Method, device and electronic equipment for identifying abnormal working resistance of hydraulic support

Abstract

The present application proposes a method, device and electronic equipment for identifying abnormal working resistance of hydraulic supports, including: based on the working data of multiple hydraulic supports in the current time period during mining work, using a target prediction model to predict the probability of opening of the safety valve of each hydraulic support within a future target time period; wherein the current time period includes the current moment; determining a first risk sub-index value based on each opening probability; performing a first analysis on the mining data of the mining work in the current time period to obtain a second risk sub-index value; performing a second analysis on the target working resistance cloud map of multiple hydraulic supports at the current moment to obtain a third risk sub-index value; based on the first risk sub-index value, the second risk sub-index value and the third risk sub-index value, identifying the target abnormal type of multiple hydraulic supports. In this way, the abnormal working resistance stress results of the hydraulic supports can be automatically and quickly identified, thereby improving the timeliness and effectiveness of the recognition of abnormal working resistance of the hydraulic supports.

Description

Technical Field

[0001] The present application relates to the field of artificial intelligence technology, and in particular to a method for identifying abnormal working resistance of a hydraulic support. Background Art

[0002] In underground mines, geological conditions are complex and ever-changing. The safety of tunnel structures is undoubtedly crucial during ore extraction. Hydraulic supports, as key equipment supporting tunnels and protecting worker safety, play an irreplaceable role. During ore mining operations, the various pressures experienced by the tunnels can easily cause abnormalities in the hydraulic supports' working resistance stress. Failure to promptly detect and properly address these abnormalities can lead to structural failure and even mining accidents. Therefore, monitoring and providing early warning of abnormal hydraulic support working resistance stress is extremely important. Summary of the Invention

[0003] The present application aims to solve one of the technical problems in the related art at least to a certain extent.

[0004] To this end, the first purpose of this application is to propose a method for identifying abnormal working resistance of a hydraulic support.

[0005] The second purpose of this application is to provide a device for identifying abnormal working resistance of a hydraulic support.

[0006] The third objective of this application is to provide an electronic device.

[0007] The fourth object of this application is to provide a computer-readable storage medium.

[0008] A fifth object of this application is to provide a computer program product.

[0009] To achieve the above-mentioned purpose, the first embodiment of the present application proposes a method for identifying abnormal working resistance of a hydraulic support, comprising:

[0010] Based on the working data of multiple hydraulic supports in the current time period during mining work, a target prediction model is used to predict the opening probability of the safety valve of each hydraulic support within a future target time period; wherein the current time period includes the current moment;

[0011] Determining a first risk sub-index value based on each of the opening probabilities; wherein the first risk sub-index value is determined based on the number of target hydraulic supports among the plurality of hydraulic supports whose opening probabilities meet a set condition and whose layout positions are consecutively adjacent;

[0012] Performing a first analysis on mining data of the mining work in the current time period to obtain a second risk sub-indicator value;

[0013] Performing a second analysis on the target working resistance cloud graphs of the plurality of hydraulic supports at the current moment to obtain a third risk sub-indicator value;

[0014] Based on the first risk sub-indicator value, the second risk sub-indicator value, and the third risk sub-indicator value, target abnormality types of the plurality of hydraulic supports are identified.

[0015] To achieve the above-mentioned purpose, the second embodiment of the present application proposes a device for identifying abnormal working resistance of a hydraulic support, the device comprising:

[0016] A prediction module is configured to predict the probability of the safety valve of each hydraulic support being opened within a future target duration based on the operating data of the hydraulic supports in a current time period during mining operations and using a target prediction model; wherein the current time period includes the current moment;

[0017] A first determining module is configured to determine a first risk sub-index value based on each of the opening probabilities; wherein the first risk sub-index value is determined based on the number of target hydraulic supports among the plurality of hydraulic supports whose opening probabilities meet a set condition and whose layout positions are consecutively adjacent;

[0018] A first analysis module is configured to perform a first analysis on the mining data of the mining work in the current time period to obtain a second risk sub-indicator value;

[0019] A second analysis module is configured to perform a second analysis on the target working resistance cloud graphs of the plurality of hydraulic supports at the current moment to obtain a third risk sub-indicator value;

[0020] An identification module is configured to identify target abnormality types of the plurality of hydraulic supports based on the first risk sub-indicator value, the second risk sub-indicator value, and the third risk sub-indicator value.

[0021] To achieve the above-mentioned purpose, the third aspect embodiment of the present application proposes an electronic device, comprising: a processor, and a memory communicatively connected to the processor; the memory stores computer-executable instructions; the processor executes the computer-executable instructions stored in the memory to implement the method described in the first aspect embodiment above.

[0022] To achieve the above-mentioned purpose, the fourth embodiment of the present application proposes a computer-readable storage medium, in which computer-executable instructions are stored. When the computer-executable instructions are executed by a processor, they are used to implement the method described in the first embodiment above.

[0023] To achieve the above-mentioned purpose, the fifth embodiment of the present application proposes a computer program product, including a computer program, which, when executed by a processor, implements the method described in the first embodiment above.

[0024] The present application provides a method for identifying abnormal working resistance of hydraulic supports. The method uses a target prediction model to predict the probability of opening of the safety valve of each hydraulic support within a future target time period based on the working data of multiple hydraulic supports in the current time period during mining work; wherein the current time period includes the current moment; according to each opening probability, a first risk sub-index value is determined; wherein the first risk sub-index value is determined based on the number of target hydraulic supports whose opening probabilities meet the set conditions and whose layout positions are continuously adjacent to each other among the multiple hydraulic supports; a first analysis is performed on the mining data of the mining work in the current time period to obtain a second risk sub-index value; a second analysis is performed on the target working resistance cloud map of the multiple hydraulic supports at the current moment to obtain a third risk sub-index value; based on the first risk sub-index value, the second risk sub-index value and the third risk sub-index value, the target abnormal type of the multiple hydraulic supports is identified. Thus, the working data of the hydraulic supports, the mining data of the mining work and the working resistance cloud map of the hydraulic supports can be combined to automatically and quickly identify the abnormal working resistance stress results of the hydraulic supports, thereby improving the timeliness and effectiveness of the recognition of abnormal working resistance of the hydraulic supports.

[0025] Additional aspects and advantages of the present application will be given in part in the description below, and in part will become apparent from the description below, or will be learned through practice of the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] The above and / or additional aspects and advantages of the present application will become apparent and easily understood from the following description of the embodiments in conjunction with the accompanying drawings, in which:

[0027] Figure 1 A flow chart of a method for identifying abnormal working resistance of a hydraulic support provided in one embodiment of the present application;

[0028] Figure 2 A flow chart of a method for identifying abnormal working resistance of a hydraulic support provided in another embodiment of the present application;

[0029] Figure 3 A flow chart of a method for identifying abnormal working resistance of a hydraulic support provided in another embodiment of the present application;

[0030] Figure 4 A schematic diagram of the target mining area provided for this application;

[0031] Figure 5 This is a schematic diagram of the first working resistance cloud diagram provided in this application;

[0032] Figure 6 This is a structural diagram of a device for identifying abnormal working resistance of a hydraulic support provided in another embodiment of the present application. DETAILED DESCRIPTION

[0033] The following describes in detail embodiments of the present application. Examples of the embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are intended to be used to explain the present application, and should not be construed as limiting the present application.

[0034] With the rapid development of intelligent sensing technology and equipment for hydraulic supports, electro-hydraulic control systems can monitor and collect information about the inclination, load, and posture of hydraulic supports throughout the entire mining face in real time, thereby obtaining a vast amount of sensing data. Based on this, relevant technicians have applied various types of wired and wireless hydraulic support state sensing elements and developed a working resistance stress monitoring device based on resistance change / time change to monitor the stress conditions of the hydraulic supports. However, these working resistance stress monitoring devices lack the ability to respond immediately to dynamically changing environments and cannot meet the needs of modern engineering for high-precision, real-time monitoring. Furthermore, when processing the relevant data of the hydraulic supports, the sheer volume of data leads to a significant decrease in database storage and query performance during actual use, resulting in page freezes, slow data queries, and data loss. This makes it impossible to fully perceive, quickly evaluate, and issue early warnings about the working status or working data of the supports.

[0035] In response to at least one of the above problems, the present application proposes a method, device and electronic equipment for identifying abnormal working resistance of a hydraulic support.

[0036] The following describes the method, device and electronic equipment for identifying abnormal working resistance of a hydraulic support according to the embodiments of the present application with reference to the accompanying drawings.

[0037] Figure 1 This is a flow chart of a method for identifying abnormal working resistance of a hydraulic support provided in one embodiment of the present application.

[0038] The embodiment of the present application takes the hydraulic support working resistance abnormality identification method as an example, which is configured in a hydraulic support working resistance abnormality identification device. The hydraulic support working resistance abnormality identification device can be applied to any electronic device so that the electronic device can perform the hydraulic support working resistance abnormality identification function.

[0039] Among them, the electronic device can be any device with computing capabilities, such as a personal computer (PC), an industrial computer, a host computer, a mobile terminal, a server, etc. The mobile terminal can be, for example, a mobile phone, a tablet computer, a personal digital assistant, a wearable device, and other hardware devices with various operating systems, touch screens and / or display screens.

[0040] like Figure 1 As shown, the method includes the following steps:

[0041] Step S101 : Based on the working data of multiple hydraulic supports in the current time period during mining work, a target prediction model is used to predict the opening probability of the safety valve of each hydraulic support within a future target time period.

[0042] The current time period may include the current moment. Alternatively, in some embodiments, the current time period may include multiple target moments. In this case, it should be noted that the multiple target moments include the current moment.

[0043] Among them, during mining work, the hydraulic support can be used to support the mining working face, and the hydraulic support can include a front column and a rear column. It should be noted that this application does not limit the number of hydraulic supports.

[0044] Optionally, in some embodiments, when the current time period includes multiple target moments, the working data may include but is not limited to: the support number of each hydraulic support, the left column resistance value (i.e., the working resistance of the left column) and the right column resistance value (i.e., the working resistance of the right column) of any hydraulic support at each target moment, the left initial support force and the right initial support force of any hydraulic support at each target moment, the target working stage of any hydraulic support at each target moment and the start time of the target working stage (recorded as the first start time in this application), the start time (recorded as the second start time in this application) and the end time when the safety valve is in the open state in the most recent safety valve opening cycle in the historical records corresponding to each target moment of any hydraulic support, the live column height data when the safety valve is in the open state in the most recent safety valve opening cycle in the historical records corresponding to each target moment of any hydraulic support, etc.

[0045] Among them, the support number is used to uniquely identify the corresponding hydraulic support.

[0046] The target working stage may be one of a column raising stage, a column lowering stage, a supporting stage and a frame moving stage.

[0047] Among them, the safety valve opening cycle can indicate the frequency at which the safety valve of the corresponding hydraulic support automatically opens and unloads when the system pressure fluctuates.

[0048] Optionally, in some embodiments, the target prediction model may include a first bidirectional LSTM (Long Short-Term Memory) layer, a Dropout layer, a second bidirectional LSTM layer, and a Sigmoid layer.

[0049] The target duration may be pre-set, such as 10s, 15s, etc., and this application does not impose any restrictions on this. It should be noted that the future target duration may be a target duration after the current time. For example, if the current time is t, the future target duration refers to 10s after t.

[0050] The opening probability can be used to indicate the possibility of the safety valve of the corresponding hydraulic support opening.

[0051] In an embodiment of the present application, based on the working data of multiple hydraulic supports in the current time period during mining work, a target prediction model can be used to predict the opening probability of the safety valve of each hydraulic support within the future target time period.

[0052] Optionally, in some embodiments, data preprocessing may be performed on the working data of multiple hydraulic supports in the current time period, wherein the data preprocessing may include normalization, missing value processing, outlier processing, etc., which is not limited in this application.

[0053] Step S102: determining a first risk sub-indicator value according to each activation probability.

[0054] The first risk sub-index value may be determined based on the number of target hydraulic supports among the plurality of hydraulic supports whose opening probabilities meet the set conditions and whose layout positions are continuously adjacent to each other.

[0055] Among them, the setting conditions can be pre-set, and this application does not limit the setting of the setting conditions.

[0056] Optionally, in some embodiments, the set condition may be, for example, that the activation probability is greater than a set probability threshold, wherein the set probability threshold may be pre-set, such as 0.8, 0.75, etc., and this application does not impose any limitation on this.

[0057] Optionally, in some embodiments, for any hydraulic support among multiple hydraulic supports, when the probability of opening of the safety valve of the hydraulic support within the future target time period meets the set conditions, the hydraulic support is determined to be the second hydraulic support; when the hydraulic support is determined to be the second hydraulic support, determine whether there is a hydraulic support whose opening probability meets the set conditions from the hydraulic supports adjacent to the layout position of the second hydraulic support, and if so, determine the second hydraulic support to be the target hydraulic support; and then determine the first risk sub-indicator value based on the number of target hydraulic supports with consecutive adjacent layout positions among the multiple hydraulic supports.

[0058] It should be noted that, when the hydraulic support is determined to be the second hydraulic support, if there is no hydraulic support adjacent to the second hydraulic support whose opening probability meets the set conditions, then the second hydraulic support is determined not to be the target hydraulic support.

[0059] As an example, assuming that the number of hydraulic supports is n, for the j-th hydraulic support among the n hydraulic supports, when the probability of opening the safety valve of the j-th hydraulic support within the future target time period meets the set conditions, the j-th hydraulic support is determined to be the second hydraulic support; when the j-th hydraulic support is determined to be the second hydraulic support, determine whether there is a hydraulic support whose opening probability meets the set conditions from the hydraulic supports adjacent to the layout position of the j-th hydraulic support. If so, the j-th hydraulic support is the target hydraulic support; otherwise, it is determined that the j-th hydraulic support is not the target hydraulic support; finally, the corresponding risk score is determined based on the maximum number of target hydraulic supports with consecutive adjacent layout positions among multiple hydraulic supports, and the risk score is determined as the first risk sub-indicator value. For example, assuming that n is 150, the 7th to 17th hydraulic supports are target hydraulic supports whose opening probabilities meet the set conditions and whose layout positions are continuously adjacent, the 80th to 89th hydraulic supports are target hydraulic supports whose opening probabilities meet the set conditions and whose layout positions are continuously adjacent, and the 135th to 147th hydraulic supports are target hydraulic supports whose opening probabilities meet the set conditions and whose layout positions are continuously adjacent. Then, the maximum number of target hydraulic supports whose opening probabilities meet the set conditions and whose layout positions are continuously adjacent among the above 150 hydraulic supports is 13 (=147-135+1). The corresponding risk score can be determined based on this maximum number, and the risk score can be determined as the first risk sub-indicator value.

[0060] Among them, it should be noted that the correspondence between the maximum number of target hydraulic supports with consecutive adjacent layout positions among multiple hydraulic supports and the risk score can be established in advance and saved. Then, after determining the maximum number, the above correspondence can be queried to obtain the corresponding risk score.

[0061] As an example, the maximum number of target hydraulic supports arranged in consecutive adjacent positions among multiple hydraulic supports and their corresponding risk scores are shown in Table 1:

[0062] Table 1 Maximum number and its corresponding risk score

[0063]

[0064] It should be noted that the above examples of the maximum number and the corresponding risk score are merely exemplary. In practical applications, they may be other examples, and this application does not impose any limitation on this.

[0065] Step S103: performing a first analysis on the mining data of the mining work in the current time period to obtain a second risk sub-indicator value.

[0066] The mining data may include but is not limited to the shearer number, mining progress, mining height, traction direction of the currently operating shearer, and support stiffness of each hydraulic support.

[0067] Among them, the coal mining machine number is used to uniquely identify the corresponding coal mining machine.

[0068] Mining progress refers to the daily mining advance distance, which is used to indicate the coal mining speed;

[0069] The traction direction is used to indicate the working direction of the corresponding coal mining machine.

[0070] Support stiffness can be used to measure the supporting capacity of the corresponding hydraulic support for the top plate.

[0071] As a possible implementation method, the second risk sub-indicator value can be obtained based on mining data using the Mining FEM model (Finite Element Method Model in Mining).

[0072] It should be noted that the core functions of the Mining FEM model include roof pressure distribution simulation, tunnel deformation analysis, and geological risk coefficient calculation. The roof pressure distribution simulation function can dynamically calculate the roof pressure distribution based on the mining height and mining progress, and predict the index value of potential roof subsidence risk. The tunnel deformation analysis function can combine the hydraulic support stiffness to evaluate the stability and deformation trend of the tunnel surrounding rock and obtain the tunnel deformation risk index value. The geological risk coefficient calculation function can output the geological risk coefficient based on the index value of roof subsidence risk and the tunnel deformation risk index value, which is recorded as the second risk sub-index value in this application.

[0073] Step S104: performing a second analysis on the target working resistance cloud diagrams of the multiple hydraulic supports at the current moment to obtain a third risk sub-index value.

[0074] Among them, the target working resistance cloud map can be used to indicate the distribution of resistance values ​​of multiple hydraulic supports at different positions.

[0075] In the embodiment of the present application, a second analysis may be performed on the target working resistance cloud diagrams of the plurality of hydraulic supports to obtain a third risk sub-index value.

[0076] Step S105 : identifying target abnormality types of the plurality of hydraulic supports based on the first risk sub-indicator value, the second risk sub-indicator value, and the third risk sub-indicator value.

[0077] The target anomaly type may be, but is not limited to, low risk, medium risk, high risk, etc.

[0078] As an example, the first risk sub-indicator value, the second risk sub-indicator value and the third risk sub-indicator value can be weighted and summed to obtain a target coefficient, and then based on the target coefficient, the corresponding abnormality type is determined, and the abnormality type is determined as the target abnormality type of multiple hydraulic supports.

[0079] As an example, the target distance interval to which the target coefficient belongs can be determined from multiple set value intervals, and the abnormality type corresponding to the target distance interval can be determined as the target abnormality type of multiple hydraulic supports.

[0080] The set value interval can be pre-set, and this application does not limit the value range of the set value interval.

[0081] It should be noted that any set value range can have a corresponding exception type. In one example, the set value range and the corresponding exception type are shown in Table 1:

[0082] Table 2 Setting value ranges and corresponding exception types

[0083]

[0084] As shown in Table 1, when the target distance interval to which the target coefficient belongs is (0.5, 0.75], the abnormality type corresponding to the target distance interval is determined to be the target abnormality type of the hydraulic support.

[0085] It should be noted that the above examples of setting the value range and the corresponding exception type are only exemplary, and this application does not limit the setting value range and the corresponding exception type.

[0086] As a possible implementation method, after obtaining the first risk sub-indicator value, the second risk sub-indicator value and the third risk sub-indicator value, the target weights of the first risk sub-indicator value, the second risk sub-indicator value and the third risk sub-indicator value can be determined respectively based on the first risk sub-indicator value, the second risk sub-indicator value and the third risk sub-indicator value.

[0087] As an example, assuming that the first risk sub-indicator value is R1, the second risk sub-indicator value is R2, and the third risk sub-indicator value is R3, the initial weights of the above-mentioned indicator values ​​can be determined according to the following formula:

[0088] ; (1)

[0089] ; (2)

[0090] ; (3)

[0091] in, Indicates the initial weight of the first risk sub-indicator value, Indicates the initial weight of the second risk sub-indicator value, Indicates the initial weight of the third risk sub-indicator value;

[0092] Furthermore, the above initial weights are normalized to obtain the target weights of the above index values.

[0093] In this way, the weights of various indicators can be calibrated in real time. By dynamically adjusting the weights of various indicator values, the rationality of weight distribution and the accuracy and effectiveness of subsequent corresponding decisions can be improved.

[0094] Optionally, in some embodiments, corresponding target response measures can be determined and deployed based on the target anomaly type. As an example, assuming the target anomaly type is low risk, routine monitoring of the hydraulic support can continue; when the target anomaly type is medium risk, the sound and light alarm system can be controlled to sound and light alarm to prompt relevant personnel to reduce the mining speed to the target speed (such as 50% of the mining speed); when the target anomaly type is high risk, the operating equipment in the mining operation (such as mining machines, transportation equipment, etc.) can be controlled to stop operation to facilitate relevant personnel to carry out support reinforcement (such as grouting / repairing, etc.).

[0095] The method for identifying abnormal working resistance of hydraulic supports in an embodiment of the present application uses a target prediction model based on the working data of multiple hydraulic supports in the current time period during mining to predict the probability of opening of the safety valve of each hydraulic support within a future target time period; wherein the current time period includes the current moment; according to each opening probability, a first risk sub-index value is determined; wherein the first risk sub-index value is determined based on the number of target hydraulic supports whose opening probabilities meet the set conditions and whose layout positions are continuously adjacent to each other among the multiple hydraulic supports; a first analysis is performed on the mining data of the mining work in the current time period to obtain a second risk sub-index value; a second analysis is performed on the target working resistance cloud map of the multiple hydraulic supports at the current moment to obtain a third risk sub-index value; based on the first risk sub-index value, the second risk sub-index value and the third risk sub-index value, the target abnormal type of the multiple hydraulic supports is identified. In this way, the working data of the hydraulic supports, the mining data of the mining work and the working resistance cloud map of the hydraulic supports can be combined to automatically and quickly identify the abnormal working resistance stress results of the hydraulic supports, thereby improving the timeliness and effectiveness of the recognition of abnormal working resistance of the hydraulic supports.

[0096] In order to clearly illustrate how, in the above embodiment of the present application, a target prediction model is used to predict the probability of opening of the safety valve of each hydraulic support within the future target time period based on the working data of multiple hydraulic supports in the current time period during mining work, the present application also proposes a method for identifying abnormal working resistance of a hydraulic support.

[0097] Figure 2 This is a flow chart of a method for identifying abnormal working resistance of a hydraulic support provided in another embodiment of the present application.

[0098] like Figure 2 As shown, the method includes the following steps:

[0099] Step S201: Statistically analyze the working data of multiple hydraulic supports in the current time period to obtain at least one statistical feature.

[0100] It should be noted that the explanations of the hydraulic support, current time period, and working data in step S101 are also applicable to this embodiment and will not be repeated here.

[0101] Optionally, in some embodiments, when the current time period includes multiple target time periods, the work data includes at least one of the following:

[0102] The left column resistance value and the right column resistance value of each hydraulic support at any target moment;

[0103] The left initial support force and right initial support force of each hydraulic support at any target time;

[0104] The target working stage of each hydraulic support at any target moment and the first starting time of the target working stage;

[0105] The second start time and end time of each hydraulic support when the safety valve is in the open state in the most recent safety valve opening cycle in the historical records corresponding to any target time;

[0106] The plunger height data when the safety valve is in the open state during the most recent safety valve opening cycle in the historical records corresponding to any target time of each hydraulic support;

[0107] Correspondingly, the statistical features may include at least one of the following:

[0108] The target resistance value of each hydraulic support at any target time; wherein the target resistance value is determined based on the left column resistance value and the right column resistance value of the corresponding hydraulic support;

[0109] The qualified rate of the initial support force of the plurality of hydraulic supports at any target time; wherein the qualified rate of the initial support force is determined based on the left initial support force and the right initial support force of the plurality of hydraulic supports;

[0110] A working cycle time vector of each hydraulic support at any target time; wherein the working cycle time vector is generated based on the target working stage of the corresponding hydraulic support at the corresponding target time and the first starting time of the target working stage;

[0111] The target duration of each hydraulic support at any target time; wherein the target duration is based on the second start time and end time of the safety valve being in the open state during the most recent safety valve opening cycle of the corresponding hydraulic support in the corresponding historical records;

[0112] The target plunger retraction amount of each hydraulic support at any target moment; wherein, the target plunger retraction amount is determined based on the plunger height data of the corresponding hydraulic support when the safety valve is in the open state during the most recent safety valve opening cycle in the corresponding historical records.

[0113] Alternatively, in some embodiments, the target resistance value may be an average of a left column resistance value and a right column resistance value of the corresponding hydraulic support.

[0114] Optionally, in some embodiments, for any target moment, the process of determining the qualified rate of the initial support force of multiple hydraulic supports at the target moment may be: for any hydraulic support, determining whether the left initial support force and the right initial support force of the hydraulic support at the target moment are both qualified; when the left initial support force and the right initial support force of the hydraulic support at the target moment are both qualified, determining that the hydraulic support is the first hydraulic support with qualified initial support force; counting the first hydraulic support among the multiple hydraulic supports to obtain a first number; and determining the qualified rate of the initial support force of the multiple hydraulic supports at the target moment based on the first number and the number of the multiple hydraulic supports.

[0115] As an example, assuming that the number of hydraulic supports is n, for the i-th hydraulic support, when the left initial support force F of the i-th hydraulic support at the target time is 左 The following conditions are met:

[0116] F 左min ≤F 左 ≤F 左max ; (4)

[0117] Among them, F 左min 、F 左max are the minimum and maximum values ​​of the left initial support force of the i-th hydraulic support respectively;

[0118] Then the left initial support force of the i-th hydraulic support at the target time is qualified;

[0119] When the left initial support force F of the i-th hydraulic support at the target time 右 The following conditions are met:

[0120] F 右min ≤F 右 ≤F 右max ; (5)

[0121] Among them, F 右min 、F 右max are the minimum and maximum values ​​of the right initial support force of the i-th hydraulic support respectively;

[0122] Then the right initial support force of the i-th hydraulic support at the target time is qualified;

[0123] When it is determined that the left initial support force and the right initial support force of the i-th hydraulic support at the target moment are both qualified, the i-th hydraulic support is determined to be the first hydraulic support with qualified initial support force; finally, the first hydraulic supports among the multiple hydraulic supports are counted to obtain a first number; the ratio of the first number to the number of the multiple hydraulic supports is determined as the qualified rate of the initial support force of the multiple hydraulic supports at the target moment.

[0124] It should be noted that the minimum value of the left initial support force and the minimum value of the right initial support force of the same hydraulic support can be the same or different. Similarly, the maximum value of the left initial support force and the maximum value of the right initial support force of the same hydraulic support can be the same or different. The minimum value of the left initial support force of different hydraulic supports can be the same or different. Similarly, the maximum value of the left initial support force of different hydraulic supports can be the same or different. The minimum value of the right initial support force of different hydraulic supports can be the same or different. The maximum value of the right initial support force of different hydraulic supports can be the same or different.

[0125] Optionally, in some embodiments, for any hydraulic support, the process of generating the working cycle time vector of the hydraulic support at any target moment may include: for any target moment, based on the time difference between the current time and the first start time of the target working phase of the hydraulic support at the target moment, determining the first duration of the target working phase of the hydraulic support at the target moment; generating the working cycle time vector of the hydraulic support at the target moment based on the first duration of the target working phase of the hydraulic support at the target moment.

[0126] As an example, assume that the working stages of a hydraulic support include a column raising stage, a column lowering stage, a support stage, and a frame moving stage. The number of hydraulic supports is n. For the i-th hydraulic support, the target time is t0. The target working stage of the i-th hydraulic support at t0 is the support stage, and the first starting time of this stage is t1. The time difference between t0 and the first starting time t1 of the target working stage of the i-th hydraulic support at t0 is (t0-t1), and this time difference is determined as the first duration of the target working stage of the i-th hydraulic support at t0. This first duration is used as the value of the element of the dimension corresponding to the working stage in the working cycle time vector, and the values ​​of the elements of the dimensions of other working stages are set to set values ​​(such as 0), thereby generating the working cycle time vector of the i-th hydraulic support at t0. For example, the working cycle time vector of the i-th hydraulic support at t0 is:

[0127] (T 支撑 , T 升柱 , T 降柱 , T 移架 ) = (t0-t1, 0, 0, 0); (6)

[0128] Among them, T 支撑 represents the dimension of the support phase, T 升柱 Indicates the dimension of the rising column stage, T 降柱 Indicates the dimension of the column-dropping stage, T 移架Dimension that represents the stage of shelf removal.

[0129] Optionally, in some embodiments, for any hydraulic support, the process of determining the target piston downward retraction of the hydraulic support at any target moment can be: for any target moment, determine the minimum and maximum values ​​of the piston height data when the safety valve is in the open state in the most recent safety valve opening cycle in the historical records corresponding to the hydraulic support at the target moment; and determine the difference between the maximum and minimum values ​​as the target piston downward retraction of the hydraulic support at the target moment.

[0130] Step S202 : Based on at least one statistical feature, a target prediction model is used to obtain the opening probability of the safety valve of each hydraulic support within a future target duration.

[0131] It should be noted that the explanation of the target prediction model, future target duration, and activation probability in step S101 is also applicable to this embodiment and will not be repeated here.

[0132] As an example, at least one statistical feature may be input into a target prediction model, and in response to an output of the target prediction model, the opening probability of the safety valve of each hydraulic support within a future target duration is obtained.

[0133] In order to effectively obtain the target prediction model, as a possible implementation method, the working data of multiple hydraulic supports within a set time period (such as half a year, 30 days, etc.) before the current time period can be obtained, and then the working data of multiple hydraulic supports within the set time period before the current time period can be divided into a training set and a test set, and the training set is used to train the initial prediction model, and the test set is used to test the initial prediction model, so as to obtain the target prediction model.

[0134] In one example, when the target prediction model includes a first bidirectional LSTM (Long Short-Term Memory) layer, a Dropout layer, a second bidirectional LSTM layer, and a Sigmoid layer, the first bidirectional LSTM layer can be used to extract the forward and backward dependencies in the time series data to capture the dynamic characteristics of the hydraulic system pressure changes; then, the Dropout layer is used to reduce the overfitting risk and improve the generalization ability of the model; further, the second bidirectional LSTM layer is used to mine deep temporal features, and finally, the Sigmoid output layer is used to generate probabilistic prediction results.

[0135] Step S203 : determining a first risk sub-index value according to the opening probability of the safety valve of each hydraulic support within the future target time period.

[0136] Step S204: performing a first analysis on the mining data of the mining work in the current time period to obtain a second risk sub-indicator value.

[0137] Step S205: Perform a second analysis on the target working resistance cloud diagrams of the multiple hydraulic supports at the current moment to obtain a third risk sub-index value.

[0138] Step S206: identifying target abnormality types of the plurality of hydraulic supports based on the first risk sub-indicator value, the second risk sub-indicator value, and the third risk sub-indicator value.

[0139] It should be noted that the execution process of steps S203 to S206 can refer to the execution process of any embodiment of the present application and will not be repeated here.

[0140] The method for identifying abnormal operating resistance of a hydraulic support in an embodiment of the present application performs a statistical analysis of the operating data of multiple hydraulic supports during a current time period to obtain at least one statistical feature. Based on this feature, a target prediction model is then used to determine the probability of each hydraulic support's safety valve opening within a future target duration. Thus, by quantifying the statistical features of the hydraulic supports and combining them with the target prediction model, the future probability of safety valve opening can be accurately captured.

[0141] In order to clearly explain how to obtain the target working resistance cloud map at the current moment in the above embodiment of the present application, the present application also proposes a method for identifying abnormal working resistance of a hydraulic support.

[0142] Figure 3 This is a flow chart of a method for identifying abnormal working resistance of a hydraulic support provided in another embodiment of the present application.

[0143] like Figure 3 As shown, based on any of the above embodiments of the present application, the method may further include the following steps:

[0144] Step S301 : Based on the working data of multiple hydraulic supports in the current time period during mining work, a target prediction model is used to predict the opening probability of the safety valve of each hydraulic support within a future target time period.

[0145] Step S302: Determine a first risk sub-indicator value according to each activation probability.

[0146] Step S303: Perform a first analysis on the mining data of the mining work in the current time period to obtain a second risk sub-indicator value.

[0147] It should be noted that the execution process of steps S301 to S303 can refer to the execution process of any embodiment of the present application and will not be repeated here.

[0148] Step S304, based on the first resistance values ​​of multiple hydraulic supports within the historical time period set before the current time period and the second resistance values ​​in the current time period, predict the first predicted resistance value of each hydraulic support in the process of advancing forward with the mining working face to the first set length position in front of the current mining working face.

[0149] Among them, the historical duration can be pre-set, such as 90 days, 120 days, etc., and this application does not limit its value.

[0150] Optionally, when the current time period includes multiple target moments, the first resistance value may be the average of the left column resistance value and the right column resistance value of the corresponding hydraulic support at any collection moment within a set historical time period before the current time period; the second resistance value may be the average of the left column resistance value and the right column resistance value of the corresponding hydraulic support at any target moment. It should be noted that the time granularity of the collection moment may be coarser (or larger, less precise) than the time granularity of the target moment. For example, the time granularity of the collection moment is hours, and the granularity of the target moment is minutes. It should also be noted that, similar to the first resistance value and the second resistance value, the first predicted resistance value may be used to indicate the average of the left column resistance value and the right column resistance value of the corresponding hydraulic support, and the time granularity of the first predicted resistance value may be the same as the time granularity of the collection moment.

[0151] Among them, the first set length can be pre-set, such as 100 meters, 120 meters, etc., and this application does not impose any restrictions on this.

[0152] As an example, assuming that the number of hydraulic supports is 150 and the current time period is 1 hour, the first resistance values ​​of the 150 hydraulic supports within 90 days before the current time period can be obtained, where each hydraulic support has a corresponding first resistance value every hour, for a total of 32,400 data; the second resistance value corresponding to each hydraulic support every minute in the current time period can also be obtained, for a total of 9,000 data.

[0153] It is understandable that the mining face will advance forward of the mining face as coal mining proceeds. Therefore, in an embodiment of the present application, based on the first resistance values ​​of multiple hydraulic supports within a set historical time period before the current time period and the second resistance values ​​within the current time period, an IDW (Inverse Distance Weighting) algorithm can be used to obtain the first predicted resistance values ​​of each hydraulic support at different positions or times as the mining face advances forward to a position at the first set length in front of the current mining face.

[0154] Step S305: Determine the target distance by pressing the step distance according to the most recently set number of cycles.

[0155] Among them, the set number of times can be pre-set, such as 3 times, etc., and this application does not limit its value.

[0156] As an example, assuming the number of times is set to n, the step distance can be compressed based on the most recent n cycles, and the target distance can be determined according to the following formula:

[0157] ; (7)

[0158] Where d is the target distance, d i The i-th cycle pressure step size among the most recent n cycles pressure step sizes.

[0159] Step S306: updating the first predicted resistance value of each hydraulic support at different positions in the target mining area to a set value.

[0160] The target mining area is the area within a second set length range in front of the mining face (in the same direction as the mining face's advance (i.e., mining direction)) when the mining face is located at the target distance from the current mining face. It should be noted that the size of the target mining area can be determined by the second set length, the number of hydraulic supports, and the width of the hydraulic supports.

[0161] The second set length may be pre-set, such as 3 meters, 4 meters, etc., and the application does not impose any restrictions on its value. Optionally, the second set length may be determined based on an average daily mining speed.

[0162] like Figure 4 As shown, the current mining face is at position A, and the mining face advancement direction (i.e., mining direction) is as follows Figure 4 As shown in the direction of the middle arrow, the distance between position B and position A is the target distance, and area C is the area within the second set length d in front of position B. The area size of area C is d×(n*L), where n is the number of hydraulic supports and L is the support width.

[0163] Among them, the set value can be pre-set, for example, it can be 0, and this application does not limit its value.

[0164] That is, the predicted first predicted resistance values ​​of the hydraulic supports at different positions in the target mining area are adjusted and updated to the set values.

[0165] Step S307: Generate a first working resistance cloud map according to the updated first predicted resistance value.

[0166] In one example, a first working resistance cloud map is generated based on the first predicted resistance values ​​updated for each hydraulic support at different positions within the target mining area and the first predicted resistance values ​​in the unupdated area of ​​the first predicted resistance values ​​in the process of each hydraulic support advancing forward with the mining working face to the first set length position in front of the current mining working face.

[0167] As an example, assuming that there are 150 hydraulic supports and the current time period is 1 hour, the first resistance values ​​of the 150 hydraulic supports within 90 days before the current time period can be obtained, where each hydraulic support has a corresponding first resistance value every hour, for a total of 32,400 data points; the second resistance value corresponding to each hydraulic support every minute in the current time period can also be obtained, for a total of 9,000 data points; the current working surface location is as follows: Figure 5 As shown, at position a, according to the updated first predicted resistance values ​​of each hydraulic support within 100 meters in front of the current mining face, the first working resistance cloud diagram is generated as follows: Figure 5 The red box in the figure corresponds to area C. Figure 5 As shown in Figure 5 150 hydraulic supports are arranged in the middle layout direction, and the mining face advances forward in the mining direction.

[0168] Step S308, based on the first resistance value change of multiple hydraulic supports within the set historical time period and the second resistance value change in the current time period, predict the predicted resistance value change of each hydraulic support in the process of advancing forward with the mining working face to the first set length position in front of the current mining working face.

[0169] Among them, when the current time period includes multiple target moments, the change in the first resistance value can be the change in the first resistance value of the corresponding hydraulic support at any two adjacent collection moments within the historical time period set before the current time period, and the change in the second resistance value can be the change in the second resistance value of the corresponding hydraulic support at any two adjacent target moments.

[0170] In an embodiment of the present application, based on the change in the first resistance value of multiple hydraulic supports within a set historical time period and the change in the second resistance value in the current time period, the IDW algorithm can be used to obtain the predicted change in resistance value of each hydraulic support as it advances forward with the mining working face to the first set length position in front of the current mining working face.

[0171] Step S309: Generate a first time-acceleration cloud map based on the predicted resistance value change; wherein the first time-acceleration cloud map has the same size as the first working resistance cloud map.

[0172] Step S310: superimpose the first working resistance cloud map and the first time speed increase cloud map to obtain a first superimposed working resistance cloud map.

[0173] As an example, when both the first working resistance cloud map and the first time speed increase cloud map are RGB (Red, Green, Blue) images, for any pixel in the first working resistance cloud map, there is a pixel in the first time speed increase cloud map that matches the position of the pixel. The pixels in the first working resistance cloud map and the first time speed increase cloud map that match the position of the pixel are recorded as a matching pixel pair. For any pair of matching pixel pairs, in any color channel, the minimum value of the matching pixel pair in that color channel is obtained. For example, if there is a matching pixel pair of pixel 1 and pixel 2, pixel 1 belongs to the first working resistance cloud map and pixel 2 belongs to the first time speed increase cloud map, and in the R channel, the value of pixel 1 in the R channel is 156 and the value of pixel 2 in the R channel is 99, then the minimum value of 99 is taken. The minimum value of the matching pixel pair in each color channel is used to generate the color value of the pixel in the first superimposed working resistance cloud map that matches the position of the matching pixel pair. In this way, the first superimposed working resistance cloud map can be obtained.

[0174] Step S311: determine the first superimposed working resistance cloud map as the target working resistance cloud map at the current moment.

[0175] Optionally, in some embodiments, a second working resistance cloud map can be generated based on the second resistance value; a second time acceleration cloud map can be generated based on the change in the second resistance value; wherein the second time acceleration cloud map has the same size as the second working resistance cloud map; the second working resistance cloud map and the second time acceleration cloud map are superimposed to obtain a second superimposed working resistance cloud map; the second superimposed working resistance cloud map is determined as the target working resistance cloud map at the current moment. Figure 5 The yellow box corresponds to area B.

[0176] Optionally, in some embodiments, the first superimposed working resistance cloud map and the second superimposed working resistance cloud map may be merged to obtain a merged working resistance cloud map; and the merged working resistance cloud map may be determined as the target working resistance cloud map at the current moment.

[0177] Step S312: Perform a second analysis on the target working resistance cloud diagrams of the multiple hydraulic supports at the current moment to obtain a third risk sub-index value.

[0178] It should be noted that the execution process of step S312 can refer to the execution process of any embodiment of the present application and will not be repeated here.

[0179] As a possible implementation method, the target working resistance cloud map can be converted into the HSV (Hue, Saturation, Value) color space to obtain the target image; the hue of the pixels in the target image is analyzed to determine the target proportion of pixels in the target image with the target color; based on the target proportion and the working stage of multiple hydraulic supports at the current moment, the value of the third risk sub-indicator is determined.

[0180] The colors of the pixels in the target image may include four colors: red (hue 0°), yellow (hue 60°), green (hue 120°), and blue (hue 240°).

[0181] Among them, in the target image, the resistance value can be positively correlated with the hue, that is, the higher the hue value, the greater the resistance value of the hydraulic support indicated by the corresponding area.

[0182] The target color may be at least one of red, yellow, green and blue, for example, red and yellow, and this application does not impose any limitation on this.

[0183] As an example, for any hydraulic support, when the column pressure of the hydraulic support at the current moment is >25MPa and the pressure fluctuation rate is <±2MPa / min, the top beam horizontality error is <1.5°, the pressure difference between adjacent supports is <10MPa, and the guard plate deployment angle is >75°, it indicates that the hydraulic support is in the support stage at the current moment. When the pushing cylinder speed of the hydraulic support at the current moment is 0.1~0.3m / s, the column pressure relief time is 3-5s, the top beam forward inclination angle suddenly increases by >8°, and the infrared positioning deviation is <50mm, it indicates that the hydraulic support is in the moving stage at the current moment.

[0184] As an example, assuming that the target colors are red and yellow, when more than 80% of the hydraulic supports are currently in the support stage, the relationship between the target proportion and the third risk sub-indicator value R3 is shown in Table 3:

[0185] Table 3 Relationship between target proportion and the third risk sub-index value R3

[0186]

[0187] When more than 80% of the hydraulic supports are in the moving stage at the current moment, the relationship between the target proportion and the third risk sub-indicator value R3 is shown in Table 2:

[0188] Table 4 Relationship between target proportion and the third risk sub-index value R3

[0189]

[0190] In other cases, the third risk sub-indicator value R3 can be set to 0.5.

[0191] Step S313: identifying target abnormality types of the plurality of hydraulic supports based on the first risk sub-indicator value, the second risk sub-indicator value, and the third risk sub-indicator value.

[0192] It should be noted that the execution process of step S313 can refer to the execution process of any embodiment of the present application and will not be repeated here.

[0193] The method for identifying abnormal working resistance of a hydraulic support in an embodiment of the present application predicts the first predicted resistance value of each hydraulic support in the process of advancing with the mining working face to a first set length position in front of the current mining working face based on the first resistance value of multiple hydraulic supports within the most recent first set time period and the second resistance value within the most recent second set time period; determines the target distance based on the pressure step distance of the most recent set number of cycles; updates the first predicted resistance value of each hydraulic support at different positions in the target mining area to a set value; wherein the target mining area is the area within the second set length range in front of the mining working face when the mining working face is located at the target distance from the current mining working face; according to the updated first The resistance value is predicted to generate a first working resistance cloud map; based on the change in the first resistance value of multiple hydraulic supports within the most recent first set time period and the change in the second resistance value within the most recent second set time period, the predicted change in the resistance value of each hydraulic support as it advances forward with the mining working face to a position at a first set length in front of the current mining working face is predicted; based on the predicted change in the resistance value, a first time acceleration cloud map is generated; wherein the first time acceleration cloud map is the same size as the first working resistance cloud map; the first working resistance cloud map and the first time acceleration cloud map are superimposed to obtain a first superimposed working resistance cloud map; the first superimposed working resistance cloud map is determined as the target working resistance cloud map at the current moment. Thus, by integrating the resistance value and the change in the resistance value, the target working resistance cloud map is effectively and accurately obtained, so that the constructed target working resistance cloud map can more truly reflect the resistance value of each hydraulic support at different positions as the mining work advances.

[0194] In order to clearly illustrate the hydraulic support working resistance abnormality identification method of the present application, the above process is described in detail below with reference to an example. As an example, the hydraulic support working resistance abnormality identification method may include the following steps:

[0195] Step 1: Obtain sensor data such as support resistance pressure parameters, support column displacement data, and coal mining machine mining parameters;

[0196] The OPC (Object Linking and Embedding (OLE) for Process Control) protocol, a key standard for data exchange and device communication in industrial automation, collects support resistance and pressure parameters, shearer mining parameters, and support column displacement data from the support electro-hydraulic control system in real time. Support resistance and pressure parameters include support number, time, left column resistance value, right column resistance value, support phase time (which may include start and / or end time), column lowering phase start time (which may include start and / or end time), support shifting phase start time (which may include start and / or end time), column raising phase time (which may include start and / or end time), support posture, working cycle time, safety valve opening pressure, safety valve opening duration, plunger retraction during safety valve opening, safety valve closing pressure, interval between safety valve openings, and support stiffness. Shearer mining parameters include shearer number, mining progress, mining height, shearer traction direction, and timestamp. Support column displacement data includes support number, timestamp, and plunger height.

[0197] Optionally, in some embodiments, after obtaining the above data, the required data format can be constructed in accordance with the requirements of the specification, data access content, message queue name, message content, and data item description, and uploaded to the corresponding Kafka message queue.

[0198] Step 2: Use Logstash technology to integrate multi-source data;

[0199] By calling the Logstash component and configuring the Pipelines file according to the Kafka topic, the uploaded message information can be obtained from Kafka. The string type message is parsed into JSON format according to the rules, and fields such as unique identifier, processing time, Kafka topic name, and Kafka displacement are added. It is stored in the corresponding table of Clickhouse as a backup of the original data and sent to the unified warn_json topic of Kafka. For the JSON format, only the unique identifier, processing time, Kafka topic name, and Kafka displacement fields need to be added and sent to the warn_json topic. Through the above data processing, multi-source data is uniformly aggregated into the warn_json topic of Kafka. Subsequent data is obtained from warn_json in real time (that is, the current time period) data.

[0200] Step 3: Construct a prediction model for the opening of the support resistance safety valve (referred to as the target prediction model in this application);

[0201] Step 4: First, clean the real-time data using a Bloom filter. Then, construct a feature vector for the cleaned real-time data. This feature vector is input into the S-bracket resistance safety valve opening prediction model to predict the safety valve opening probability.

[0202] Step 5: Determine the support resistance safety valve warning risk coefficient R1 (referred to as the first risk sub-indicator value in this application) based on the safety valve opening probability;

[0203] Step 6: Determine the geological risk coefficient R2 (referred to as the second risk sub-index value in this application) using the finite element method stope coal rock structure model (i.e., MiningFEM model);

[0204] Step 7: Based on the resistance cloud map (referred to as the target working resistance cloud map in this application) dynamically fused with multiple indicators, determine the stent regional risk coefficient R3 (referred to as the third risk sub-indicator value in this application);

[0205] Step 8: Use the support resistance adaptive dynamic weight evaluation model to obtain the target anomaly types of multiple hydraulic supports.

[0206] Among them, the support resistance adaptive dynamic weight assessment model dynamically adjusts the weights of each risk factor by comprehensively considering the support resistance safety valve warning risk coefficient R1, geological risk coefficient R2 and support area risk coefficient R3, thereby determining the abnormal type of the hydraulic support.

[0207] It should be noted that when it is determined that the support resistance stress is abnormal, a real-time alarm can be triggered and an early warning processing mechanism can be triggered.

[0208] In summary, the hydraulic support working resistance abnormality identification method of the present application can automatically and quickly identify the abnormal type of hydraulic support working resistance in combination with the support resistance safety valve warning risk, geological risk and support area risk, thereby improving the timeliness and effectiveness of the hydraulic support working resistance abnormality identification.

[0209] With the above Figures 1 to 3 Corresponding to the method for identifying abnormal working resistance of a hydraulic support provided in the embodiment, the present application also provides a device for identifying abnormal working resistance of a hydraulic support. Figures 1 to 3 The embodiment provides a method for identifying abnormal working resistance of a hydraulic support, so the implementation method of the method for identifying abnormal working resistance of a hydraulic support is also applicable to the device for identifying abnormal working resistance of a hydraulic support provided in the embodiment of the present application, and will not be described in detail in the embodiment of the present application.

[0210] Figure 6 This is a structural diagram of a device for identifying abnormal working resistance of a hydraulic support provided in another embodiment of the present application.

[0211] like Figure 6 As shown, the hydraulic support working resistance abnormality identification device 600 may include: a prediction module 601, a first determination module 602, a first analysis module 603, a second analysis module 604 and an identification module 605.

[0212] Among them, the prediction module 601 is used to predict the opening probability of the safety valve of each hydraulic support within the future target time period based on the working data of multiple hydraulic supports in the current time period during mining work, using a target prediction model; wherein the current time period includes the current moment.

[0213] The first determination module 602 is used to determine a first risk sub-index value according to each opening probability; wherein the first risk sub-index value is determined based on the number of target hydraulic supports among the multiple hydraulic supports whose opening probabilities meet the set conditions and whose layout positions are continuously adjacent.

[0214] The first analysis module 603 is used to perform a first analysis on the mining data of the mining work in the current time period to obtain a second risk sub-indicator value.

[0215] The second analysis module 604 is used to perform a second analysis on the target working resistance cloud diagrams of the multiple hydraulic supports at the current moment to obtain a third risk sub-index value.

[0216] The identification module 605 is configured to identify target abnormality types of the plurality of hydraulic supports based on the first risk sub-indicator value, the second risk sub-indicator value, and the third risk sub-indicator value.

[0217] In a possible implementation of an embodiment of the present application, the prediction module 601 is used to: perform statistical analysis on the working data of multiple hydraulic supports in the current time period to obtain at least one statistical feature; and based on at least one statistical feature, use a target prediction model to obtain the probability of opening the safety valve of each hydraulic support within the future target duration.

[0218] In a possible implementation of the embodiment of the present application, the current time period includes multiple target moments, and the work data includes at least one of the following:

[0219] The left and right bar resistance values ​​at any target moment;

[0220] The left initial support force and the right initial support force at any target moment;

[0221] The target working stage at any target moment and the first starting time of the target working stage;

[0222] The second start time and end time when the safety valve is in the open state in the most recent safety valve opening cycle in the historical record corresponding to any target time;

[0223] The plunger height data when the safety valve is in the open state during the most recent safety valve opening cycle in the historical records corresponding to any target time.

[0224] In a possible implementation of the embodiment of the present application, the statistical feature includes at least one of the following:

[0225] The target resistance value of each hydraulic support at any target time; wherein the target resistance value is determined based on the left column resistance value and the right column resistance value of the corresponding hydraulic support;

[0226] The qualified rate of the initial support force of the plurality of hydraulic supports at any target time; wherein the qualified rate of the initial support force is determined based on the left initial support force and the right initial support force of the plurality of hydraulic supports;

[0227] A working cycle time vector of each hydraulic support at any target time; wherein the working cycle time vector is generated based on the target working stage of the corresponding hydraulic support at the corresponding target time and the first starting time of the target working stage;

[0228] The target duration of each hydraulic support at any target time; wherein the target duration is based on the second start time and end time of the safety valve being in the open state during the most recent safety valve opening cycle of the corresponding hydraulic support in the corresponding historical records;

[0229] The target plunger retraction amount of each hydraulic support at any target moment; wherein, the target plunger retraction amount is determined based on the plunger height data of the corresponding hydraulic support when the safety valve is in the open state during the most recent safety valve opening cycle in the corresponding historical records.

[0230] In a possible implementation of the embodiment of the present application, the hydraulic support working resistance abnormality identification device 600 may further include:

[0231] The second determination module is used to: determine, for any target moment, whether the left initial support force and the right initial support force of any hydraulic support at the target moment are both qualified; when the left initial support force and the right initial support force of the hydraulic support at the target moment are both qualified, determine the hydraulic support as the first hydraulic support with qualified initial support force; count the first hydraulic supports among multiple hydraulic supports to obtain a first number; and determine the qualified rate of the initial support force of multiple hydraulic supports at the target moment based on the first number and the number of multiple hydraulic supports.

[0232] In a possible implementation of an embodiment of the present application, the hydraulic support working resistance abnormality identification device 600 may also include: a generation module, used to: for any target moment, determine the first duration of the target working stage of the hydraulic support at the target moment based on the time difference between the target moment and the first start time of the target working stage of the hydraulic support at the target moment; generate the working cycle time vector of the hydraulic support at the target moment according to the first duration of the target working stage of the hydraulic support at the target moment.

[0233] In a possible implementation of the embodiment of the present application, the hydraulic support working resistance abnormality identification device 600 may also include: an acquisition module for: predicting the first predicted resistance value of each hydraulic support in the process of advancing with the mining working face to the first set length position in front of the current mining working face based on the first resistance value of multiple hydraulic supports within the historical time period set before the current time period and the second resistance value in the current time period; determining the target distance based on the pressure step distance of the most recently set number of cycles; updating the first predicted resistance value of each hydraulic support at different positions in the target mining area to the set value; wherein the target mining area is the second set resistance value in front of the mining working face when the mining working face is located at the target distance from the current mining working face. an area within a length range; a first working resistance cloud map is generated according to the updated first predicted resistance value; according to the first resistance value change of multiple hydraulic supports within the set historical time length and the second resistance value change in the current time period, the predicted resistance value change of each hydraulic support in the process of advancing with the mining working face to the first set length position in front of the current mining working face is predicted; according to the predicted resistance value change, a first time acceleration cloud map is generated; wherein, the first time acceleration cloud map has the same size as the first working resistance cloud map; the first working resistance cloud map and the first time acceleration cloud map are superimposed to obtain a first superimposed working resistance cloud map; the first superimposed working resistance cloud map is determined as the target working resistance cloud map at the current moment.

[0234] In a possible implementation method of an embodiment of the present application, the acquisition module is also used to: generate a second working resistance cloud map based on the second resistance value; generate a second time acceleration cloud map based on the change in the second resistance value; wherein the second time acceleration cloud map and the second working resistance cloud map have the same size; superimpose the second working resistance cloud map and the second time acceleration cloud map to obtain a second superimposed working resistance cloud map; and determine the second superimposed working resistance cloud map as the target working resistance cloud map at the current moment.

[0235] In a possible implementation of an embodiment of the present application, the acquisition module is also used to: merge the first superimposed working resistance cloud map and the second superimposed working resistance cloud map to obtain a merged working resistance cloud map; and determine the merged working resistance cloud map as the target working resistance cloud map at the current moment.

[0236] In a possible implementation of an embodiment of the present application, the second analysis module 604 is used to: convert the target working resistance cloud map into the HSV color space to obtain a target image; wherein the target image includes four colors: red, yellow, green and blue; analyze the hue of the pixel points in the target image to determine the target proportion of the pixel points in the target image whose color is the target color; determine the third risk sub-indicator value based on the target proportion and the working stage of multiple hydraulic supports at the current moment.

[0237] The hydraulic support working resistance anomaly identification device of the embodiment of the present application predicts the opening probability of the safety valve of each hydraulic support within the future target time period based on the working data of multiple hydraulic supports in the current time period during mining work, using a target prediction model; wherein the current time period includes the current moment; according to each opening probability, a first risk sub-index value is determined; wherein the first risk sub-index value is determined based on the number of target hydraulic supports whose opening probability meets the set conditions and whose layout positions are continuously adjacent among the multiple hydraulic supports; a first analysis is performed on the mining data of the mining work in the current time period to obtain a second risk sub-index value; a second analysis is performed on the target working resistance cloud map of the multiple hydraulic supports at the current moment to obtain a third risk sub-index value; based on the first risk sub-index value, the second risk sub-index value and the third risk sub-index value, the target anomaly type of the multiple hydraulic supports is identified. In this way, the working data of the hydraulic support, the mining data of the mining work and the working resistance cloud map of the hydraulic support can be combined to automatically and quickly identify the abnormal working resistance stress results of the hydraulic support, thereby improving the timeliness and effectiveness of the recognition of the abnormal working resistance of the hydraulic support.

[0238] In order to implement the above embodiments, the present application also proposes an electronic device, comprising: a processor, and a memory communicatively connected to the processor; the memory stores computer execution instructions; the processor executes the computer execution instructions stored in the memory to implement the method for identifying abnormal working resistance of a hydraulic support provided in the above embodiments.

[0239] In order to implement the above embodiments, the present application also proposes a computer-readable storage medium, which stores computer-executable instructions. When the computer-executable instructions are executed by a processor, they are used to implement the method for identifying abnormal working resistance of a hydraulic support provided in the above embodiments.

[0240] In order to implement the above embodiments, the present application also proposes a computer program product, including a computer program, which, when executed by a processor, implements the method for identifying abnormal working resistance of a hydraulic support provided in the above embodiments.

[0241] The collection, storage, use, processing, transmission, provision and application of user personal information involved in this application are in compliance with relevant laws and regulations and do not violate public order and good morals.

[0242] It is important to note that personal information collected from users should be used for legitimate and reasonable purposes and should not be shared or sold beyond these legitimate uses. Furthermore, such collection / sharing should be conducted only after receiving the user's informed consent, including but not limited to notifying the user to read the user agreement / user notice and sign an agreement / authorization that includes the relevant user information before using the feature. Furthermore, any necessary steps must be taken to safeguard and secure access to such personal information and ensure that others with access to personal information comply with its privacy policy and procedures.

[0243] This application contemplates providing implementation options for users to selectively block the use or access of personal information data. Specifically, this application contemplates providing hardware and / or software to prevent or block access to such personal information data. Risks can be minimized by limiting data collection and deleting data once it is no longer needed. Furthermore, where applicable, such personal information can be de-identified to protect user privacy.

[0244] In the descriptions of the foregoing embodiments, the reference terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" mean that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present application. In this specification, the schematic expressions of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or more embodiments or examples in a suitable manner. In addition, those skilled in the art may combine and combine different embodiments or examples described in this specification and features of different embodiments or examples, unless they are mutually inconsistent.

[0245] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed to control or imply relative importance or implicitly indicate the number of technical features being controlled. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one such feature. Throughout the description of this application, "plurality" means at least two, such as two, three, etc., unless otherwise specifically defined.

[0246] Any process or method description in a flowchart or otherwise described herein may be understood to represent a module, segment or portion of code comprising one or more executable instructions for implementing the steps of a custom logical function or process, and the scope of the preferred embodiments of the present application includes alternative implementations in which functions may be performed out of the order shown or discussed, including performing functions in a substantially simultaneous manner or in the reverse order depending on the functions involved, which should be understood by those skilled in the art to which the embodiments of the present application belong.

[0247] The logic and / or steps represented in a flowchart or otherwise described herein, for example, can be considered a sequenced list of executable instructions for implementing the 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 (e.g., a computer-based system, a system including a processor, or other system that can fetch and execute instructions from an instruction execution system, apparatus, or device). For purposes of this specification, a "computer-readable medium" is any device that can contain, store, communicate, propagate, or transport a program for use by, or in conjunction with, an instruction execution system, apparatus, or device. More specific examples (not exhaustive) of computer-readable media include: an electrical connection with one or more wires (electronic devices), a portable computer disk cartridge (magnetic device), random access memory (RAM), read-only memory (ROM), erasable and programmable read-only memory (EPROM or flash memory), fiber optic devices, and a portable compact disc read-only memory (CDROM). Furthermore, the computer-readable medium may even be 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 and then editing, interpreting or processing it in another suitable manner if necessary, and then storing it in a computer memory.

[0248] It should be understood that various parts of this application can be implemented using hardware, software, firmware, or a combination thereof. In the above-described embodiments, multiple steps or methods can be implemented using software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented using hardware, as in another embodiment, any one of the following technologies known in the art or a combination thereof can be used: a discrete logic circuit having logic gate circuits for implementing logical functions on data signals, an application-specific integrated circuit having suitable combinational logic gate circuits, a programmable gate array (PGA), a field-programmable gate array (FPGA), etc.

[0249] Those skilled in the art will understand that all or part of the steps in the method of the above embodiment can be completed by instructing related hardware through a program, and the program can be stored in a computer-readable storage medium. When the program is executed, it includes one or a combination of the steps of the method embodiment.

[0250] In addition, the functional units in the various embodiments of the present application may be integrated into a processing module, or each unit may exist physically separately, or two or more units may be integrated into a module. The above-mentioned integrated module may be implemented in the form of hardware or in the form of a software functional module. If the integrated module is implemented in the form of a software functional module and sold or used as an independent product, it may also be stored in a computer-readable storage medium.

[0251] The storage medium mentioned above may be a read-only memory, a magnetic disk, or an optical disk, etc. Although the embodiments of the present application have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting the present application. Persons skilled in the art may make changes, modifications, substitutions, and variations to the above embodiments within the scope of the present application.

Claims

1. A method for identifying abnormal working resistance of a hydraulic support, characterized in that: The method comprises: Based on the working data of multiple hydraulic supports in the current time period during mining work, a target prediction model is used to predict the opening probability of the safety valve of each hydraulic support within a future target time period; wherein the current time period includes the current moment; Determining a first risk sub-index value based on each of the opening probabilities; wherein the first risk sub-index value is determined based on the number of target hydraulic supports among the plurality of hydraulic supports whose opening probabilities meet a set condition and whose layout positions are consecutively adjacent; Performing a first analysis on mining data of the mining work in the current time period to obtain a second risk sub-indicator value, wherein the mining data includes a shearer traction direction, a mining height, and a support stiffness; Performing a second analysis on the target working resistance cloud graphs of the plurality of hydraulic supports at the current moment to obtain a third risk sub-indicator value; identifying target abnormality types of the plurality of hydraulic supports based on the first risk sub-indicator value, the second risk sub-indicator value, and the third risk sub-indicator value; The process of obtaining the target work resistance cloud map at the current moment includes: Based on the first resistance values ​​of the plurality of hydraulic supports within a set historical time period before the current time period and the second resistance values ​​in the current time period, a first predicted resistance value of each hydraulic support in the process of advancing along with the mining working face to a position at a first set length in front of the current mining working face is predicted; Determine the target distance by pressing the step distance according to the most recently set number of cycles; Updating the first predicted resistance value of each hydraulic support at different positions in the target mining area to a set value; wherein the target mining area is the area within a second set length range in front of the mining working face when the mining working face is located at the target distance from the current mining working face; generating a first working resistance cloud map according to the updated first predicted resistance value; Based on the first resistance value changes of the plurality of hydraulic supports within the set historical time period and the second resistance value changes in the current time period, a predicted resistance value change of each hydraulic support in the process of advancing forward along the mining working face to a position at a first set length in front of the current mining working face is predicted; generating a first time-acceleration cloud graph according to the predicted resistance value change; wherein the first time-acceleration cloud graph has the same size as the first working resistance cloud graph; Superimposing the first working resistance cloud map and the first time speed increase cloud map to obtain a first superimposed working resistance cloud map; The first superimposed working resistance cloud map is determined as the target working resistance cloud map at the current moment.

2. The method according to claim 1, characterized in that The target prediction model is used to predict the opening probability of the safety valve of each hydraulic support within the future target time period based on the working data of multiple hydraulic supports in the current time period during the mining operation, including: Performing statistical analysis on the working data of the plurality of hydraulic supports in the current time period to obtain at least one statistical feature; The target prediction model is used according to the at least one statistical feature to obtain the opening probability of the safety valve of each hydraulic support within a future target duration.

3. The method according to claim 2, characterized in that The current time period includes multiple target moments, and the work data includes at least one of the following: the left-bar resistance value and the right-bar resistance value at any said target moment; The left initial support force and the right initial support force at any target moment; The target working stage at any target moment and the first starting time of the target working stage; The second start time and end time when the safety valve is in the open state in the most recent safety valve opening cycle in the historical records corresponding to any of the target moments; The plunger height data when the safety valve is in the open state during the most recent safety valve opening cycle in the historical records corresponding to any of the target moments.

4. The method according to claim 3, characterized in that The statistical features include at least one of the following: a target resistance value of each hydraulic support at any target moment; wherein the target resistance value is determined based on the left column resistance value and the right column resistance value of the corresponding hydraulic support; The qualified rate of the initial support force of the plurality of hydraulic supports at any target time; wherein the qualified rate of the initial support force is determined based on the left initial support force and the right initial support force of the plurality of hydraulic supports; A working cycle time vector of each hydraulic support at any target moment; wherein the working cycle time vector is generated based on the target working stage of the corresponding hydraulic support at the corresponding target moment and the first starting time of the target working stage; The target duration of each hydraulic support at any target moment; wherein the target duration is based on the second start time and end time of the safety valve being in the open state during the most recent safety valve opening cycle of the corresponding hydraulic support in the corresponding historical records; The target piston retraction amount of each hydraulic support at any target moment; wherein, the target piston retraction amount is determined based on the piston height data of the corresponding hydraulic support when the safety valve is in the open state in the most recent safety valve opening cycle in the corresponding historical records.

5. The method according to claim 4, characterized in that The process of determining the qualified rate of the initial support force of the plurality of hydraulic supports at any target time includes: For any of the target moments, determining whether the left initial support force and the right initial support force of any of the hydraulic supports at the target moment are both qualified; When the left initial supporting force and the right initial supporting force of the hydraulic support at the target time are both qualified, determining the hydraulic support as the first hydraulic support with qualified initial supporting force; Counting the first hydraulic supports among the plurality of hydraulic supports to obtain a first quantity; An initial support force qualification rate of the plurality of hydraulic supports at the target time is determined according to the first number and the number of the plurality of hydraulic supports.

6. The method according to claim 4, characterized in that For any of the hydraulic supports, the process of generating the working cycle time vector of the hydraulic support at any of the target moments includes: For any of the target moments, determining a first duration of the target working phase of the hydraulic support at the target moment based on a time difference between the target moment and a first start time of the target working phase of the hydraulic support at the target moment; A working cycle time vector of the hydraulic support at the target moment is generated according to a first duration of the target working phase of the hydraulic support at the target moment.

7. The method according to claim 6, characterized in that The method further comprises: generating a second working resistance cloud diagram according to the second resistance value; generating a second time-acceleration cloud graph according to the second resistance value change; wherein the second time-acceleration cloud graph has the same size as the second working resistance cloud graph; Superimposing the second working resistance cloud map and the second time speed increase cloud map to obtain a second superimposed working resistance cloud map; The second superimposed work resistance cloud map is determined as the target work resistance cloud map at the current moment.

8. The method according to claim 7, characterized in that The method further comprises: Merging the first superimposed working resistance cloud map and the second superimposed working resistance cloud map to obtain a merged working resistance cloud map; The combined work resistance cloud map is determined as the target work resistance cloud map at the current moment.

9. The method according to any one of claims 1 to 8, characterized in that The second analysis of the target working resistance cloud graphs of the plurality of hydraulic supports at the current moment to obtain a third risk sub-index value includes: Convert the target working resistance cloud map into the HSV color space to obtain a target image; wherein the target image includes four colors: red, yellow, green and blue; Analyzing the hue of pixels in the target image to determine a target ratio of pixels in the target image having a target color; The third risk sub-indicator value is determined according to the target proportion and the working stage of the multiple hydraulic supports at the current moment.

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