Coal mine production behavior dynamic portrait generation method and system based on production data

Abstract

The invention discloses a coal mine production behavior dynamic portrait generation method and system based on production data, and relates to the technical field of coal mine fully mechanized mining. Analyzing the relationship between the coal mine power consumption data and the personnel positioning monitoring data under different time sequences, the relationship between the coal mine power consumption data and the equipment operation data under different time sequences, and the relationship between the coal mine power consumption data and the coal dynamic flow data under different time sequences, and constructing a mining strength evaluation model; and judging whether super-intensity production exists or not according to the mining intensity judgment model, and judging whether illegal mining exists or not according to the relationship between personnel and electricity consumption. According to the system, machine learning and data analysis technologies are utilized, various influence factors can be comprehensively considered, and a production behavior dynamic portrait of a coal mine is generated in real time, so that a manager is helped to immediately know the operation condition of a mine field. Dependence on traditional manual inspection is reduced, and the problems that manual inspection is low in efficiency, incomplete in coverage, high in risk and the like are solved.

Description

Technical Field

[0001] The present invention relates to the technical field of coal mine fully mechanized mining, and in particular to a method and system for generating a dynamic portrait of coal mine production behavior based on production data. Background Art

[0002] Monitoring of coal mine production behavior is crucial to ensuring the ecological and environmental safety of mining areas and avoiding over-exploitation of resources. Illegal mining activities, such as over-capacity production and illegal mining, not only consume national resources, but may also cause geological disasters and environmental pollution, posing a threat to public safety.

[0003] Compliance production monitoring helps improve the economic benefits of coal mines and prevent excessive wear and tear of equipment and reduced production efficiency due to over-intensive production. In addition, by preventing illegal activities such as illegal mining in suspended coal mines, the interests of legitimate coal companies can be protected and market order can be maintained.

[0004] Traditional manual inspection methods are inefficient and usually require a lot of manpower, especially in vast mining areas. Inspectors need to check mining equipment and operating areas one by one, which is a very time-consuming process and difficult to carry out frequently, thus affecting the effect of real-time monitoring. Manual inspections rely on the experience and judgment of individual inspectors, which may produce inconsistent results due to personal subjective judgment and experience differences.

[0005] Coal mining involves many devices and links, and the data sources are diverse, including but not limited to sensor data, production logs, and surveillance videos. How to effectively integrate these heterogeneous data sources is a technical challenge. The illegal mining in closed coal mines is usually highly concealed and difficult to discover and confirm through traditional methods.

[0006] The monitoring system needs to be able to not only process a large amount of data input, but also analyze the data in real time to detect abnormal behavior. This requires the system to have efficient data processing capabilities and a fast response mechanism.

[0007] In order to solve the above challenges, the present invention proposes a method and system for generating dynamic portraits of coal mine production behavior based on production data. The system adopts advanced data processing technology, including machine learning models and complex event processing (CEP) technology, to achieve real-time and accurate monitoring of coal mine production behavior. The core of the present invention is:

[0008] Model design: Develop two specialized models to assess whether there is overproduction and whether there is illegal mining. These models will use historical and real-time data to automatically predict and identify potential violations through pattern recognition and behavioral analysis.

[0009] Dynamic portrait generation: Convert the monitored data into dynamic portraits, providing intuitive visual representations so that managers can understand and respond to potential production problems 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 to be solved by the present invention is that the traditional manual inspection method is inefficient and usually requires a large amount of human resources; and effectively integrating heterogeneous data sources is a technical challenge.

[0012] To solve the above technical problems, the present invention provides the following technical solutions: a method for generating a dynamic portrait of coal mine production behavior based on production data, which includes collecting historical behavior data of coal mines, wherein the historical behavior data includes coal mine electricity consumption data, personnel positioning monitoring data, equipment operation data and coal dynamic flow data; analyzing the relationship between coal mine electricity consumption data and personnel positioning monitoring data at different time series, and determining the relationship between personnel and electricity consumption; analyzing the relationship between coal mine electricity consumption data and equipment operation data at different time series, and determining the relationship between main mining work and electricity consumption; analyzing the relationship between coal mine electricity consumption data and coal dynamic flow data at different time series, and determining the relationship between transportation and electricity consumption; constructing a mining intensity evaluation model based on the relationship between personnel and electricity consumption, the relationship between main mining work and electricity consumption, and the relationship between transportation and electricity consumption; judging whether there is over-intensity production according to the mining intensity evaluation model, and judging whether there is illegal mining according to the relationship between personnel and electricity consumption.

[0013] As a preferred solution of the method for generating a dynamic portrait of coal mine production behavior based on production data of the present invention, wherein: the determination of the relationship between personnel and power consumption includes analyzing the relationship between coal mine power consumption data and personnel positioning monitoring data under different time series, and obtaining the relationship between personnel factors and power consumption E r (t) is expressed as,

[0014]

[0015] Among them, e r is the average power consumption per person, N(τ) is the number of people per time τ, W(τ) is the work intensity function, Δt i It is expressed as the time difference between the i-th worker going up and down the well, τ is expressed as an integral variable, representing any time point in the time period 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] Among them, H(τ) is the number of working hours in time τ, I(τ) is the labor intensity index, which is obtained based on the physical requirements of the work, Ψ(τ,N) is the dynamic adjustment factor, which takes into account the impact of the number of people on the workload, z is an index that reflects the impact of work distribution on individual workload, η(τ) is an attenuation function that simulates the attenuation of efficiency over time, λ is the attenuation coefficient, ω and φ are 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 solution of the method for generating a dynamic portrait of coal mine production behavior based on production data of the present invention, wherein: the determination of 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 under different time sequences, and obtaining the relationship formula E between the main mining work of the equipment and the power consumption s (t) is expressed as,

[0021]

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

[0023] As a preferred solution of the method for generating a dynamic portrait of coal mine production behavior based on production data of the present invention, wherein: the determination of the relationship between transportation and power consumption includes determining the relationship between transportation and power consumption according to the dynamic flow of coal in the mine. y (t) is expressed as,

[0024]

[0025] Among them, Q(τ) represents the dynamic flow of coal at time τ, v(τ) represents the coal flow rate, θ represents the adjustment parameter, which is the basic formula for the influence of the balance rate on power consumption, σ represents the coefficient of the logarithmic term, which is used to increase the regulation of power consumption at high flow, and e y Expressed as the average electricity consumption per unit flow.

[0026] As a preferred solution of the method for generating a dynamic portrait of coal mine production behavior based on production data of the present invention, the construction of the mining intensity evaluation model includes relationship correction and model construction, and the relationship correction includes converting the historical total electricity consumption E z (t) minus E s (t) and E y (t), and obtain the difference in power consumption E c (t), denoted as E c (t) = E z (t)-E s (t)-E y (t), the difference in power consumption E c (t) and E r (t) Make the difference and take the absolute value to get the error power consumption E w (t), the error power consumption E w (t) is compared with the minimum allowable error l. If E w If (t) is greater than l, the error power consumption is added equally to E r (t), E s (t), E y (t), for e r 、e s 、e y Modifications were made to meet the new E r (t), E s (t), E y (t).

[0027] As a preferred solution of the method for generating a dynamic portrait of coal mine production behavior based on production data of the present invention, the model construction includes constructing a mining intensity evaluation model based on the corrected electricity consumption relationship, which is expressed as:

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

[0029] Among them, it is expressed as the value used for e r 、e s 、e y Modified weight factor.

[0030] As a preferred solution of the method for generating a dynamic portrait of coal mine production behavior based on production data described in the present invention, wherein: the judging whether there is over-intensity production according to the mining intensity judgment model includes arranging the new behavior data in time series and bringing it into the mining intensity judgment model to obtain the estimated power consumption of each time series behavior data, comparing the estimated power consumption with the maximum power consumption threshold, if it is less than the maximum power consumption threshold, marking the data point corresponding to this time series in green, if it is greater than the maximum power consumption threshold, marking the data point corresponding to this time series in red, and finally arranging the behavior data in time series into a red and green curve through dynamic portrait, and inputting it into the terminal; the judging whether there is illegal mining according to the relationship between personnel and power consumption includes substituting the behavior data when the mining industry is shut down into E r (t), the estimated power consumption of personnel is obtained, and the estimated power consumption of personnel is compared with the minimum power consumption threshold. If it is less than the minimum power consumption threshold, the data point corresponding to this time series is marked green; if it is greater than the minimum power consumption threshold, the data point corresponding to this time series is marked yellow. Finally, the behavior data during the mining shutdown is arranged in time series into a yellow and green curve through dynamic portrait, and input into the terminal.

[0031] Another object of the present invention is to provide a system for generating dynamic portraits of coal mine production behavior based on production data. This system can estimate electricity consumption based on the behavior data of the mining enterprise, and determine whether the mining enterprise has engaged in over-intensity production or illegal mining based on the estimated electricity consumption.

[0032] To solve the above technical problems, the present invention provides the following technical solutions: a system for generating a dynamic portrait 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 behavior data of coal mines, and the historical behavior data includes coal mine electricity consumption data, personnel positioning 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 positioning monitoring data at different time series, and determines the relationship between personnel and electricity consumption; analyzes the relationship between coal mine electricity consumption data and equipment operation data at different time series, and determines the relationship between main mining work and electricity consumption; analyzes the relationship between coal mine electricity consumption data and coal dynamic flow data at different time series, and determines the relationship between transportation and electricity consumption; based on the relationship between personnel and electricity consumption, the relationship between main mining work and electricity consumption, and the relationship between transportation and electricity consumption, a mining intensity evaluation model is constructed; the judgment module determines whether it is over-intensity production according to the mining intensity evaluation model, and determines whether there is illegal mining according to the relationship between personnel and electricity consumption.

[0033] A computer device includes a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, the steps of the method for generating a dynamic portrait of coal mine production behavior based on production data as described above are implemented.

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

[0035] The beneficial effects of the present invention are as follows: by collecting and analyzing the production data of coal mines (such as equipment operation data, electricity consumption data, coal dynamic flow data, etc.) in real time, the system of the present invention can generate a dynamic portrait of the production behavior of the coal mine in real time, which helps managers to instantly understand the operating status of the mine, discover problems in time and respond quickly.

[0036] The system of the present invention utilizes machine learning and data analysis technology to comprehensively consider various influencing factors, such as equipment load, operating frequency, personnel activities, etc., so as to provide comprehensive data support for mine management, assist decision makers in making scientific decisions, and optimize production plans and resource allocation.

[0037] The present invention reduces the reliance on traditional manual inspections through automated data collection and analysis, and solves the problems of low efficiency, incomplete coverage, and high risk of manual inspections, which is particularly important in harsh or dangerous environments. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for describing the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work. Among them:

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

[0040] Figure 2 This is a module structure diagram of the system for generating dynamic portraits of coal mine production behavior based on production data in Example 3. DETAILED DESCRIPTION

[0041] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the accompanying drawings.

[0042] In the following description, many specific details are set forth to facilitate a full understanding of the present invention, but the present invention may also be implemented in other ways different from those described herein, and those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0043] Example 1, reference Figure 1 , which is the first embodiment of the present invention, and provides a method for generating a dynamic portrait of coal mine production behavior based on production data, including: Figure 1 As shown:

[0044] Step 1: Collect historical behavior data of coal mines, including 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 positioning monitoring data at different time series to determine the relationship between personnel and electricity consumption.

[0046] The relationship between coal mine electricity consumption data and personnel positioning monitoring data under different time series is analyzed, and the relationship between personnel factors and electricity consumption is obtained. r (t) is expressed as,

[0047]

[0048] Among them, e r is the average power consumption per person, N(τ) is the number of people per time τ, W(τ) is the work intensity function, Δt i It is expressed as the time difference of the i-th worker going up and down the well, and τ is expressed as an integral variable, representing any time point in the time period from t0 to t.

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

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

[0051]

[0052] η(τ)=e -λτ

[0053] Among them, H(τ) is the number of working hours in time τ, I(τ) is the labor intensity index, which is obtained based on the physical requirements of the work, Ψ(τ,N) is the dynamic adjustment factor, which takes into account the impact of the number of people on the workload, z is an index that reflects the impact of work distribution on individual workload, η(τ) is an attenuation function that simulates the attenuation of efficiency over time, λ is the attenuation coefficient, ω and φ are 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 at different time series to determine the relationship between main mining work and electricity consumption.

[0055] The relationship between coal mine electricity consumption data and equipment operation data under different time series is analyzed, and the relationship between the main mining work of the equipment and the electricity consumption is obtained. s (t) is expressed as,

[0056]

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

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

[0059] According to the dynamic flow of coal in the mine, determine the relationship between transportation and electricity consumption E y (t) is expressed as,

[0060]

[0061] Among them, Q(τ) represents the dynamic flow of coal at time τ, v(τ) represents the coal flow rate, θ represents the adjustment parameter, which is the basic formula for the influence of the balance rate on power consumption, σ represents the coefficient of the logarithmic term, which is used to increase the regulation of power consumption at high flow, and e y 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 work and electricity consumption, and the relationship between transportation and electricity consumption, a mining intensity evaluation model is constructed.

[0063] The construction of mining intensity evaluation model includes relationship modification and model construction. The relationship modification includes converting the historical total electricity consumption E z (t) minus E s (t) and E y (t), and obtain the difference in power consumption E c (t), denoted as E c (t) = E z (t)-E s (t)-E y (t), the difference in power consumption E c (t) and Er (t) Make the difference and take the absolute value to get the error power consumption E w (t), the error power consumption E w (t) is compared with the minimum allowable error l. If E w If (t) is greater than l, the error power consumption is added equally to E r (t), E s (t), E y (t), for e r 、e s 、e y Modifications were made to meet the new E r (t), E s (t), E y (t). Use E r (t) and E c (t) The reason for the comparison is that the power consumption of personnel is more unstable than that of equipment collection and coal transportation, so E is used. r (t) and E c (t)Comparison can better reflect the error.

[0064] The construction of the mining intensity evaluation model based on the revised electricity consumption relationship is expressed as:

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

[0066] Among them, it is expressed as the value used for e r 、e s 、e y Modified weight factor.

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

[0068] The new behavior data is arranged in time series and brought into the mining intensity evaluation model to obtain the estimated power consumption of each time series behavior data. The estimated power consumption is compared with the maximum power consumption threshold. If it is less than the maximum power consumption threshold, the data point corresponding to this time series is marked green. If it is greater than the maximum power consumption threshold, the data point corresponding to this time series is marked red. Finally, the behavior data is arranged in time series into a red and green curve through dynamic portraits and input into the terminal.

[0069] Judging whether there is illegal mining based on the relationship between personnel and electricity consumption includes substituting the behavior data when the mining industry is shut down into E r(t), the estimated power consumption of personnel is obtained, and the estimated power consumption of personnel is compared with the minimum power consumption threshold. If it is less than the minimum power consumption threshold, the data point corresponding to this time series is marked green; if it is greater than the minimum power consumption threshold, the data point corresponding to this time series is marked yellow. Finally, the behavior data during the mining shutdown is arranged in time series into a yellow and green curve through dynamic portrait, and input into the terminal.

[0070] Relevant personnel can perform visual operations on the terminal, which is conducive to improving processing efficiency.

[0071] Example 2 is the second example of the present invention, which is different from the first example in that: the method for generating a dynamic portrait of coal mine production behavior based on production data also includes: in order to verify and illustrate the technical effects adopted in this method, this example adopts a traditional technical solution and the method of the present invention for comparative testing, and compares the test results by means of scientific argumentation to verify the actual effect of this method.

[0072] Purpose of the experiment: 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 two methods to complete an inspection of the entire mine during a typical production cycle.

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

[0076] Safety statistics: Statistics use two methods to record safety accidents during the monitoring period.

[0077] Monitoring coverage: Compare the two methods in terms of their ability to fully cover mine monitoring. The test data obtained is shown in Table 1:

[0078] Table 1: Test data comparison table

[0079]

[0080]

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

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

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

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

[0085] Example 3, reference Figure 2 , which is the third embodiment of the present invention, and is different from the previous two embodiments in that: a system for generating a dynamic portrait 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 behavior data of coal mines, and the historical behavior data includes coal mine electricity consumption data, personnel positioning 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 positioning monitoring data at different time series, and determines the relationship between personnel and electricity consumption; analyzes the relationship between coal mine electricity consumption data and equipment operation data at different time series, and determines the relationship between main mining work and electricity consumption; analyzes the relationship between coal mine electricity consumption data and coal dynamic flow data at different time series, and determines the relationship between transportation and electricity consumption; based on the relationship between personnel and electricity consumption, the relationship between main mining work and electricity consumption, and the relationship between transportation and electricity consumption, a mining intensity evaluation model is constructed; the judgment module determines whether it is over-intensity production according to the mining intensity evaluation model, and determines whether there is illegal mining according to the relationship between personnel and electricity consumption.

[0086] If the functions are implemented in the form of 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 the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the methods described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk, etc., which can store program codes.

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

[0088] More specific examples of computer-readable media (a non-exhaustive list) include the following: an electrical connection with one or more wires (electronic device), a portable computer disk case (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disk read-only memory (CDROM). In addition, the computer-readable medium may even be a paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, deciphering or, if necessary, processing in another suitable manner, and then stored in a computer memory.

[0089] It should be understood that the various parts of the present invention can be implemented by hardware, software, firmware or a combination thereof. In the above-mentioned embodiments, a plurality of steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, it can be implemented by any one of the following technologies known in the art or their combination: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0090] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. A method for generating a dynamic portrait of coal mine production behavior based on production data, characterized in that: include, Collecting historical behavior data of coal mines, including coal mine electricity consumption data, personnel location monitoring data, equipment operation data and coal dynamic flow data; Analyze the relationship between coal mine electricity consumption data and personnel location monitoring data at different time series to determine the relationship between personnel and electricity consumption; Analyze the relationship between coal mine electricity consumption data and equipment operation data at different time series to determine the relationship between main mining work and electricity consumption; Analyze the relationship between coal mine electricity consumption data and coal dynamic flow data at 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 work and electricity consumption, and the relationship between transportation and electricity consumption, a mining intensity evaluation model is constructed; Whether it is over-intensity production is determined based on the mining intensity evaluation model, and whether there is illegal mining is determined based on the relationship between personnel and electricity consumption.

2. The method for generating a dynamic portrait of coal mine production behavior based on production data according to claim 1, characterized in that: Determining the relationship between personnel and power consumption includes analyzing the relationship between coal mine power consumption data and personnel positioning monitoring data at different time sequences, and obtaining the relationship between personnel factors and power consumption. r (t) is expressed as, Among them, e r is the average power consumption per person, N(τ) is the number of people per time τ, W(τ) is the work intensity function, Δt i It is represented as the time difference between the i-th worker going up and down the well, and τ is represented as an integral variable, representing any time point in the time period from t0 to t; The work intensity function W(τ) is specifically expressed as: W(τ)=H(τ)·(I(τ)·Ψ(τ,N(τ)))·η(τ)·(1+sin(ωτ+φ)) η(τ)=e -λτ Among them, H(τ) is the number of working hours in time τ, I(τ) is the labor intensity index, which is obtained based on the physical requirements of the work, Ψ(τ,N) is the dynamic adjustment factor, which takes into account the impact of the number of people on the workload, z is an index that reflects the impact of work distribution on individual workload, η(τ) is an attenuation function that simulates the attenuation of efficiency over time, λ is the attenuation coefficient, ω and φ are the frequency and phase of the sine function, which respectively determine the period and starting point of the change in work intensity.

3. The method for generating a dynamic portrait of coal mine production behavior based on production data according to claim 2, characterized in that: 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, and obtaining the relationship between the main mining work of the equipment and the power consumption E s (t) is expressed as, Among them, α and β represent the power consumption of basic start-stop and additional start-stop, respectively. F(τ) represents the number of basic start-stops of the equipment within time τ. L(τ) represents the operating load of the equipment at time τ. thresh It is expressed as the load threshold. When this threshold is exceeded, additional start and stop are required to protect the equipment. δ(L(τ),L thresh ) is represented as a conditional logic function. When L(τ)>L thresh , δ=1, otherwise δ=0, e s It is represented as the average power consumption of a unit device, and R(τ) is represented as the operating time of the device in time τ.

4. The method for generating a dynamic portrait of coal mine production behavior based on production data according to claim 3, characterized in that: The determination of the relationship between transportation and power consumption includes determining the relationship between transportation and power consumption according to the dynamic flow of coal in the mine. y (t) is expressed as, Among them, Q(τ) represents the dynamic flow of coal at time τ, v(τ) represents the coal flow rate, θ represents the adjustment parameter, which is the basic formula for the influence of the balance rate on power consumption, σ represents the coefficient of the logarithmic term, which is used to increase the regulation of power consumption at high flow, and e y Expressed as the average electricity consumption per unit flow.

5. The method for generating a dynamic portrait of coal mine production behavior based on production data according to claim 4, characterized in that: The construction of the mining intensity evaluation model includes relationship modification and model construction. The relationship modification includes converting the historical total electricity consumption E z (t) minus E s (t) and E y (t), and obtain the difference in power consumption E c (t), denoted as E c (t) = E z (t)-E s (t)-E y (t), the difference in power consumption E c (t) and E r (t) Make the difference and take the absolute value to get the error power consumption E w (t), the error power consumption E w (t) and the minimum allowable error For comparison, if E w (t) greater than Then the error power consumption is added equally to E r (t), E s (t), E y (t), for e r 、e s 、e y Modifications were made to meet the new E r (t), E s (t), E y (t).

6. The method for generating a dynamic portrait of coal mine production behavior based on production data according to claim 5, characterized in that: The model construction includes the construction of a mining intensity evaluation model based on the corrected electricity consumption relationship, which is expressed as: E j (t)=W1E r (t)+W2E s (t)+W3E y (t) Among them, it is expressed as the value used for e r 、e s 、e y Modified weight factor.

7. The method for generating a dynamic portrait of coal mine production behavior based on production data according to claim 6, characterized in that: The method of judging whether the over-intensity production is carried out according to the mining intensity evaluation model includes: arranging the new behavior data in time series and bringing it into the mining intensity evaluation model to obtain the estimated power consumption of each time series behavior data, comparing the estimated power consumption with the maximum power consumption threshold, and marking the data point corresponding to the time series green if it is less than the maximum power consumption threshold, and marking the data point corresponding to the time series red if it is greater than the maximum power consumption threshold, and finally arranging the behavior data in time series into a red and green curve through dynamic portrait, and inputting it into the terminal; The judgment of whether there is illegal mining based on the relationship between personnel and electricity consumption includes substituting the behavior data when the mining industry is shut down into E r (t), the estimated power consumption of personnel is obtained, and the estimated power consumption of personnel is compared with the minimum power consumption threshold. If it is less than the minimum power consumption threshold, the data point corresponding to this time series is marked green; if it is greater than the minimum power consumption threshold, the data point corresponding to this time series is marked yellow. Finally, the behavior data during the mining shutdown is arranged in time series into a yellow and green curve through dynamic portrait, and input into the terminal.

8. A system using the method for generating a dynamic portrait of coal mine production behavior based on production data as claimed in any one of claims 1 to 7, characterized in that: It includes data collection module, model building module and judgment module; The data collection module collects historical behavior data of the coal mine, and the historical behavior data includes 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 positioning monitoring data at different time sequences to determine the relationship between personnel and electricity consumption; analyzes the relationship between coal mine electricity consumption data and equipment operation data at different time sequences to determine the relationship between main mining work and electricity consumption; analyzes the relationship between coal mine electricity consumption data and coal dynamic flow data at different time sequences to determine the relationship between transportation and electricity consumption; Based on the relationship between personnel and electricity consumption, the relationship between main mining work and electricity consumption, and the relationship between transportation and electricity consumption, a mining intensity evaluation model is constructed; The judgment module determines whether over-intensity production occurs based on the mining intensity evaluation model, and determines whether illegal mining occurs based on the relationship between personnel and electricity consumption.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method for generating a dynamic portrait of coal mine production behavior based on production data as described in any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method for generating a dynamic portrait of coal mine production behavior based on production data as described in any one of claims 1 to 7 are implemented.

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