined by professionals based on actual conditions and is not specifically limited.

[0057] It should be noted that the data inconsistency coefficient refers to the degree of inconsistency of the uploaded data when multiple data sources upload the same ecological data about the target abandoned mine. The greater the inconsistency of the uploaded data, the greater the error in the identified ecological data about the target abandoned mine, which may lead to incorrect judgments on the restoration progress of the dynamically identified target abandoned mine, and thus lead to incorrect judgments on the effect and efficiency of ecological restoration of abandoned mines. The reasons are:

[0058] Issues with the credibility and accuracy of data sources: Generally speaking, if multiple data sources collect the same ecological indicator (such as vegetation coverage, soil moisture, etc.), in theory, these data should be highly consistent within a certain range. If there are large inconsistencies between the data, it may indicate that the data quality of some data sources is poor, or that there are deviations in the data collection process. This deviation may be caused by factors such as sensor failure, environmental interference, and data transmission errors. If these errors are not identified, these problematic data may be regarded as real data, which will affect the judgment of the overall ecological status.

[0059] Data representativeness and sample authenticity issues: When there are significant differences in ecological data provided by different data sources, this means that these data may not reflect the ecological status of the target abandoned mines truthfully. For example, if some data sources report good vegetation recovery, while other data sources show poor recovery, this contradiction may indicate that the data is not representative or there are problems with data sample collection. If this is not distinguished, it may lead to overly optimistic or pessimistic ecological restoration assessment results.

[0060] Impact of error accumulation: In ecological monitoring, comprehensive analysis of multiple data sources is often used to form an overall assessment of the ecosystem. If there are significant inconsistencies between the various data sources, such errors may be amplified during the data fusion and analysis process, leading to misjudgments of the progress of ecological restoration. The accumulation of errors may cause the final assessment results to deviate from the actual situation, thereby affecting the formulation of restoration strategies.

[0061] Reliability issues in the dynamic identification process: The dynamic identification method relies on the consistency and accuracy of data to assess the progress of ecological restoration. If there are significant errors in the input data itself, the results of the entire dynamic identification process will also be affected. This may lead to incorrect restoration decisions, such as misjudging the restoration progress to be fast and reducing the restoration efforts, or misjudging the restoration progress to be slow and increasing unnecessary restoration investment, thus affecting the overall restoration effect and resource utilization efficiency.

[0062] Therefore, the data inconsistency coefficient is an important indicator that helps identify inconsistencies between multiple data sources, thereby discovering possible data errors at an early stage and avoiding these errors from having an adverse impact on the overall judgment and decision-making of ecological restoration.

[0063] In one embodiment, obtaining the electromagnetic interference coefficient according to the electromagnetic interference data includes:

[0064] Obtain the electromagnetic interference signals around the corresponding target abandoned mine at different times in each sub-interval when collecting ecological data of the target abandoned mine, and mark them as B(t), where t=1, 2, 3, 4, ..., Z, Z is a positive integer, and Z represents the number of electromagnetic interference signals;

[0065] Morlet wavelet is used to perform continuous wavelet transform on the electromagnetic interference signal B(t) at each moment. Through continuous wavelet transform (CWT), the electromagnetic interference signal is decomposed in the time domain and frequency domain. Continuous wavelet transform is realized through convolution, and its calculation formula is as follows: Where x(t) is the input signal, ψ(t) is the selected wavelet basis function, a is the scale parameter used to adjust the scale of the wavelet basis function; b is the translation parameter used to adjust the position of the wavelet basis function; t represents the time coordinate of the signal, that is, the position on the time axis;

[0066] For each scale a and translation b, the result calculated using wavelet transform is called wavelet coefficient;

[0067] Extract the energy characteristics of the wavelet coefficients. According to the calculated wavelet coefficients, further extract the energy characteristics, calculate the energy of the wavelet coefficients at each scale, and then perform weighted summation of the energies at different scales to obtain the comprehensive energy characteristics. The calculation formula is as follows: Where E(a) is the energy at scale a, CWT(a,b) is the wavelet coefficient;

[0068] Calculate the electromagnetic interference coefficient at each moment, and the calculation expression is: BK(t)=E(a) where BK(t) is the electromagnetic interference coefficient;

[0069] The electromagnetic interference coefficient BK(t) at different times in each sub-interval is added to obtain the electromagnetic interference coefficient. The calculation formula is: Where JM is the electromagnetic interference coefficient.

[0070] It should be noted that, in each sub-interval, when collecting ecological data of the target abandoned mine, the electromagnetic interference signals around the corresponding target abandoned mine at different times can be acquired by high-precision electromagnetic sensors around the target abandoned mine.

[0071] It should be noted that the electromagnetic interference coefficient refers to the degree of electromagnetic interference around the corresponding target abandoned mine at different times when the ecological data of the target abandoned mine is collected; if the degree of electromagnetic interference is greater, it means that there is an error in the ecological data of the target abandoned mine identified, which may lead to an erroneous judgment on the progress of the restoration of the target abandoned mine in the dynamic identification, thereby leading to an erroneous judgment on the effect and efficiency of the ecological restoration of the abandoned mine. The reasons are:

[0072] Sensitivity of data collection equipment: Many devices used for ecological data collection (such as sensors, data loggers, etc.) are very sensitive to electromagnetic interference. Electromagnetic interference may affect the normal operation of the equipment and cause inaccurate sensor readings. For example, electromagnetic interference may cause temperature sensor readings to fluctuate, humidity sensors to output incorrect values, or GPS positioning devices to offset. These inaccurate data will directly affect subsequent data analysis and judgment.

[0073] Interference in signal transmission: During data collection, sensors usually transmit data to the data recording center via wireless or wired means. If the electromagnetic interference in the surrounding environment is strong, signal transmission may be interfered with, resulting in the loss or erroneous transmission of data packets. This interference may make the final received data incomplete or incorrect, affecting the accurate assessment of ecological restoration.

[0074] Degradation of data quality: Electromagnetic interference may cause the quality of collected data to deteriorate, making the data contain more noise. High levels of noise can mask true ecological signals, making it difficult to identify small but important changes in the ecosystem. In this case, errors may be mistaken for real ecological changes, leading to misjudgments of the progress of ecological restoration.

[0075] Error accumulation and misjudgment: In the dynamic identification process, data is obtained based on the summary analysis of data from multiple time points or regions. If these data are affected by electromagnetic interference during the collection process, it may eventually lead to the accumulation of errors. Incorrect data input will lead to incorrect model predictions, which in turn will affect the judgment of the effect and efficiency of ecological restoration of abandoned mines. For example, if errors caused by electromagnetic interference are mistaken for signs of slow ecological recovery, it may prompt unnecessary further restoration actions and waste resources; conversely, it may also lead to the neglect of actual ecological problems.

[0076] Therefore, the electromagnetic interference coefficient is an important indicator for evaluating potential errors in the data collection process. By monitoring and analyzing the degree of electromagnetic interference, possible errors can be identified and corrected to ensure the accuracy of ecological data, thereby improving the accuracy of judgment on the progress of ecological restoration of abandoned mines and the effectiveness of restoration strategies.

[0077] In one embodiment, obtaining the data mutation coefficient according to the mutation information includes:

[0078] Obtain the total number of uploaded ecological data about the target abandoned mine and the number of abnormal data from all data sources in each sub-interval, and obtain the data anomaly coefficient. The calculation formula is RF = tg / tp, where RF is the data anomaly coefficient, tg and tp are the total number of uploaded ecological data about the target abandoned mine and the number of abnormal data, respectively;

[0079] Obtain the number of tampered ecological data uploaded about the target abandoned mine from all data sources in each sub-interval, and obtain the data tampering coefficient. The calculation formula is QE = ty / tp, where QE is the data tampering coefficient and ty is ty;

[0080] Calculate the data mutation coefficient, the calculation formula is: Mgh = a1×RF+a2×QE; where Mgh is the data mutation coefficient, a1 and a2 represent the preset weight coefficients of the data anomaly quantity coefficient and the data tampering quantity coefficient, and a1 and a2 are both greater than 0.

[0081] In one embodiment, obtaining the error coefficient of each sub-interval according to the data inconsistency coefficient, the electromagnetic interference coefficient, and the data mutation coefficient in the sub-interval includes:

[0082]

[0083] Wherein, Lgt is the error coefficient, Nj, JM and Mgh are the data inconsistency coefficient, electromagnetic interference coefficient and data mutation coefficient respectively, b1, b2 and b3 are the preset proportional coefficients of the data inconsistency coefficient, electromagnetic interference coefficient and data mutation coefficient respectively, and b1, b2 and b3 are all greater than 0.

[0084] In one embodiment, an error coefficient set is established based on the error coefficients in all subintervals, and all abnormal coefficients in the error coefficient set are screened out, including:

[0085] Establish an error coefficient set according to the error coefficients in all subintervals, and filter out all abnormal coefficients in the error coefficient set;

[0086] When the error coefficient is not less than the preset error coefficient reference threshold, the error coefficient is marked as an abnormal coefficient;

[0087] When the error coefficient is less than a preset error coefficient reference threshold, the error coefficient is marked as a normal coefficient.

[0088] In one implementation, the above-mentioned preset error coefficient reference threshold is set by professionals according to actual conditions, and the details are not repeated here.

[0089] In one embodiment, all error coefficients are used to establish an abnormal set, and the standard deviation in the abnormal set is calculated, and the standard deviation is compared with a preset standard deviation value, and an alarm signal is issued according to the comparison result, including:

[0090] All error coefficients are used to establish an abnormal set, and the standard deviation in the abnormal set is calculated. The standard deviation is compared with the preset standard deviation value, and whether there is a problem with the system operation is determined based on the comparison result;

[0091] A number of error coefficients are used to establish a data set and calibrated as N, then W = {RY u}, where du = {1, 2, 3…t}, u is a positive integer;

[0092] And calculate the standard deviation of several error coefficients in the data set, and calibrate the standard deviation of several error coefficients in the data set as dsky, and compare the error coefficient standard deviation dsky with the preset error coefficient standard deviation threshold dskiy;

[0093] If the error coefficient standard deviation dsky is not less than the preset error coefficient standard deviation threshold dskiy, an alarm signal is issued;

[0094] If the error coefficient standard deviation dsky is less than the preset error coefficient standard deviation threshold dskiy, no alarm signal is issued.

[0095] When an alarm signal is issued, it means that there are errors in the identified ecological data about the target abandoned mine, which may lead to incorrect judgments on the progress of the dynamic identification of the restoration of the target abandoned mine, and thus lead to incorrect judgments on the effect and efficiency of the ecological restoration of the abandoned mine, which may lead to problems in the setting of the ecological restoration plan for the target abandoned mine, resulting in the ecological restoration of the target abandoned mine failing to meet expectations. At this time, an alarm is immediately issued to remind the staff that the identified ecological data about the target abandoned mine is wrong, and therefore relevant treatment measures need to be taken in a timely manner to ensure that the ecological restoration of the target abandoned mine will not be affected.

[0096] The above is a detailed description of an embodiment of the present invention, but the content is only a preferred embodiment of the present invention and cannot be considered to limit the scope of implementation of the present invention. All equivalent changes and improvements made within the scope of the present invention should still fall within the scope of the patent coverage of the present invention.

Claims

1. A dynamic identification method for ecological restoration of abandoned mines, It is characterized in that The following steps are involved: Obtaining the time interval for uploading ecological data of the target abandoned mine to obtain a target time interval, and dividing the target time interval according to a preset window to obtain a number of sub-intervals; Obtain the consistency information of ecological data of the target abandoned mine in each sub-interval, and obtain the data inconsistency coefficient according to the consistency information; Acquire electromagnetic interference data of the target abandoned mine in each sub-interval, and obtain an electromagnetic interference coefficient according to the electromagnetic interference data; Obtain mutation information from ecological data of target abandoned mines in each sub-interval, and obtain data mutation coefficient according to the mutation information; According to the data inconsistency coefficient, electromagnetic interference coefficient and data mutation coefficient in each sub-interval, the error coefficient of the sub-interval is obtained; An error coefficient set is established based on the error coefficients in all subintervals, and all abnormal coefficients in the error coefficient set are screened out, and an abnormal set is established for all the error coefficients, and the standard deviation in the abnormal set is calculated, and the standard deviation is compared with the preset standard deviation value, and an alarm signal is issued according to the comparison result.

2. A dynamic identification method for ecological restoration of abandoned mines according to claim 1, It is characterized in that The method for obtaining the data inconsistency coefficient is: Get the total number of the same ecological data uploaded by each data source in each sub-interval and mark it as R 实 f , f represents the number of data sources, f = 1, 2, 3, 4, ..., c, c is a positive integer; R 实 f Within the preset data upload quantity range (T 少 , T 多 ) are re-labeled as Q w , w represents R 实 f Within the preset data upload quantity range (T 少 , T 多 ) is numbered as follows: f=1, 2, 3, 4, ..., d, where d is a positive integer; the data inconsistency coefficient Nj is calculated as follows: Nj=ln(d / c+1).

3. A dynamic identification method for ecological restoration of abandoned mines according to claim 1, It is characterized in that The electromagnetic interference coefficient obtained according to the electromagnetic interference data includes: Obtain the electromagnetic interference signals around the corresponding target abandoned mine at different times in each sub-interval when collecting ecological data of the target abandoned mine, and mark them as B(t), where t=1, 2, 3, 4, ..., Z, Z is a positive integer, and Z represents the number of electromagnetic interference signals; Morlet wavelet is used to perform continuous wavelet transform on the electromagnetic interference signal B(t) at each moment. Through continuous wavelet transform (CWT), the electromagnetic interference signal is decomposed in the time domain and frequency domain. Continuous wavelet transform is realized through convolution, and its calculation formula is as follows: Where x(t) is the input signal, ψ(t) is the selected wavelet basis function, a is the scale parameter used to adjust the scale of the wavelet basis function; b is the translation parameter used to adjust the position of the wavelet basis function; t represents the time coordinate of the signal, that is, the position on the time axis; For each scale a and translation b, the result calculated using wavelet transform is called wavelet coefficient; Extract the energy characteristics of the wavelet coefficients. According to the calculated wavelet coefficients, further extract the energy characteristics, calculate the energy of the wavelet coefficients at each scale, and then perform weighted summation of the energies at different scales to obtain the comprehensive energy characteristics. The calculation formula is as follows: Where E(a) is the energy at scale a, CWT(a, b) is the wavelet coefficient; Calculate the electromagnetic interference coefficient at each moment, and the calculation expression is: BK(t)=E(a) where BK(t) is the electromagnetic interference coefficient; The electromagnetic interference coefficient BK(t) at different times in each sub-interval is added to obtain the electromagnetic interference coefficient. The calculation formula is: Where JM is the electromagnetic interference coefficient.

4. A dynamic identification method for ecological restoration of abandoned mines according to claim 1, It is characterized in that The data mutation coefficients obtained according to the mutation information include: Obtain the total number of uploaded ecological data about the target abandoned mine and the number of abnormal data from all data sources in each sub-interval, and obtain the data anomaly coefficient. The calculation formula is RF = tg / t p , where RF is the data anomaly coefficient, tg and tp are the total number of uploaded ecological data about the target abandoned mine and the number of abnormal data, respectively; Obtain the number of tampered ecological data uploaded about the target abandoned mine from all data sources in each sub-interval, and obtain the data tampering coefficient. The calculation formula is QE = ty / tp, where QE is the data tampering coefficient and ty is ty; Calculate the data mutation coefficient, the calculation formula is: Mgh = a1×RF+a2×QE; where Mgh is the data mutation coefficient, a1 and a2 represent the preset weight coefficients of the data anomaly quantity coefficient and the data tampering quantity coefficient, and a1 and a2 are both greater than 0.

5. A dynamic identification method for ecological restoration of abandoned mines according to claim 1, It is characterized in that According to the data inconsistency coefficient, electromagnetic interference coefficient and data mutation coefficient in each sub-interval, the error coefficient of the sub-interval is obtained, including: Wherein, Lgt is the error coefficient, Nj, JM and Mgh are the data inconsistency coefficient, electromagnetic interference coefficient and data mutation coefficient respectively, b1, b2 and b3 are the preset proportional coefficients of the data inconsistency coefficient, electromagnetic interference coefficient and data mutation coefficient respectively, and b1, b2 and b3 are all greater than 0.

6. A dynamic identification method for ecological restoration of abandoned mines according to claim 1, It is characterized in that The error coefficient set is established based on the error coefficients in all subintervals, and all abnormal coefficients in the error coefficient set are screened out, including: Establish an error coefficient set according to the error coefficients in all subintervals, and filter out all abnormal coefficients in the error coefficient set; When the error coefficient is not less than the preset error coefficient reference threshold, the error coefficient is marked as an abnormal coefficient; When the error coefficient is less than a preset error coefficient reference threshold, the error coefficient is marked as a normal coefficient.

7. A dynamic identification method for ecological restoration of abandoned mines according to claim 1, It is characterized in that Establishing an abnormal set for all error coefficients, calculating the standard deviation in the abnormal set, and comparing the standard deviation with a preset standard deviation value, and issuing an alarm signal according to the comparison result includes: All error coefficients are used to establish an abnormal set, and the standard deviation in the abnormal set is calculated. The standard deviation is compared with the preset standard deviation value, and whether there is a problem with the system operation is determined based on the comparison result; A number of error coefficients are used to establish a data set and calibrated as N, then W = {RY u }, where du = {1, 2, 3…t}, and u is a positive integer; And calculate the standard deviation of several error coefficients in the data set, and calibrate the standard deviation of several error coefficients in the data set as dsky, and compare the error coefficient standard deviation dsky with the preset error coefficient standard deviation threshold dskiy; If the error coefficient standard deviation dsky is not less than the preset error coefficient standard deviation threshold dskiy, an alarm signal is issued; If the error coefficient standard deviation dsky is less than the preset error coefficient standard deviation threshold dskiy, no alarm signal is issued.

Images