Remote weighing system and weighing method for mining

Abstract

The invention discloses a remote weighing system and weighing method for mining, and relates to the field of mining. Comprising a weighing module which is arranged at a mining transfer node and specifically comprises a rigid hopper and a weighing sensor group fixed at the bottom of the rigid hopper; the local control module comprises a signal processing module used for converting a weighing analog signal into a digital signal, an environment compensation module used for correcting data according to the environment and a communication module; the remote server is connected with the communication module of the local control module through an encrypted communication network to realize remote communication; the terminal display device receives and displays the weighing result in real time; according to the invention, by combining the environmental data, the acquired weight data and environmental data are compensated by using the compensation algorithm, so that the weighing error of the weighing sensor caused by the environmental influence can be prevented, and the weighing precision is improved.

Description

Technical Field

[0001] This invention relates to the field of mining, and more particularly to a remote weighing system and weighing method for mining. Background Technology

[0002] In mining production and operation, accurate weighing of mined materials is a core aspect of production statistics, cost accounting, and trade settlement.

[0003] The current mainstream approach is to install mechanical weighbridges or electronic scales at transfer points, where operators record the tare weight and gross weight of the vehicle on-site and then calculate the net weight.

[0004] This model has the following drawbacks:

[0005] First, personnel need to frequently approach loading areas and other high-dust, high-noise environments to work, and the movement of mine cars and the rolling of ore can easily cause work-related accidents.

[0006] Secondly, manual records need to be transcribed into the management system twice, which can easily lead to transcription errors or even data tampering.

[0007] Therefore, this invention proposes a remote weighing system and weighing method for mining. Summary of the Invention

[0008] The purpose of this invention is to address the shortcomings of existing technologies by proposing a remote weighing system and weighing method for mining.

[0009] To achieve the above objectives, the present invention adopts the following technical solution:

[0010] A remote weighing system for mining, comprising:

[0011] The weighing module, which is set at the node of mining transfer, specifically includes a rigid hopper and a set of weighing sensors fixed to the bottom of the rigid hopper;

[0012] The local control module includes a signal processing module for converting weighing analog signals into digital signals, an environmental compensation module for correcting data according to the environment, and a communication module.

[0013] A remote server connects to the communication module of the local control module via an encrypted communication network to achieve remote communication;

[0014] The terminal display device receives and displays the weighing results in real time.

[0015] Preferably, the environmental compensation module includes an environmental sensor group and an environmental compensation model disposed at the weighing module.

[0016] Preferably, the environmental sensor group includes any one or more combinations of temperature sensors, humidity sensors, wind speed and direction sensors, and air dust concentration sensors.

[0017] A remote weighing method for mining includes the following steps:

[0018] S1: Weighing. At the mining transfer node, the material to be weighed is transferred into a rigid hopper. The weighing sensor group collects the weight information and transmits the collected weight information to the local control module.

[0019] S2: The local control module converts the analog signal of the weight information into a digital signal, collects environmental information through the environmental sensor group, and compensates the digital signal by combining the environmental compensation model to obtain the compensated weight digital signal.

[0020] S3: The local control module encrypts and transmits the compensated weight digital signal to the remote server for storage via the communication module. At the same time, the remote server receives the signal, decodes it, and then transmits it to the terminal display device for weight display.

[0021] Preferably, in step S2, the logic for environmental compensation is as follows:

[0022] S21: Establish a model Δ for the difference between the actual and measured weighing values ​​for each environmental dimension (the environmental dimension corresponds to the sensor types included in the environmental sensor group). P i =f i (P i ), P i Let P be the environmental sensor data in the i-th dimension, and Δ be the data in the i-th dimension. P i f is the difference between the actual and measured weight values ​​when the environmental sensor data for the i-th dimension is P. i () represents the functional relationship between the environmental sensor data value and the difference between the actual weighing value and the measured value in the i-th dimension;

[0023] S22: Collect environmental data from different dimensions using environmental sensors to form an environmental data set {P1, ..., P}. i , ..., P n};

[0024] S23: According to the formula Calculate the overall variance;

[0025] S24: Obtain the actual value measured by the digitized weighing sensor, and then calculate the difference between the actual value and the difference value to obtain the compensated theoretical value.

[0026] Preferably, in step S21, the model building method is the same for each environmental dimension, which includes the following steps:

[0027] S211: Determine the range of environmental data variation based on actual operating conditions (P) min P max ), and set n-2 environmental data collection points at equal intervals within the range of variation;

[0028] S212: Establish a mechanism to transfer corresponding environmental data from P min To P max The experimental space was altered, and the weighing sensor was placed within the experimental space.

[0029] S213: Place a mass block with a standard weight of Q at the sensing end of the weighing sensor, and obtain the weighing value Q′ through the weighing sensor. Calculate the difference Δ=QQ′.

[0030] S214: Change the experimental environment within the experimental space from P min Start until P max End, and obtain Δ from n environmental data collection points;

[0031] S215: Establish a two-dimensional coordinate system for environmental data and Δ, input n collection points into the coordinate system, and then fit the curve to obtain the model expression.

[0032] Preferably, in step S215, the curve fitting is performed using Taylor series expansion or least squares method.

[0033] Preferably: in step S23, k i is the weight value of the i-th dimension, which is directly proportional to the accuracy of the environmental sensor.

[0034] Preferably, in step S3, the encryption method includes the following steps:

[0035] S31: Collect m encryption algorithms, number the m encryption algorithms according to 1-m, and form an encryption set;

[0036] S32: Divide the transmitted data into random blocks and extract the data characteristics T of each data block;

[0037] S33: Normalize the data feature T to scale or expand it to the (1, m) interval;

[0038] S34: Encrypt the data block using an encryption algorithm that matches the normalized data feature T number.

[0039] Preferably, in step S33, the data feature T is any one of the following: the number of characters, the number of 0s / 1s in the binary data, or the difference / sum of 0s and 1s in the binary data.

[0040] The beneficial effects of this invention are as follows:

[0041] 1. This invention utilizes a weighing sensor for on-site weighing, and then uses a local control module as a medium for data processing and transmission, thereby achieving remote weighing through a terminal display device. Simultaneously, the data can be saved online through a remote server.

[0042] 2. This invention combines environmental data and uses a compensation algorithm to compensate for the collected weight data and environmental data, thereby preventing weighing errors caused by environmental influences on the weighing sensor and increasing weighing accuracy.

[0043] 3. The present invention adopts an experimental approach to model building, inputting the obtained data points into a coordinate system, and then fitting the curve to obtain the model expression, thereby increasing the accuracy of model building and its fit with the real situation, and further increasing the accuracy of weighing.

[0044] 4. This invention increases the security of data transmission by encrypting the data. The encryption adopts a random encryption method, which randomly divides the data into blocks, extracts features from each data block, and selects an encryption algorithm based on the features. This allows different data blocks to use different encryption methods, thereby increasing the difficulty of cracking and further enhancing data security. Attached Figure Description

[0045] Figure 1 This is a diagram of a remote weighing system architecture for mining proposed in this invention;

[0046] Figure 2 This is a flowchart of a remote weighing method for mining proposed in this invention. Detailed Implementation

[0047] The technical solution of the present invention will be further described in detail below with reference to specific embodiments.

[0048] Example 1:

[0049] A remote weighing system for mining, comprising:

[0050] The weighing module, which is set at the node of mining transfer, specifically includes a rigid hopper and a set of weighing sensors fixed to the bottom of the rigid hopper;

[0051] The local control module includes a signal processing module for converting weighing analog signals into digital signals, an environmental compensation module for correcting data according to the environment, and a communication module.

[0052] A remote server connects to the communication module of the local control module via an encrypted communication network to achieve remote communication;

[0053] The terminal display device receives and displays the weighing results in real time.

[0054] The environmental compensation module includes an environmental sensor group and an environmental compensation model installed at the weighing module.

[0055] The environmental sensor group includes a temperature sensor, a humidity sensor, a wind speed and direction sensor, and an air dust concentration sensor.

[0056] This invention utilizes a weighing sensor for on-site weighing, and then uses a local control module as a medium for data processing and transmission, thereby achieving remote weighing through a terminal display device and enabling online data storage through a remote server.

[0057] Example 2:

[0058] A remote weighing method for mining includes the following steps:

[0059] S1: Weighing. At the mining transfer node, the material to be weighed is transferred into a rigid hopper. The weighing sensor group collects the weight information and transmits the collected weight information to the local control module.

[0060] S2: The local control module converts the analog signal of the weight information into a digital signal, collects environmental information through the environmental sensor group, and compensates the digital signal by combining the environmental compensation model to obtain the compensated weight digital signal.

[0061] S3: The local control module encrypts and transmits the compensated weight digital signal to the remote server for storage via the communication module. At the same time, the remote server receives the signal, decodes it, and then transmits it to the terminal display device for weight display.

[0062] In step S2, the logic for environmental compensation is as follows:

[0063] S21: Establish a model Δ for the difference between the actual and measured weighing values ​​for each environmental dimension (the environmental dimension corresponds to the sensor types included in the environmental sensor group). P i =f i (P i ), P i Let P be the environmental sensor data in the i-th dimension, and Δ be the data in the i-th dimension. P i f is the difference between the actual and measured weight values ​​when the environmental sensor data for the i-th dimension is P. i() represents the functional relationship between the environmental sensor data value and the difference between the actual weighing value and the measured value in the i-th dimension;

[0064] S22: Collect environmental data from different dimensions using environmental sensors to form an environmental data set {P1, ..., P}. i ,......,P n};

[0065] S23: According to the formula Calculate the overall variance;

[0066] S24: Obtain the actual value measured by the digitized weighing sensor, and then calculate the difference between the actual value and the difference value to obtain the compensated theoretical value.

[0067] This invention combines environmental data and uses a compensation algorithm to compensate for the collected weight data and environmental data, thereby preventing weighing errors caused by environmental influences on the weighing sensor and increasing weighing accuracy.

[0068] Example 3:

[0069] A remote weighing method for mining includes the following steps:

[0070] S1: Weighing. At the mining transfer node, the material to be weighed is transferred into a rigid hopper. The weighing sensor group collects the weight information and transmits the collected weight information to the local control module.

[0071] S2: The local control module converts the analog signal of the weight information into a digital signal, collects environmental information through the environmental sensor group, and compensates the digital signal by combining the environmental compensation model to obtain the compensated weight digital signal.

[0072] S3: The local control module encrypts and transmits the compensated weight digital signal to the remote server for storage via the communication module. At the same time, the remote server receives the signal, decodes it, and then transmits it to the terminal display device for weight display.

[0073] In step S2, the logic for environmental compensation is as follows:

[0074] S21: Establish a model Δ for the difference between the actual and measured weighing values ​​for each environmental dimension (the environmental dimension corresponds to the sensor types included in the environmental sensor group). P i =f i (P i ), P i Let P be the environmental sensor data in the i-th dimension, and Δ be the data in the i-th dimension. P i f is the difference between the actual and measured weight values ​​when the environmental sensor data for the i-th dimension is P.i () represents the functional relationship between the environmental sensor data value and the difference between the actual weighing value and the measured value in the i-th dimension;

[0075] S22: Collect environmental data from different dimensions using environmental sensors to form an environmental data set {P1, ..., P}. i , ..., P n};

[0076] S23: According to the formula Calculate the overall variance;

[0077] S24: Obtain the actual value measured by the digitized weighing sensor, and then calculate the difference between the actual value and the difference value to obtain the compensated theoretical value.

[0078] In step S21, the model building method is the same for each environmental dimension, and it includes the following steps:

[0079] S211: Determine the range of environmental data variation based on actual operating conditions (P) min P max ), and set n-2 environmental data collection points at equal intervals within the range of variation;

[0080] S212: Establish a mechanism to transfer corresponding environmental data from P min To P max The experimental space was altered, and the weighing sensor was placed within the experimental space.

[0081] S213: Place a mass block with a standard weight of Q at the sensing end of the weighing sensor, and obtain the weighing value Q′ through the weighing sensor. Calculate the difference Δ=QQ′.

[0082] S214: Change the experimental environment within the experimental space from P min Start until P max End, and obtain Δ from n environmental data collection points;

[0083] S215: Establish a two-dimensional coordinate system for environmental data and Δ, input n collection points into the coordinate system, and then fit the curve to obtain the model expression.

[0084] In step S215, the curve fitting uses Taylor series expansion fitting.

[0085] In step S23, k i is the weight value of the i-th dimension, which is directly proportional to the accuracy of the environmental sensor.

[0086] Example 4:

[0087] A remote weighing method for mining includes the following steps:

[0088] S1: Weighing. At the mining transfer node, the material to be weighed is transferred into a rigid hopper. The weighing sensor group collects the weight information and transmits the collected weight information to the local control module.

[0089] S2: The local control module converts the analog signal of the weight information into a digital signal, collects environmental information through the environmental sensor group, and compensates the digital signal by combining the environmental compensation model to obtain the compensated weight digital signal.

[0090] S3: The local control module encrypts and transmits the compensated weight digital signal to the remote server for storage via the communication module. At the same time, the remote server receives the signal, decodes it, and then transmits it to the terminal display device for weight display.

[0091] In step S2, the logic for environmental compensation is as follows:

[0092] S21: Establish a model Δ for the difference between the actual and measured weighing values ​​for each environmental dimension (the environmental dimension corresponds to the sensor types included in the environmental sensor group). P i =f i (P i ), P i Let P be the environmental sensor data in the i-th dimension, and Δ be the data in the i-th dimension. P i f is the difference between the actual and measured weight values ​​when the environmental sensor data for the i-th dimension is P. i () represents the functional relationship between the environmental sensor data value and the difference between the actual weighing value and the measured value in the i-th dimension;

[0093] S22: Collect environmental data from different dimensions using environmental sensors to form an environmental data set {P1, ..., P}. i ,......,P n};

[0094] S23: According to the formula Calculate the overall variance;

[0095] S24: Obtain the actual value measured by the digitized weighing sensor, and then calculate the difference between the actual value and the difference value to obtain the compensated theoretical value.

[0096] In step S21, the model building method is the same for each environmental dimension, and it includes the following steps:

[0097] S211: Determine the range of environmental data variation based on actual operating conditions (P) min P max ), and set n-2 environmental data collection points at equal intervals within the range of variation;

[0098] S212: Establish a mechanism to transfer corresponding environmental data from P min To P max The experimental space was altered, and the weighing sensor was placed within the experimental space.

[0099] S213: Place a mass block with a standard weight of Q at the sensing end of the weighing sensor, and obtain the weighing value Q′ through the weighing sensor. Calculate the difference Δ=QQ′.

[0100] S214: Change the experimental environment within the experimental space from P min Start until P max End, and obtain Δ from n environmental data collection points;

[0101] S215: Establish a two-dimensional coordinate system for environmental data and Δ, input n collection points into the coordinate system, and then fit the curve to obtain the model expression.

[0102] In step S215, the curve fitting uses the least squares method.

[0103] In step S23, k i is the weight value of the i-th dimension, which is directly proportional to the accuracy of the environmental sensor.

[0104] This invention employs an experimental approach to model building, inputting the obtained data points into a coordinate system and then fitting the curve to obtain the model expression. This increases the accuracy of model building and its fit with the real situation, further enhancing the accuracy of weighing.

[0105] Example 5:

[0106] A remote weighing method for mining includes the following steps:

[0107] S1: Weighing. At the mining transfer node, the material to be weighed is transferred into a rigid hopper. The weighing sensor group collects the weight information and transmits the collected weight information to the local control module.

[0108] S2: The local control module converts the analog signal of the weight information into a digital signal, collects environmental information through the environmental sensor group, and compensates the digital signal by combining the environmental compensation model to obtain the compensated weight digital signal.

[0109] S3: The local control module encrypts and transmits the compensated weight digital signal to the remote server for storage via the communication module. At the same time, the remote server receives the signal, decodes it, and then transmits it to the terminal display device for weight display.

[0110] In step S2, the logic for environmental compensation is as follows:

[0111] S21: Establish a model Δ for the difference between the actual and measured weighing values ​​for each environmental dimension (the environmental dimension corresponds to the sensor types included in the environmental sensor group). P i =f i (P i ), P i Let P be the environmental sensor data in the i-th dimension, and Δ be the data in the i-th dimension. P i f is the difference between the actual and measured weight values ​​when the environmental sensor data for the i-th dimension is P. i () represents the functional relationship between the environmental sensor data value and the difference between the actual weighing value and the measured value in the i-th dimension;

[0112] S22: Collect environmental data from different dimensions using environmental sensors to form an environmental data set {P1, ..., P}. i ,......,P n};

[0113] S23: According to the formula Calculate the overall variance;

[0114] S24: Obtain the actual value measured by the digitized weighing sensor, and then calculate the difference between the actual value and the difference value to obtain the compensated theoretical value.

[0115] In step S21, the model building method is the same for each environmental dimension, and it includes the following steps:

[0116] S211: Determine the range of environmental data variation based on actual operating conditions (P) min P max ), and set n-2 environmental data collection points at equal intervals within the range of variation;

[0117] S212: Establish a mechanism to transfer corresponding environmental data from P min To P max The experimental space was altered, and the weighing sensor was placed within the experimental space.

[0118] S213: Place a mass block with a standard weight of Q at the sensing end of the weighing sensor, and obtain the weighing value Q′ through the weighing sensor. Calculate the difference Δ=QQ′.

[0119] S214: Change the experimental environment within the experimental space from P min Start until P max End, and obtain Δ from n environmental data collection points;

[0120] S215: Establish a two-dimensional coordinate system for environmental data and Δ, input n collection points into the coordinate system, and then fit the curve to obtain the model expression.

[0121] In step S215, the curve fitting is performed using Taylor series expansion or least squares method.

[0122] In step S23, k i is the weight value of the i-th dimension, which is directly proportional to the accuracy of the environmental sensor.

[0123] In step S3, the encryption method includes the following steps:

[0124] S31: Collect m encryption algorithms, number the m encryption algorithms according to 1-m, and form an encryption set;

[0125] S32: Divide the transmitted data into random blocks and extract the data characteristics T of each data block;

[0126] S33: Normalize the data feature T to scale or expand it to the (1, m) interval;

[0127] S34: Encrypt the data block using an encryption algorithm that matches the normalized data feature T number.

[0128] Example 6:

[0129] A remote weighing method for mining includes the following steps:

[0130] S1: Weighing. At the mining transfer node, the material to be weighed is transferred into a rigid hopper. The weighing sensor group collects the weight information and transmits the collected weight information to the local control module.

[0131] S2: The local control module converts the analog signal of the weight information into a digital signal, collects environmental information through the environmental sensor group, and compensates the digital signal by combining the environmental compensation model to obtain the compensated weight digital signal.

[0132] S3: The local control module encrypts and transmits the compensated weight digital signal to the remote server for storage via the communication module. At the same time, the remote server receives the signal, decodes it, and then transmits it to the terminal display device for weight display.

[0133] In step S2, the logic for environmental compensation is as follows:

[0134] S21: Establish a model Δ for the difference between the actual and measured weighing values ​​for each environmental dimension (the environmental dimension corresponds to the sensor types included in the environmental sensor group). P i =f i (P i ), P i Let P be the environmental sensor data in the i-th dimension, and Δ be the data in the i-th dimension.P i f is the difference between the actual and measured weight values ​​when the environmental sensor data for the i-th dimension is P. i () represents the functional relationship between the environmental sensor data value and the difference between the actual weighing value and the measured value in the i-th dimension;

[0135] S22: Collect environmental data from different dimensions using environmental sensors to form an environmental data set {P1, ..., P}. i ,......,P n};

[0136] S23: According to the formula Calculate the overall variance;

[0137] S24: Obtain the actual value measured by the digitized weighing sensor, and then calculate the difference between the actual value and the difference value to obtain the compensated theoretical value.

[0138] In step S21, the model building method is the same for each environmental dimension, and it includes the following steps:

[0139] S211: Determine the range of environmental data variation based on actual operating conditions (P) min P max ), and set n-2 environmental data collection points at equal intervals within the range of variation;

[0140] S212: Establish a mechanism to transfer corresponding environmental data from P min To P max The experimental space was altered, and the weighing sensor was placed within the experimental space.

[0141] S213: Place a mass block with a standard weight of Q at the sensing end of the weighing sensor, and obtain the weighing value Q′ through the weighing sensor. Calculate the difference Δ=QQ′.

[0142] S214: Change the experimental environment within the experimental space from P mix Start until P max End, and obtain Δ from n environmental data collection points;

[0143] S215: Establish a two-dimensional coordinate system for environmental data and Δ, input n collection points into the coordinate system, and then fit the curve to obtain the model expression.

[0144] In step S215, the curve fitting is performed using Taylor series expansion or least squares method.

[0145] In step S23, k i is the weight value of the i-th dimension, which is directly proportional to the accuracy of the environmental sensor.

[0146] In step S3, the encryption method includes the following steps:

[0147] S31: Collect m encryption algorithms, number the m encryption algorithms according to 1-m, and form an encryption set;

[0148] S32: Divide the transmitted data into random blocks and extract the data characteristics T of each data block;

[0149] S33: Normalize the data feature T to scale or expand it to the (1, m) interval;

[0150] S34: Encrypt the data block using an encryption algorithm that matches the normalized data feature T number.

[0151] In step S33, the data feature T is any one of the following: the number of characters, the number of 0s / 1s in the binary data, or the difference / sum of 0s and 1s in the binary data.

[0152] This invention increases the security of data transmission by encrypting the data. The encryption adopts a random encryption method, which randomly divides the data into blocks, extracts features from each data block, and selects an encryption algorithm based on the features. This allows different data blocks to use different encryption methods, thereby increasing the difficulty of cracking and further enhancing data security.

[0153] The above description is only a preferred embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any equivalent substitutions or modifications made by those skilled in the art within the scope of the technology disclosed in the present invention, based on the technical solution and inventive concept of the present invention, should be covered within the scope of protection of the present invention.

Claims

1. A remote weighing system for mining, characterized in that, include: A weighing module, which is installed at a mining transfer node, includes a rigid hopper and a set of weighing sensors fixed to the bottom of the rigid hopper; The local control module includes a signal processing module for converting weighing analog signals into digital signals, an environmental compensation module for correcting data according to the environment, and a communication module. A remote server connects to the communication module of the local control module via an encrypted communication network to achieve remote communication; The terminal display device receives and displays the weighing results in real time.

2. The remote weighing system for mining according to claim 1, characterized in that, The environmental compensation module includes an environmental sensor group and an environmental compensation model installed at the weighing module.

3. A remote weighing system for mining according to claim 2, characterized in that, The environmental sensor group includes any one or more combinations of temperature sensors, humidity sensors, wind speed and direction sensors, and air dust concentration sensors.

4. A remote weighing method for mining, comprising the weighing method of a remote weighing system for mining as described in any one of claims 1-3, characterized in that, Includes the following steps: S1: Weighing. At the mining transfer node, the material to be weighed is transferred into a rigid hopper. The weighing sensor group collects the weight information and transmits the collected weight information to the local control module. S2: The local control module converts the analog signal of the weight information into a digital signal, collects environmental information through the environmental sensor group, and compensates the digital signal by combining the environmental compensation model to obtain the compensated weight digital signal. S3: The local control module encrypts and transmits the compensated weight digital signal to the remote server for storage via the communication module. At the same time, the remote server receives the signal, decodes it, and then transmits it to the terminal display device for weight display.

5. A remote weighing method for mining according to claim 4, characterized in that, In step S2, the logic for environmental compensation is as follows: S21: Establish a model Δ for the difference between the actual and measured weighing values ​​for each environmental dimension (the environmental dimension corresponds to the sensor types included in the environmental sensor group). P i =f i (P i ), P i Let P be the environmental sensor data in the i-th dimension, and Δ be the data in the i-th dimension. P i f is the difference between the actual and measured weight values ​​when the environmental sensor data for the i-th dimension is P. i () represents the functional relationship between the environmental sensor data value and the difference between the actual weighing value and the measured value in the i-th dimension; S22: Collect environmental data from different dimensions using environmental sensors to form an environmental data set {P1, ... P2}. i ,......,P n }; S23: According to the formula Calculate the overall variance; S24: Obtain the actual value measured by the digitized weighing sensor, and then calculate the difference between the actual value and the difference value to obtain the compensated theoretical value.

6. A remote weighing method for mining according to claim 5, characterized in that, In step S21, the model building method is the same for each environmental dimension, and it includes the following steps: S211: Determine the range of environmental data variation based on actual operating conditions (P) min P max ), and set n-2 environmental data collection points at equal intervals within the range of variation; S212: Establish a mechanism to transfer corresponding environmental data from P min To P max The experimental space was altered, and the weighing sensor was placed within the experimental space. S213: Place a mass block with a standard weight of Q at the sensing end of the weighing sensor, and obtain the weighing value Q′ through the weighing sensor. Calculate the difference Δ=QQ′. S214: Change the experimental environment within the experimental space from P min Start until P max End, obtaining Δ from n environmental data collection points; S215: Establish a two-dimensional coordinate system for environmental data and Δ, input n collection points into the coordinate system, and then fit the curve to obtain the model expression.

7. A remote weighing method for mining according to claim 6, characterized in that, In step S215, the curve fitting is performed using Taylor series expansion or least squares method.

8. A remote weighing method for mining according to claim 5, characterized in that, In step S23, k i is the weight value of the i-th dimension, which is directly proportional to the accuracy of the environmental sensor.

9. A remote weighing method for mining according to claim 4, characterized in that, In step S3, the encryption method includes the following steps: S31: Collect m encryption algorithms, number the m encryption algorithms according to 1-m, and form an encryption set; S32: Divide the transmitted data into random blocks and extract the data characteristics T of each data block; S33: Normalize the data feature T to scale or expand it to the (1, m) interval; S34: Encrypt the data block using an encryption algorithm that matches the normalized data feature T number.

10. A remote weighing method for mining according to claim 9, characterized in that, In step S33, the data feature T is any one of the following: the number of characters, the number of 0s / 1s in the binary data, or the difference / sum of 0s and 1s in the binary data.

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