A Method and System for Generating Dynamic Profiles of Coal Mine Production Behavior Based on Production Data

Abstract

The application discloses a coal mine production behavior dynamic portrait generation method and system based on production data, relates to the fully mechanized mining technical field, and comprises the following steps: collecting coal mine historical behavior data; analyzing the relationship between coal mine power consumption data and personnel positioning monitoring data at different time sequences, the relationship between coal mine power consumption data and equipment operation data at different time sequences, and the relationship between coal mine power consumption data and coal dynamic flow data at different time sequences; and constructing a mining intensity evaluation model; whether over-intensity production exists is judged according to the mining intensity evaluation model, and whether there is stealing mining exists is judged according to the relationship between personnel and power consumption. The system utilizes machine learning and data analysis technology, can comprehensively consider various influence factors, and generates a production behavior dynamic portrait of the coal mine in real time, which helps managers to instantly understand the operation condition of the mine site. The system reduces the dependence on traditional manual inspection, and solves the problems of low efficiency, incomplete coverage and high risk of manual inspection.

Description

Technical Field

[0001] This invention relates to the field of coal mine fully mechanized mining technology, and in particular to a method and system for generating dynamic profiles of coal mine production behavior based on production data. Background Technology

[0002] Monitoring coal mine production activities is crucial for ensuring the ecological safety of mining areas and preventing over-exploitation of resources. Non-compliant mining activities, such as overcapacity production and illegal mining, not only deplete national resources but may also trigger geological disasters and environmental pollution, posing a threat to public safety.

[0003] Compliant production monitoring helps improve the economic efficiency of coal mines and prevents excessive equipment wear and tear and decreased production efficiency caused by overproduction. Furthermore, by preventing illegal activities, such as unauthorized mining by shut-down mines, it protects the interests of legitimate coal companies and maintains market order.

[0004] Traditional manual inspection methods are inefficient and typically require a large amount of manpower, especially in vast mining areas. Inspectors need to check each piece of mining equipment and work area one by one, a process that is very time-consuming and difficult to perform frequently, thus affecting the effectiveness of real-time monitoring. Manual inspections rely on the experience and judgment of individual inspectors, which may lead to inconsistent results due to differences in individual subjective judgment and experience.

[0005] Coal mine production involves numerous pieces of equipment and processes, and data comes from diverse sources, including but not limited to sensor data, production logs, and surveillance videos. Effectively integrating these heterogeneous data sources presents a significant technical challenge. Illegal mining activities at shut-down coal mines are often highly concealed, making them difficult to detect and verify using traditional methods.

[0006] Monitoring systems need to not only handle large amounts of data input but also analyze this data in real time to detect abnormal behavior. This requires the system to have efficient data processing capabilities and a rapid response mechanism.

[0007] To address the aforementioned challenges, this invention proposes a method and system for generating dynamic profiles of coal mine production behavior based on production data. This system employs advanced data processing technologies, including machine learning models and Complex Event Processing (CEP) techniques, to achieve real-time and accurate monitoring of coal mine production behavior. The core of this invention lies in:

[0008] Model Design: Two specialized models will be developed to assess whether overproduction and illegal mining are occurring, respectively. These models will utilize historical and real-time data, employing pattern recognition and behavioral analysis to automatically predict and identify potential violations.

[0009] Dynamic profile generation: Convert monitoring data into dynamic profiles, providing an intuitive visual representation that enables managers to understand and respond to potential production issues in a timely manner. Summary of the Invention

[0010] In view of the above-mentioned problems, the present invention is proposed.

[0011] Therefore, the problem that this invention aims to solve is that traditional manual inspection methods are inefficient and usually require a large amount of human resources; effectively integrating heterogeneous data sources is a technical challenge.

[0012] To address the aforementioned technical problems, this invention provides the following technical solution: a method for generating dynamic profiles of coal mine production behavior based on production data, comprising: collecting historical coal mine behavior data, including coal mine electricity consumption data, personnel location monitoring data, equipment operation data, and coal dynamic flow data; analyzing the relationship between coal mine electricity consumption data and personnel location monitoring data under different time series to determine the relationship between personnel and electricity consumption; analyzing the relationship between coal mine electricity consumption data and equipment operation data under different time series to determine the relationship between main mining operations and electricity consumption; analyzing the relationship between coal mine electricity consumption data and coal dynamic flow data under different time series to determine the relationship between transportation and electricity consumption; constructing a mining intensity assessment model based on the relationships between personnel and electricity consumption, main mining operations and electricity consumption, and transportation and electricity consumption; determining whether over-intensity production exists based on the mining intensity assessment model, and determining whether illegal mining exists based on the relationship between personnel and electricity consumption.

[0013] As a preferred embodiment of the dynamic profile generation method for coal mine production behavior based on production data described in this invention, the determination of the relationship between personnel and electricity consumption includes analyzing the relationship between coal mine electricity consumption data and personnel location monitoring data under different time series to obtain the relationship formula E between personnel factors and electricity consumption. r (t) is represented as,

[0014]

[0015] Among them, e r Let N(τ) represent the average electricity consumption per unit of personnel, N(τ) represent the number of personnel at time τ, and W(τ) represent the workload function, Δt i Let represent the time difference between the i-th worker's ascent and descent from the well, and τ represent the integral variable, indicating any time point within the time interval from t0 to t; the work intensity function W(τ) is specifically expressed as,

[0016] W(τ)=H(τ)·(I(τ)·Ψ(τ,N(τ)))·η(τ)·(1+sin(ωτ+φ))

[0017]

[0018] η(τ)=e -λτ

[0019] Where H(τ) represents the number of working hours at time τ, I(τ) represents the labor intensity index, which is obtained based on the physical requirements of the work, Ψ(τ,N) represents the dynamic adjustment factor, which takes into account the impact of the number of personnel on the workload, z represents an index that reflects the impact of work allocation on individual workload, η(τ) represents a decay function that simulates the decay of efficiency over time, λ represents the decay coefficient, and ω and φ represent the frequency and phase of the sine function, which respectively determine the period and starting point of the change in work intensity.

[0020] As a preferred embodiment of the dynamic profile generation method for coal mine production behavior based on production data described in this invention, the determination of the relationship between main mining operations and electricity consumption includes analyzing the relationship between coal mine electricity consumption data and equipment operation data under different time series to obtain the relationship formula E between main mining operations and electricity consumption. s (t) is represented as,

[0021]

[0022] Where α and β represent the power consumption of basic start-stop and additional start-stop, respectively, F(τ) represents the number of basic start-stop cycles of the device within time τ, and L(τ) represents the operating load of the device within time τ. thresh This is represented as the load threshold. When this threshold is exceeded, additional start / stop operations are required to protect the equipment. δ(L(τ),L) thresh ) can be represented as a conditional logic function, where L(τ) > L thresh δ = 1, otherwise δ = 0, e s R(τ) represents the average power consumption per unit of equipment, and R(τ) represents the operating time of the equipment at time τ.

[0023] As a preferred embodiment of the method for generating dynamic profiles of coal mine production behavior based on production data according to the present invention, the step of determining the relationship between transportation and electricity consumption includes determining the relationship E between transportation and electricity consumption based on the dynamic flow of coal within the mine. y (t) is represented as,

[0024]

[0025] Where Q(τ) represents the dynamic flow rate of coal at time τ, v(τ) represents the coal flow rate, θ represents the adjustment parameter, which is used in the basic formula to balance the impact of the rate on electricity consumption, σ represents the coefficient of the logarithmic term, which is used to increase the adjustment of electricity consumption at high flow rates, and e y It is expressed as the average electricity consumption per unit flow.

[0026] As a preferred embodiment of the dynamic profile generation method for coal mine production behavior based on production data described in this invention, the construction of the mining intensity assessment model includes relationship correction and model construction, wherein the relationship correction includes converting the historical total electricity consumption E... z (t) minus E s (t) and E y (t), to obtain the difference in electricity consumption E c (t), denoted as E c (t)=E z (t)-E s (t)-E y (t), the difference in electricity consumption E c (t) and E r (t) The difference and absolute value are used to obtain the error power E. w (t), the error power consumption E w Compare (t) with the minimum permissible error l, if E w If (t) is greater than l, then the error electricity consumption will be evenly distributed and added to E. r (t), E s (t), E y In (t), for e r e s e y Make corrections to meet the new E r (t), E s (t), E y (t).

[0027] As a preferred embodiment of the dynamic profile generation method for coal mine production behavior based on production data described in this invention, the model construction includes: constructing a mining intensity assessment model based on the corrected electricity consumption relationship, expressed as follows:

[0028] E j (t)=W1E r (t)+W2E s (t)+W3E y (t)

[0029] Wherein, it is represented as used for e r e s e y Corrected weighting factors.

[0030] As a preferred embodiment of the dynamic profile generation method for coal mine production behavior based on production data described in this invention, the following steps are included: Determining whether over-intensity production is occurring based on a mining intensity assessment model involves arranging new behavioral data chronologically and inputting it into the mining intensity assessment model to obtain the estimated electricity consumption for each time-series behavioral data. The estimated electricity consumption is then compared with a maximum electricity consumption threshold. If the estimated consumption is less than the maximum electricity consumption threshold, the corresponding data point for that time-series is marked in green; if it is greater than the maximum electricity consumption threshold, the corresponding data point for that time-series is marked in red. Finally, the behavioral data is arranged chronologically into a red-green curve using a dynamic profile and input to the terminal. Determining whether illegal mining exists based on the relationship between personnel and electricity consumption involves substituting the behavioral data from when the mine is shut down into the E... r In (t), the estimated electricity consumption of personnel is obtained. The estimated electricity consumption of personnel is compared with the minimum electricity consumption threshold. If it is less than the minimum electricity consumption threshold, the data point corresponding to this time sequence is marked in green. If it is greater than the minimum electricity consumption threshold, the data point corresponding to this time sequence is marked in yellow. Finally, the behavior data of the mining shutdown is arranged into a yellow-green curve according to the time sequence through dynamic profiling and input into the terminal.

[0031] Another objective of this invention is to provide a dynamic profile generation system for coal mine production behavior based on production data. This system can estimate electricity consumption based on the behavior data of mining enterprises and determine whether the mining enterprise is engaged in excessive production or illegal mining based on the estimated electricity consumption.

[0032] To address the aforementioned technical problems, this invention provides the following technical solution: a system for generating dynamic profiles of coal mine production behavior based on production data, comprising: a data acquisition module, a model building module, and a judgment module; the data acquisition module collects historical coal mine behavior data, including coal mine electricity consumption data, personnel location monitoring data, equipment operation data, and coal dynamic flow data; the model building module analyzes the relationship between coal mine electricity consumption data and personnel location monitoring data under different time series to determine the relationship between personnel and electricity consumption; analyzes the relationship between coal mine electricity consumption data and equipment operation data under different time series to determine the relationship between main mining operations and electricity consumption; analyzes the relationship between coal mine electricity consumption data and coal dynamic flow data under different time series to determine the relationship between transportation and electricity consumption; based on the relationship between personnel and electricity consumption, the relationship between main mining operations and electricity consumption, and the relationship between transportation and electricity consumption, a mining intensity assessment model is constructed; the judgment module judges whether over-intensity production exists based on the mining intensity assessment model, and judges whether illegal mining exists based on the relationship between personnel and electricity consumption.

[0033] A computer device includes a memory and a processor, the memory storing a computer program, and the processor executing the computer program to implement the steps of the method for generating dynamic profiles of coal mine production behavior based on production data as described above.

[0034] A computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the method for generating dynamic profiles of coal mine production behavior based on production data as described above.

[0035] The beneficial effects of this invention are as follows: By collecting and analyzing coal mine production data (such as equipment operation data, electricity consumption data, coal dynamic flow data, etc.) in real time, the system of this invention can generate a dynamic profile of coal mine production behavior in real time. This helps managers to understand the mine's operating status in an instant, discover problems in a timely manner, and make rapid responses.

[0036] The system of this invention utilizes machine learning and data analysis technologies to comprehensively consider various influencing factors, such as equipment load, operating frequency, and personnel activities, thereby providing comprehensive data support for mine management, assisting decision-makers in making scientific decisions, and optimizing production plans and resource allocation.

[0037] This invention reduces reliance on traditional manual inspections by automating data collection and analysis, solving problems such as low efficiency, incomplete coverage, and high risk associated with manual inspections, which is especially important in harsh or dangerous environments. Attached Figure Description

[0038] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the description of the embodiments 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. Wherein:

[0039] Figure 1 This is a flowchart of the method for generating dynamic profiles of coal mine production behavior based on production data in Example 1.

[0040] Figure 2 This is a module structure diagram of the dynamic profile generation system for coal mine production behavior based on production data in Example 3. Detailed Implementation

[0041] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings.

[0042] Many specific details are set forth in the following description in order to provide a full understanding of the invention. However, the invention may also be practiced in other ways different from those described herein, and those skilled in the art can make similar extensions without departing from the spirit of the invention. Therefore, the invention is not limited to the specific embodiments disclosed below.

[0043] Example 1, referring to Figure 1 This is the first embodiment of the present invention, which provides a method for generating dynamic profiles of coal mine production behavior based on production data, including, for example... Figure 1 As shown:

[0044] Step 1: Collect historical behavior data of the coal mine, including coal mine electricity consumption data, personnel location monitoring data, equipment operation data, and coal dynamic flow data.

[0045] Step 2: Analyze the relationship between coal mine electricity consumption data and personnel location monitoring data under different time series to determine the relationship between personnel and electricity consumption.

[0046] Analyzing the relationship between coal mine electricity consumption data and personnel location monitoring data at different time series, the relationship between personnel factors and electricity consumption, E, is obtained. r (t) is represented as,

[0047]

[0048] Among them, e r Let N(τ) represent the average electricity consumption per unit of personnel, N(τ) represent the number of personnel at time τ, and W(τ) represent the workload function, Δt i Let t represent the time difference between the i-th worker's entry and exit from the well, and let τ represent the integral variable, which represents any point in time within the time interval from t0 to t.

[0049] The work intensity function W(τ) is specifically expressed as follows:

[0050] W(τ)=H(τ)·(I(τ)·Ψ(τ,N(τ)))·η(τ)·(1+sin(ωτ+φ))

[0051]

[0052] η(τ)=e -λτ

[0053] Where H(τ) represents the number of working hours at time τ, I(τ) represents the labor intensity index, which is obtained based on the physical requirements of the work, Ψ(τ,N) represents the dynamic adjustment factor, which takes into account the impact of the number of personnel on the workload, z represents an index that reflects the impact of work allocation on individual workload, η(τ) represents a decay function that simulates the decay of efficiency over time, λ represents the decay coefficient, and ω and φ represent the frequency and phase of the sine function, which respectively determine the period and starting point of the change in work intensity.

[0054] Step 3: Analyze the relationship between coal mine electricity consumption data and equipment operation data under different time series to determine the relationship between main mining operations and electricity consumption.

[0055] Analyzing the relationship between coal mine electricity consumption data and equipment operation data at different time series, the relationship between main mining operations and electricity consumption, E, is obtained. s (t) is represented as,

[0056]

[0057] Where α and β represent the power consumption of basic start-stop and additional start-stop, respectively, F(τ) represents the number of basic start-stop cycles of the device within time τ, and L(τ) represents the operating load of the device within time τ. thresh This is represented as the load threshold. When this threshold is exceeded, additional start / stop operations are required to protect the equipment. δ(L(τ),L) thresh ) can be represented as a conditional logic function, where L(τ) > L thresh δ = 1, otherwise δ = 0, e s R(τ) represents the average power consumption per unit of equipment, and R(τ) represents the operating time of the equipment at time τ.

[0058] Step 4: Analyze the relationship between coal mine electricity consumption data and coal dynamic flow data under different time series to determine the relationship between transportation and electricity consumption.

[0059] Based on the dynamic flow of coal within the mine, determine the relationship between transportation and electricity consumption (E). y (t) is represented as,

[0060]

[0061] Where Q(τ) represents the dynamic flow rate of coal at time τ, v(τ) represents the coal flow rate, θ represents the adjustment parameter, which is used in the basic formula to balance the impact of the rate on electricity consumption, σ represents the coefficient of the logarithmic term, which is used to increase the adjustment of electricity consumption at high flow rates, and e y It is expressed as the average electricity consumption per unit flow.

[0062] Step 5: Based on the relationship between personnel and electricity consumption, the relationship between main mining operations and electricity consumption, and the relationship between transportation and electricity consumption, construct a mining intensity assessment model.

[0063] Constructing a mining intensity assessment model includes relationship correction and model building. Relationship correction includes incorporating historical total electricity consumption E. z (t) minus E s (t) and E y (t), to obtain the difference in electricity consumption E c (t), denoted as E c (t)=E z (t)-E s (t)-E y (t), the difference in electricity consumption E c (t) and Er (t) The difference and absolute value are used to obtain the error power E. w (t), the error power consumption E w Compare (t) with the minimum permissible error l, if E w If (t) is greater than l, then the error electricity consumption will be evenly distributed and added to E. r (t), E s (t), E y In (t), for e r e s e y Make corrections to meet the new E r (t), E s (t), E y (t). Using E r (t) and E c (t) The reason for the comparison is that the electricity consumption of personnel is more unstable compared to the electricity consumption of equipment collection and coal transportation, so E is used. r (t) and E c (t) comparison can better reflect the error.

[0064] The mining intensity assessment model is constructed based on the modified electricity consumption relationship, and is expressed as follows:

[0065] E j (t)=W1E r (t)+W2E s (t)+W3E y (t)

[0066] Wherein, it is represented as used for e r e s e y Corrected weighting factors.

[0067] Step 6: Determine whether there is over-intensity production based on the mining intensity assessment model, and determine whether there is illegal mining based on the relationship between personnel and electricity consumption.

[0068] The new behavioral data is arranged in chronological order and fed into the mining intensity assessment model to obtain the estimated electricity consumption for each time-series behavioral data. The estimated electricity consumption is compared with the maximum electricity consumption threshold. If it is less than the maximum electricity consumption threshold, the corresponding data point for this time series is marked in green. If it is greater than the maximum electricity consumption threshold, the corresponding data point for this time series is marked in red. Finally, the behavioral data is arranged in chronological order into a red and green curve through dynamic profiling and input into the terminal.

[0069] Determining whether illegal mining exists based on the relationship between personnel and electricity consumption includes substituting behavioral data from when mining operations cease into E. rIn (t), the estimated electricity consumption of personnel is obtained. The estimated electricity consumption of personnel is compared with the minimum electricity consumption threshold. If it is less than the minimum electricity consumption threshold, the data point corresponding to this time sequence is marked in green. If it is greater than the minimum electricity consumption threshold, the data point corresponding to this time sequence is marked in yellow. Finally, the behavior data of the mining shutdown is arranged into a yellow-green curve according to the time sequence through dynamic profiling and input into the terminal.

[0070] Relevant personnel can perform visual operations on the terminal, which helps improve processing efficiency.

[0071] Example 2 is the second embodiment of the present invention. It differs from the first embodiment in that the method for generating dynamic profiles of coal mine production behavior based on production data further includes, in order to verify and explain the technical effects adopted in this method, a comparative test is conducted between the traditional technical solution and the method of the present invention, and the test results are compared using scientific demonstration methods to verify the real effect of the method.

[0072] Experimental objective: To verify the advantages of the method of the present invention over traditional manual inspection in terms of efficiency, accuracy, cost-effectiveness, safety, and monitoring coverage.

[0073] Experimental setup:

[0074] Efficiency and response time: Simulate the time required for both methods to complete an inspection of the entire mine within a typical production cycle.

[0075] Accuracy and error rate: Record the false alarm and false negative rates of the two methods when monitoring production activities (such as overcapacity production, illegal mining).

[0076] Security statistics: Records of security incidents during monitoring using two methods.

[0077] Monitoring Coverage: Comparing the capabilities of the two methods in achieving comprehensive mine monitoring coverage. The experimental data obtained are shown in Table 1:

[0078] Table 1: Comparison of Experimental Data

[0079]

[0080]

[0081] Response time: The method of the present invention significantly reduces response time through real-time data analysis and automatic alarm, thereby improving the speed of handling emergencies.

[0082] Accuracy, false alarm rate and false negative rate: Due to the use of advanced data analysis and machine learning techniques, the method of this invention has higher accuracy and lower error rate when detecting mining production activities.

[0083] Safety incidents: By reducing personnel exposure to hazardous environments and monitoring potential risks in real time, the method of this invention can significantly reduce the incidence of safety incidents.

[0084] Coverage: Compared to manual inspection, the method of this invention can achieve more comprehensive coverage of the mine through networked sensors and monitoring equipment.

[0085] Example 3, referring to Figure 2 This is the third embodiment of the present invention, which differs from the previous two embodiments in that: a system for generating dynamic profiles of coal mine production behavior based on production data includes a data acquisition module, a model building module, and a judgment module; the data acquisition module collects historical behavior data of the coal mine, including coal mine electricity consumption data, personnel location monitoring data, equipment operation data, and coal dynamic flow data; the model building module analyzes the relationship between coal mine electricity consumption data and personnel location monitoring data under different time series to determine the relationship between personnel and electricity consumption; analyzes the relationship between coal mine electricity consumption data and equipment operation data under different time series to determine the relationship between main mining operations and electricity consumption; analyzes the relationship between coal mine electricity consumption data and coal dynamic flow data under different time series to determine the relationship between transportation and electricity consumption; based on the relationship between personnel and electricity consumption, the relationship between main mining operations and electricity consumption, and the relationship between transportation and electricity consumption, a mining intensity assessment model is constructed; the judgment module judges whether there is over-intensity production based on the mining intensity assessment model, and judges whether there is illegal mining based on the relationship between personnel and electricity consumption.

[0086] If the aforementioned functions are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0087] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a sequenced list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (such as a computer-based system, a processor-included system, or other system that can fetch and execute instructions from, an instruction execution system, apparatus, or device). For the purposes of this specification, "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transmit programs for use by, or in conjunction with, an instruction execution system, apparatus, or device.

[0088] More specific examples of computer-readable media (a non-exhaustive list) include: electrical connections (electronic devices) having one or more wires, portable computer disk drives (magnetic devices), random access memory (RAM), read-only memory (ROM), erasable and editable read-only memory (EPROM or flash memory), fiber optic devices, and portable optical disc read-only memory (CDROM). Furthermore, computer-readable media can even be paper or other suitable media on which the program can be printed, because the program can be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, interpreting, or otherwise processing as necessary, and then stored in computer memory.

[0089] It should be understood that various parts of the present invention can be implemented in hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented in software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, it can be implemented using any one or a combination of the following techniques known in the art: discrete logic circuits having logic gates for implementing logical functions on data signals, application-specific integrated circuits (ASICs) having suitable combinational logic gates, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc.

[0090] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A method for generating a dynamic portrait of a coal mine production behavior based on production data, characterized in that: Comprising, collecting historical behavior data of the coal mine, the historical behavior data comprising coal mine power consumption data, personnel positioning monitoring data, equipment operation data, and coal dynamic flow data; analyzing the relationship between the coal mine power consumption data and the personnel positioning monitoring data at different time sequences to determine the relationship between personnel and power consumption; analyzing the relationship between the coal mine power consumption data and the equipment operation data at different time sequences to determine the relationship between main mining work and power consumption; analyzing the relationship between the coal mine power consumption data and the coal dynamic flow data at different time sequences to determine the relationship between transportation and power consumption; constructing a mining intensity evaluation model based on the relationship between personnel and power consumption, the relationship between main mining work and power consumption, and the relationship between transportation and power consumption; determining whether the production is over-intensity according to the mining intensity evaluation model, and determining whether there is illegal mining according to the relationship between personnel and power consumption; The determining the relationship between the personnel and the power consumption includes analyzing the relationship between the coal mine power consumption data and the personnel positioning monitoring data at different time sequences to obtain a relationship formula E between the personnel factor and the power consumption r (t) is represented as, where e r represents the average power consumption per person, N(τ) represents the number of persons at time τ, W(τ) represents the work intensity function, Δt i represents the time difference between the uphole and downhole times for the i-th worker, τ represents the integration variable, and t represents an arbitrary time point within the time period from t0 to t; The work intensity function W(τ) is specifically represented as, W(τ) = H(τ) · (I(τ) · Ψ(τ, N)) · η(τ) · (1 + sin(ωτ + φ)) η(τ) = e -λτ wherein H(τ) represents the working hours at time τ, I(τ) represents a labor intensity index, which is obtained based on physical requirements of work, Ψ(τ, N) represents a dynamic adjustment factor, which takes into account the influence of the number of personnel on work load, z represents an index reflecting the influence of work allocation on individual work load, η(τ) represents a decay function, which simulates the decay of efficiency over time, λ represents a decay coefficient, ω and φ represent the frequency and phase of the sine function, respectively, which determine the period and starting point of the change in work intensity; The determining the relationship between the main mining work and the power consumption includes analyzing the relationship between the coal mine power consumption data and the equipment operation data at different time sequences to obtain a relationship formula E between the main mining work of the equipment and the power consumption s (t) is represented as, where a, b represent the power consumption of basic start-stop and extra start-stop, respectively, F(τ) represents the number of basic start-stop of the device within time τ, L(τ) represents the running load of the device at time τ, L thresh represents a load threshold value, and when the threshold value is exceeded, start-stop needs to be increased to protect the device, δ(L(τ), L thresh ) represents a conditional logic function, when L(τ) > L thresh , δ = 1, otherwise δ = 0, e s represents the average power consumption of a unit device, and R(τ) represents the running time of the device at time τ; The determining the relationship between the transportation and the power consumption includes determining the relationship E between the transportation and the power consumption according to the dynamic flow amount of the coal in the mine y (t) is represented as, where Q(τ) represents the dynamic flow of coal at time τ, v(τ) represents the flow rate of coal, θ represents a tuning parameter for balancing the rate versus power consumption influence of the base formula, σ represents a coefficient for the logarithmic term to increase the tuning of power consumption for high flow rates, and e y represents the average power consumption per unit flow.

2. The method for generating a dynamic picture of a coal mine production behavior based on production data according to claim 1, characterized in that: The construction of the mining intensity assessment model includes relationship correction and model construction. The relationship correction includes adjusting the historical total electricity consumption E. z (t) minus E s (t) and E y (t), to obtain the difference in electricity consumption E c (t), denoted as E c (t)=E z (t)-E s (t)-E y (t), the difference in electricity consumption E c (t) and E r (t) The difference and absolute value are used to obtain the error power E. w (t), the error power consumption E w (t) and minimum permissible error For comparison, if E w (t) is greater than Then the error power consumption is evenly distributed and added to E. r (t), E s (t), E y In (t), for e r e s e y Make corrections to meet the new E r (t), E s (t), E y (t).

3. The method for generating a dynamic picture of a coal mine production behavior based on production data according to claim 2, characterized in that: The model construction comprises constructing a mining intensity evaluation model based on the corrected power consumption relationship, which is represented as, E j (t) = W1E r (t) + W2E s (t) + W3E y (t) wherein W1, W2, W3 are respectively defined as the weight factors for the e r , e s , e y corrected weight factors.

4. The method for generating a dynamic picture of a coal mine production behavior based on production data according to claim 3, characterized in that: The determination of whether the production is over-intensity according to the mining intensity evaluation model comprises arranging the new behavior data in time sequence and inputting it into the mining intensity evaluation model to obtain the predicted power consumption of each time sequence behavior data, comparing the predicted power consumption with the maximum power consumption threshold, marking the data points corresponding to the time sequence as green if the predicted power consumption is less than the maximum power consumption threshold, marking the data points corresponding to the time sequence as red if the predicted power consumption is greater than the maximum power consumption threshold, and finally arranging the behavior data in time sequence as a red and green alternating curve through dynamic imaging and inputting it into the terminal; The determining whether there is stealing according to the relationship between the personnel and the power consumption includes substituting the behavior data of the mine production stop into the E r In (t), the expected personnel power consumption is obtained, the expected personnel power consumption is compared with the minimum power threshold, if less than the minimum power threshold, the data point corresponding to this time sequence is marked green, if greater than the minimum power threshold, the data point corresponding to this time sequence is marked yellow, finally the behavior data of the mine production stop is arranged in time sequence as a yellow-green alternating curve by dynamic image, and input to the terminal.

5. A system for generating a dynamic portrait of a coal mine production behavior based on production data using the method according to any one of claims 1 to 4, characterized in that: comprising a data collection module, a model construction module, and a determination module; The data collection module collects historical behavior data of the coal mine, the historical behavior data comprising coal mine power consumption data, personnel positioning monitoring data, equipment operation data, and coal dynamic flow data; The model construction module analyzes the relationship between the coal mine power consumption data and the personnel positioning monitoring data at different time sequences to determine the relationship between personnel and power consumption; analyzes the relationship between the coal mine power consumption data and the equipment operation data at different time sequences to determine the relationship between main mining work and power consumption; and analyzes the relationship between the coal mine power consumption data and the coal dynamic flow data at different time sequences to determine the relationship between transportation and power consumption; constructing a mining intensity evaluation model based on the relationship between personnel and power consumption, the relationship between main mining work and power consumption, and the relationship between transportation and power consumption; The judging module judges whether the production is over-intensity production according to a mining intensity evaluation model and judges whether there is stealing mining according to the relationship between the personnel and the power consumption. 6.A computer device, comprising a memory and a processor, wherein the memory stores a computer program, and the computer device is characterized in that: The processor implements the steps of the coal mine production behavior dynamic portrait generation method based on production data in any one of claims 1 to 4 when executing the computer program.

7. A computer readable storage medium having stored thereon a computer program, characterized in that: The computer program implements the steps of the coal mine production behavior dynamic portrait generation method based on production data in any one of claims 1 to 4 when executed by the processor.

Images