Method and device for processing floor washing data for industrial dust-contaminated floors

Abstract

The present application provides a method and device for processing floor washing data for industrial dust-contaminated ground. The method obtains a dust monitoring data set of the industrial area to be cleaned through a dust monitoring sensor array, and sends the dust monitoring data set to a scheduling cloud platform, so that the scheduling cloud platform determines the target floor washing water storage capacity according to the target planning path of the target floor washing device in the floor washing device cluster and the dust monitoring data set, and sends the target floor washing water storage capacity to the target floor washing device, and when the target floor washing device determines that the current remaining water storage capacity uploaded by the target floor washing device is less than the target floor washing water storage capacity, a water adding instruction is generated to instruct the target floor washing device to add water. Thus, the target floor washing water storage capacity is intelligently determined according to the dust monitoring data set and the target planning path of the target floor washing device, ensuring that the floor washing device has sufficient water storage when performing tasks, and avoiding the problem of incomplete cleaning due to insufficient water storage.

Description

Technical Field

[0001] The present application relates to data processing technology, and more particularly to a method and device for processing floor washing data for industrial dust-contaminated floors. Background Art

[0002] Dust pollution is a common problem in industrial production, especially in the steel, cement, chemical and other industries. Traditional dust pollution floor cleaning methods usually rely on manual experience to judge the amount of water for floor washing, which has many shortcomings. First, manual judgment of the amount of water for floor washing is often not accurate enough, which can easily lead to different floor washing effects. Excessive water storage will cause waste of water resources, while insufficient water storage will affect the cleaning effect.

[0003] In addition, with the continuous expansion of industrial production scale and the improvement of automation level, higher requirements are placed on the efficiency and effectiveness of dust-contaminated floor cleaning. The traditional method of relying on manual judgment of water storage capacity can no longer meet the needs of modern industrial production. Summary of the invention

[0004] The present application provides a method and device for processing floor washing data for industrial dust-contaminated floors, which are used to intelligently determine the target floor washing water storage volume, ensure that the floor washing device has sufficient water storage when performing tasks, and avoid the problem of incomplete cleaning due to insufficient water storage.

[0005] In a first aspect, the present application provides a method for processing floor scrubbing data of industrial dust-contaminated ground, which is applied to a floor scrubbing device scheduling system, wherein the floor scrubbing device scheduling system includes a floor scrubbing device cluster, a scheduling cloud platform, and a dust monitoring sensor array, wherein each floor scrubbing device in the floor scrubbing device cluster and each dust monitoring sensor in the dust monitoring sensor array are respectively connected to the scheduling cloud platform for communication; the method includes:

[0006] Acquire a dust monitoring data set of the industrial area to be cleaned through the dust monitoring sensor array, and send the dust monitoring data set to the scheduling cloud platform, the industrial area to be cleaned includes a grid array to be cleaned, each grid to be cleaned in the grid array to be cleaned is arranged with at least one dust monitoring sensor in the dust monitoring sensor array, and the dust monitoring data corresponding to each grid to be cleaned in the grid array to be cleaned in the dust monitoring data set includes dust particle concentration characteristic data and dust particle size characteristic data;

[0007] The scheduling cloud platform determines a target floor-washing water storage capacity according to a target planning path of a target floor-washing device in the floor-washing device cluster and the dust monitoring data set, and sends the target floor-washing water storage capacity to the target floor-washing device, wherein the target planning path is a planning path for performing floor-washing operations on some of the grids to be cleaned in the grid array to be cleaned;

[0008] If the target floor scrubber determines that the current remaining water storage volume uploaded by the target floor scrubber is less than the target floor scrubber water storage volume, the target floor scrubber generates a water adding instruction to instruct to perform a water adding operation on the target floor scrubber.

[0009] In the above scheme, dust data is collected in real time by dust monitoring sensors deployed at different locations. These data are transmitted to the central scheduling cloud platform. The scheduling cloud platform intelligently determines the target floor washing water storage capacity based on the dust monitoring data set and the target planning path of the target floor washing device, ensuring that the floor washing device has sufficient water storage when performing tasks, avoiding the problem of incomplete cleaning due to insufficient water storage. In addition, when the target floor washing device finds that the current remaining water storage capacity is insufficient, it automatically generates a water addition instruction, which improves the automation degree and response speed of the floor washing operation, reduces manual intervention and downtime waiting time, and further improves the overall floor washing efficiency for industrial dust-contaminated floors.

[0010] Optionally, before the scheduling cloud platform determines the target floor scrubbing water storage capacity according to the target planning path of the target floor scrubbing device in the floor scrubbing device cluster and the dust monitoring data set, the method further includes:

[0011] The scheduling cloud platform determines the floor washing water demand corresponding to each grid to be cleaned in the grid array according to the dust monitoring data corresponding to each grid to be cleaned in the dust monitoring data set.

[0012] In the above scheme, before the scheduling cloud platform determines the target floor washing water storage volume, it first accurately calculates the corresponding floor washing water demand based on the dust monitoring data (including dust particle concentration and size characteristic data) of each grid to be cleaned, thereby ensuring the refined analysis of the floor washing demand and making the subsequent floor washing planning more scientific and reasonable. By separately calculating the floor washing water demand of each grid to be cleaned, the scheduling cloud platform can more accurately evaluate the overall demand of the entire industrial area to be cleaned. This helps to optimize the allocation of resources before the floor washing operation begins, such as reasonably arranging the number of floor washing devices, water storage, etc., so as to avoid waste or shortage of resources. In addition, based on the refined floor washing water demand analysis, the scheduling cloud platform can formulate a floor washing plan that is more in line with the actual situation, which not only helps to improve the pertinence of the floor washing operation and reduce ineffective labor, but also shortens the floor washing cycle to a certain extent and improves the overall floor washing efficiency. In addition, by obtaining dust monitoring data in real time and dynamically calculating the floor washing water demand, the floor washing system can be adaptively adjusted according to the pollution conditions of the industrial area, so that the floor washing system can cope with various complex pollution conditions and ensure the stability and reliability of the floor washing effect.

[0013] Optionally, the scheduling cloud platform determines a target floor-washing water storage capacity according to a target planning path of a target floor-washing device in the floor-washing device cluster and the dust monitoring data set, including:

[0014] The scheduling cloud platform determines the dust particle concentration deviation characteristic value of the industrial area to be cleaned according to the dust monitoring data corresponding to each to-be-cleaned grid in the to-be-cleaned grid array in the dust monitoring data set;

[0015] The scheduling cloud platform determines the dust particle size deviation characteristic value of the industrial area to be cleaned according to the dust monitoring data corresponding to each to-be-cleaned grid in the to-be-cleaned grid array in the dust monitoring data set;

[0016] The scheduling cloud platform determines the target floor scrubbing water storage capacity according to the dust particle concentration deviation characteristic value, the dust particle size deviation characteristic value, the target planning path, and the dust monitoring data set.

[0017] In the above scheme, the scheduling cloud platform not only considers the dust monitoring data of a single grid to be cleaned, but also comprehensively evaluates the distribution and severity of dust pollution from a global perspective by calculating the characteristic value of dust particle concentration deviation and the characteristic value of dust particle size deviation in the entire industrial area to be cleaned, thereby providing a key basis for the subsequent formulation of a scientific floor washing strategy. After determining the characteristic value of dust pollution, the scheduling cloud platform can more accurately determine the target floor washing water storage volume in combination with the target planning path of the target floor washing device. This ensures that the floor washing device can carry an appropriate amount of water when performing tasks, and will not affect the cleaning effect due to insufficient water storage, nor will it increase unnecessary burden and energy consumption due to excessive water storage. In addition, for areas with different degrees of dust pollution, the scheduling cloud platform can flexibly adjust the allocation of floor washing water storage volume. For areas with severe pollution, the floor washing water storage volume is appropriately increased to ensure the cleaning effect; for areas with less pollution, the floor washing water storage volume is appropriately reduced to save resources. This differentiated treatment method enhances the pertinence and effectiveness of floor washing operations. Throughout the entire process, the scheduling cloud platform fully utilized its advantages in data processing, analysis and decision support, and realized the intelligent management of floor washing operations, which not only reduced the degree of manual intervention, but also improved the accuracy and reliability of floor washing operations.

[0018] Optionally, the scheduling cloud platform determines the target floor scrubbing water storage capacity according to the dust particle concentration deviation characteristic value, the dust particle size deviation characteristic value, the target planning path, and the dust monitoring data set, including:

[0019] The scheduling cloud platform determines a target sequence of grids to be cleaned from the array of grids to be cleaned according to the target planning path;

[0020] The scheduling cloud platform determines the required water volume compensation coefficient according to the dust particle concentration deviation characteristic value and the dust particle size deviation characteristic value;

[0021] The scheduling cloud platform determines the target floor-washing water storage volume according to the to-be-washed grid sequence and the floor-washing water demand corresponding to each to-be-washed grid in the to-be-washed grid array and the required water compensation coefficient.

[0022] In the above scheme, the scheduling cloud platform accurately determines the target sequence of grids to be cleaned from the array of grids to be cleaned according to the target planning path. By analyzing the characteristic value of dust particle concentration deviation and the characteristic value of dust particle size deviation, the scheduling cloud platform can intelligently calculate the required water compensation coefficient, thereby fully considering the differences in dust pollution in different areas. Through the dynamic adjustment of the compensation coefficient, the rationality and scientificity of the floor washing water storage volume are ensured. After determining the target sequence of grids to be cleaned and the required water volume for floor washing, the scheduling cloud platform combines the required water compensation coefficient to calculate the target floor washing water storage volume, which not only considers the actual floor washing demand of each grid, but also takes into account the overall pollution characteristics and floor washing planning, making the determination of floor washing water storage volume more accurate and efficient. Through the above calculation of floor washing water storage volume, the scheduling cloud platform can effectively avoid the waste or shortage of water resources in the floor washing process, which not only improves the utilization efficiency of water resources, but also reduces the cost of floor washing operations, and enhances the economy and sustainability of the entire floor washing system. In addition, through the above solution, the floor washing system can also automatically adjust the floor washing water storage volume according to the real-time dust pollution situation in the industrial area, thereby enhancing the adaptability and flexibility of the system to different pollution conditions.

[0023] Optionally, after acquiring the dust monitoring data set of the industrial area to be cleaned through the dust monitoring sensor array and sending the dust monitoring data set to the scheduling cloud platform, the method further includes:

[0024] The scheduling cloud platform determines a target grid to be cleaned from the grid array to be cleaned according to the dust monitoring data set, and determines the target grid to be cleaned as the starting grid of the target planning path, wherein the target grid to be cleaned is a dust pollution source grid in the grid array to be cleaned.

[0025] In the above scheme, through the dust monitoring data set obtained by the dust monitoring sensor array, the scheduling cloud platform can accurately identify the dust pollution source grid in the industrial area to be cleaned, and then determine the dust pollution source grid as the starting grid of the target planning path, so that the floor washing device can directly clean the most polluted area at the beginning of the operation, thereby improving the efficiency of the floor washing operation. In addition, starting the floor washing operation from the pollution source grid can ensure that the floor washing device can give priority to removing dust pollution that has the greatest impact on the environment and production within a limited amount of water and time. This targeted operation method helps to improve the overall floor washing effect and reduce further pollution. This automated and intelligent operation method not only improves the efficiency of floor washing operations, but also reduces dependence on manual intervention and reduces the possibility of human error.

[0026] Optionally, the scheduling cloud platform determines a target grid to be cleaned from the array of grids to be cleaned according to the dust monitoring data set, including:

[0027] The scheduling cloud platform obtains first dust particle concentration characteristic data of a first grid to be cleaned in the grid array to be cleaned;

[0028] The scheduling cloud platform determines a set of adjacent grids to be cleaned from the array of grids to be cleaned according to the first grid to be cleaned, wherein the grids to be cleaned in the set of adjacent grids to be cleaned are adjacent grids to the first grid to be cleaned;

[0029] If the scheduling cloud platform determines that the first dust particle concentration characteristic data is greater than the dust particle concentration characteristic data of any grid to be cleaned in the set of adjacent grids to be cleaned, the first grid to be cleaned is determined to be the target grid to be cleaned.

[0030] In the above scheme, by comparing the dust particle concentration characteristic data of each grid in the grid array to be cleaned and its adjacent grids, the grid with the highest dust concentration, that is, the pollution source grid, can be quickly and accurately identified. This local comparison method reduces the complexity of the global search and improves the recognition efficiency. In addition, by considering the dust concentration data of adjacent grids, the scheduling cloud platform can more comprehensively evaluate the pollution status of each grid. This evaluation method based on spatial relationships helps to reduce misjudgments caused by abnormalities in a single data point and improves the accuracy of identifying pollution source grids. This method refines the processing process of the dust monitoring data set into a one-by-one comparison of a single grid and its adjacent grids, making the data processing process clearer and more orderly, helping to reduce data processing time and improve system response speed. In addition, by quickly and accurately identifying the pollution source grid and determining it as the starting grid for the floor washing operation, the scheduling cloud platform can guide the floor washing device to prioritize the most polluted areas for cleaning, which helps to improve the overall efficiency of the floor washing operation.

[0031] Optionally, after determining the target to-be-cleaned grid as the starting grid of the target planning path, the method further includes:

[0032] The scheduling cloud platform determines the grid to be cleaned with the highest dust particle concentration characteristic data in the set of adjacent grids to be cleaned as the second grid to be cleaned, and determines the second grid to be cleaned as the next grid to be cleaned of the target grid to be cleaned on the target planning path;

[0033] The scheduling cloud platform takes the target grid to be cleaned as the center, the second grid to be cleaned as the starting point, and the first direction around the target grid to be cleaned as the planning direction, and sequentially sorts the adjacent grids to be cleaned to generate the target planning path.

[0034] In the above scheme, after determining the target grid to be cleaned (i.e., the pollution source grid) as the starting point, the grid with the second highest concentration is selected as the next grid to be cleaned according to the dust concentration characteristic data of the adjacent grids. This progressive path planning method ensures that the floor washing operation can be carried out in order from high to low pollution level, optimizes the floor washing path, and improves the operation efficiency. Then, by orderly sorting around the target grid to be cleaned (pollution source grid), the scheduling cloud platform can generate a continuous and complete floor washing planning path. This path planning method helps to reduce the repeated cleaning and omission phenomenon of the floor washing device during the operation process, and improves the quality of the floor washing operation. In addition, the adjacent high-concentration pollution grids are included in the floor washing planning path in turn, so that the floor washing device can maintain a high degree of continuity during the operation process. This continuity helps to reduce the waiting time and energy consumption of the floor washing device when changing the operation area, and improves the overall operation efficiency. The above scheme is not only suitable for the cleaning operation of a single point pollution source, but also can flexibly respond to complex and changeable pollution distribution. By gradually expanding the floor washing path, the scheduling cloud platform can gradually cover the entire area to be cleaned, ensuring the comprehensiveness and thoroughness of the floor washing operation.

[0035] In a second aspect, the present application provides a floor cleaning data processing device for industrial dust-contaminated floors, comprising:

[0036] an acquisition module, configured to acquire a dust monitoring data set of an industrial area to be cleaned, wherein the industrial area to be cleaned includes a grid array to be cleaned, each grid to be cleaned in the grid array to be cleaned is arranged with at least one dust monitoring sensor in the dust monitoring sensor array, and the dust monitoring data corresponding to each grid to be cleaned in the grid array to be cleaned in the dust monitoring data set includes dust particle concentration characteristic data and dust particle size characteristic data;

[0037] a processing module, configured to determine a target floor-washing water storage capacity according to a target planning path of a target floor-washing device and the dust monitoring data set, wherein the target planning path is a planning path for performing floor-washing operations on a portion of the grids to be cleaned in the grid array to be cleaned;

[0038] The indication module is used to generate a water adding indication to indicate a water adding operation when it is determined that the current remaining water storage volume uploaded by the target floor scrubbing device is less than the target floor scrubbing water storage volume.

[0039] In a third aspect, the present application provides an electronic device, including:

[0040] processor; and,

[0041] A memory, configured to store executable instructions of the processor;

[0042] The processor is configured to perform any possible method described in the first aspect by executing the executable instructions.

[0043] In a fourth aspect, the present application provides a computer-readable storage medium, wherein the computer-readable storage medium stores computer-executable instructions, and when the computer-executable instructions are executed by a processor, they are used to implement any possible method described in the first aspect.

[0044] The present application provides a method and device for processing floor washing data for industrial dust-contaminated ground. The dust monitoring data set of the industrial area to be cleaned is obtained through a dust monitoring sensor array, and the dust monitoring data set is sent to a scheduling cloud platform, so that the scheduling cloud platform determines the target floor washing water storage capacity according to the target planning path of the target floor washing device in the floor washing device cluster and the dust monitoring data set, and sends the target floor washing water storage capacity to the target floor washing device. When the target floor washing device determines that the current remaining water storage capacity uploaded by the target floor washing device is less than the target floor washing water storage capacity, a water adding instruction is generated to indicate that the target floor washing device is to be operated to add water. Thus, according to the dust monitoring data set and the target planning path of the target floor washing device, the target floor washing water storage capacity is intelligently determined to ensure that the floor washing device has sufficient water storage capacity when performing the task, avoiding the problem of incomplete cleaning due to insufficient water storage. In addition, when the target floor washing device finds that the current remaining water storage capacity is insufficient, a water adding instruction is automatically generated, thereby improving the automation and response speed of the floor washing operation, reducing manual intervention and downtime waiting time, and further improving the overall floor washing efficiency for industrial dust-contaminated ground. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present application and, together with the description, serve to explain the principles of the present application.

[0046] Figure 1 It is a flowchart of a method for processing floor washing data for industrial dust-contaminated floors according to an exemplary embodiment of the present application;

[0047] Figure 2 is a flowchart of a method for processing floor washing data for industrial dust-contaminated floors according to another exemplary embodiment of the present application;

[0048] Figure 3 is a structural schematic diagram of a floor-cleaning data processing device for industrial dust-contaminated floors according to an exemplary embodiment of the present application;

[0049] Figure 4 It is a schematic diagram of the structure of an electronic device according to an exemplary embodiment of the present application.

[0050] The above drawings have shown clear embodiments of the present application, which will be described in more detail later. These drawings and text descriptions are not intended to limit the scope of the present application in any way, but to illustrate the concept of the present application to those skilled in the art by referring to specific embodiments. DETAILED DESCRIPTION

[0051] Exemplary embodiments will be described in detail herein, examples of which are shown in the accompanying drawings. When the following description refers to the drawings, the same numbers in different drawings represent the same or similar elements unless otherwise indicated. The implementations described in the following exemplary embodiments do not represent all implementations consistent with the present application. Instead, they are merely examples of devices and methods consistent with some aspects of the present application as detailed in the appended claims.

[0052] In the industrial production process, dust pollution is a common problem, posing a threat to the environment and human health. The traditional method of floor washing is often to directly fill the water storage capacity of the floor washing robot and then work, which easily causes the floor washing robot to load too much water to work and cause unnecessary energy loss. Or, during the floor washing process, the water is filled up after it is consumed, which causes the floor washing robot to leave the floor washing working state to add water during the floor washing process, thereby affecting the floor washing efficiency. In order to solve the above problems, the embodiments provided in this application provide accurate floor washing water storage planning for the floor washing device by real-time monitoring of dust pollution conditions and combining intelligent calculations of the scheduling cloud platform, thereby achieving efficient cleaning of industrial dust-contaminated floors. Specifically include the following aspects:

[0053] Dust monitoring and data transmission: A dust monitoring sensor array is arranged in the industrial area to be cleaned, and at least one dust monitoring sensor is arranged in each grid to be cleaned. The dust monitoring sensor obtains dust particle concentration and size characteristic data in real time and transmits this data to the scheduling cloud platform.

[0054] Intelligent calculation of floor washing water demand: The scheduling cloud platform uses a preset formula to accurately calculate the floor washing water demand of each grid to be cleaned based on the received dust monitoring data. This step ensures a refined analysis of floor washing demand and provides a scientific basis for subsequent floor washing planning.

[0055] Global feature evaluation and determination of floor washing water storage volume: The scheduling cloud platform further calculates the characteristic value of dust particle concentration deviation and dust particle size deviation in the entire area to be cleaned, and evaluates the distribution and severity of dust pollution from a global perspective. Combined with the target planning path and dust monitoring data of the target floor washing device, the target floor washing water storage volume is intelligently determined to ensure that the floor washing device has sufficient and reasonable water storage when performing tasks.

[0056] Dynamic path planning and optimization: The scheduling cloud platform accurately identifies the pollution source grid based on the dust monitoring data and determines it as the starting point of the floor washing planning path. The grid with the second highest dust concentration among the adjacent grids is selected as the next grid to be cleaned, and the floor washing path is gradually expanded until the entire area to be cleaned is covered. In this way, the floor washing device can be cleaned in order from high to low pollution level, which improves the efficiency and pertinence of the floor washing operation.

[0057] Water adding indication and automatic operation: When the floor scrubber finds that the remaining water storage is insufficient, it will automatically generate a water adding indication to remind the operator to add water in time. This design improves the automation and response speed of floor scrubbing operations and reduces manual intervention and downtime waiting time.

[0058] Figure 1 FIG. 1 is a flow chart of a method for processing floor cleaning data for industrial dust-contaminated floors according to an exemplary embodiment of the present application. Figure 1 As shown, the method provided in this embodiment includes:

[0059] S101. Obtain a dust monitoring data set of the industrial area to be cleaned through a dust monitoring sensor array, and send the dust monitoring data set to a scheduling cloud platform.

[0060] The floor washing data processing method for industrial dust-contaminated ground provided in this embodiment can be applied to a floor washing device scheduling system, which includes a floor washing device cluster, a scheduling cloud platform, and a dust monitoring sensor array. Each floor washing device in the floor washing device cluster and each dust monitoring sensor in the dust monitoring sensor array are respectively communicated with the scheduling cloud platform.

[0061] In this step, a dust monitoring data set of the industrial area to be cleaned can be obtained through the dust monitoring sensor array, and the dust monitoring data set is sent to the scheduling cloud platform. The industrial area to be cleaned includes a grid array to be cleaned, and each grid to be cleaned in the grid array is arranged with at least one dust monitoring sensor in the dust monitoring sensor array. The dust monitoring data corresponding to each grid to be cleaned in the grid array in the dust monitoring data set includes dust particle concentration characteristic data and dust particle size characteristic data.

[0062] Specifically, in the industrial area to be cleaned, the entire area is divided into several grids to be cleaned, and at least one dust monitoring sensor is arranged in each grid. The dust monitoring sensor monitors the concentration and size characteristic data of dust particles in the grid in real time. Then, the dust monitoring sensor sends the monitored dust data set to the scheduling cloud platform through a communication connection. The data set contains the dust particle concentration characteristic data and dust particle size characteristic data of each grid to be cleaned.

[0063] S102: The scheduling cloud platform determines a target floor washing water storage volume according to a target planning path of a target floor washing device in the floor washing device cluster and a dust monitoring data set, and sends the target floor washing water storage volume to the target floor washing device.

[0064] In this step, the scheduling cloud platform determines the target floor washing water storage capacity based on the target planning path of the target floor washing device in the floor washing device cluster and the dust monitoring data set, and sends the target floor washing water storage capacity to the target floor washing device, wherein the target planning path is a planned path for performing floor washing operations on some of the grids to be cleaned in the grid array to be cleaned.

[0065] Specifically, after receiving the dust monitoring data, the scheduling cloud platform calculates the required water volume for floor washing corresponding to each grid to be cleaned. This calculation can take into account multiple factors such as grid area, time interval, dust particle size, dust particle concentration, dust deposition rate factor, and the required water volume for floor washing per unit volume of dust. The scheduling cloud platform further calculates the characteristic value of dust particle concentration deviation and the characteristic value of dust particle size deviation in the entire area to be cleaned based on the dust monitoring data of all grids. These characteristic values ​​reflect the overall distribution and severity of dust pollution in the area. Then, combined with the target planning path of the target floor washing device (i.e., the planned path for floor washing operations on some grids to be cleaned) and the dust monitoring data set, the scheduling cloud platform uses an intelligent algorithm to determine the target floor washing water storage capacity.

[0066] S103: The target floor scrubbing device generates a water adding instruction.

[0067] If the target scrubbing device determines that the current remaining water storage volume uploaded by the target scrubbing device is less than the target scrubbing water storage volume, the target scrubbing device generates a water adding instruction to instruct to perform a water adding operation on the target scrubbing device.

[0068] Specifically, before executing the task, the target scrubbing device will check whether its current remaining water storage capacity meets the target scrubbing water storage capacity requirements sent by the scheduling cloud platform. If the current remaining water storage capacity is less than the target scrubbing water storage capacity, the target scrubbing device will automatically generate a water addition instruction and notify the operator through the scheduling cloud platform to perform the water addition operation in a timely manner. This process improves the automation and response speed of the scrubbing operation and reduces manual intervention and downtime waiting time.

[0069] In this embodiment, a dust monitoring data set of the industrial area to be cleaned is obtained through a dust monitoring sensor array, and the dust monitoring data set is sent to a scheduling cloud platform, so that the scheduling cloud platform determines the target floor washing water storage capacity according to the target planning path of the target floor washing device in the floor washing device cluster and the dust monitoring data set, and sends the target floor washing water storage capacity to the target floor washing device, and when the target floor washing device determines that the current remaining water storage capacity uploaded by the target floor washing device is less than the target floor washing water storage capacity, a water adding instruction is generated to instruct the target floor washing device to add water. Thus, according to the dust monitoring data set and the target planning path of the target floor washing device, the target floor washing water storage capacity is intelligently determined to ensure that the floor washing device has sufficient water storage capacity when performing tasks, avoiding the problem of incomplete cleaning due to insufficient water storage. In addition, when the target floor washing device finds that the current remaining water storage capacity is insufficient, a water adding instruction is automatically generated, thereby improving the automation and response speed of the floor washing operation, reducing manual intervention and downtime waiting time, and further improving the overall floor washing efficiency for industrial dust-contaminated ground.

[0070] Figure 2 FIG. 1 is a flow chart of a method for processing floor cleaning data for industrial dust-contaminated floors according to another exemplary embodiment of the present application. Figure 2 As shown, the method for processing floor washing data of industrial dust-contaminated floors provided in this embodiment includes:

[0071] S201. Obtain a dust monitoring data set of the industrial area to be cleaned through a dust monitoring sensor array, and send the dust monitoring data set to a scheduling cloud platform.

[0072] In this step, a dust monitoring data set of the industrial area to be cleaned can be obtained through the dust monitoring sensor array, and the dust monitoring data set is sent to the scheduling cloud platform. The industrial area to be cleaned includes a grid array to be cleaned, and each grid to be cleaned in the grid array is arranged with at least one dust monitoring sensor in the dust monitoring sensor array. The dust monitoring data corresponding to each grid to be cleaned in the grid array in the dust monitoring data set includes dust particle concentration characteristic data and dust particle size characteristic data.

[0073] Specifically, in the industrial area to be cleaned, the entire area is divided into several grids to be cleaned, and at least one dust monitoring sensor is arranged in each grid. The dust monitoring sensor monitors the concentration and size characteristic data of dust particles in the grid in real time. Then, the dust monitoring sensor sends the monitored dust data set to the scheduling cloud platform through a communication connection. The data set contains the dust particle concentration characteristic data and dust particle size characteristic data of each grid to be cleaned.

[0074] S202: The scheduling cloud platform determines the floor washing water demand corresponding to each grid to be cleaned in the grid array according to the dust monitoring data corresponding to each grid to be cleaned in the dust monitoring data set.

[0075] In this step, the dust monitoring data set of the industrial area to be cleaned is obtained in real time through the dust monitoring sensor array. These data include the characteristic data of dust particle concentration and dust particle size in each grid to be cleaned. Subsequently, these monitoring data are sent to the scheduling cloud platform in a timely manner to provide a basis for subsequent data processing and analysis. After receiving the dust monitoring data, the scheduling cloud platform will calculate the required water volume for floor washing of each grid to be cleaned based on these data. The calculated required water volume for floor washing of each grid to be cleaned will be stored in the database of the scheduling cloud platform and updated in real time. In this way, when determining the target floor washing water storage volume and planning the cleaning path in the future, the scheduling cloud platform can directly call these data to ensure the accuracy and timeliness of the calculation. The calculated required water volume for floor washing can not only be used for the subsequent determination of the target floor washing water storage volume, but also provide data support for other functional modules of the scheduling cloud platform, such as the optimal configuration of cleaning resources, the scheduling arrangement of cleaning equipment, etc. Through the comprehensive utilization of these data, the scheduling cloud platform can realize the comprehensive management and intelligent scheduling of floor washing operations.

[0076] In a possible implementation, the scheduling cloud platform uses formula 1 and determines the floor washing water demand corresponding to each to-be-cleaned grid in the to-be-cleaned grid array according to the dust monitoring data corresponding to each to-be-cleaned grid in the dust monitoring data set. , where Formula 1 is:

[0077]

[0078] in, The first The ground area of ​​the grid to be cleaned, For the The time interval between the last cleaning completion of the grid to be cleaned and the current time. For the The dust particle size in the dust monitoring data corresponding to the grid to be cleaned, For the The dust particle concentration in the dust monitoring data corresponding to the grid to be cleaned, For the The dust deposition rate factor pre-calibrated in the grid to be cleaned, It is the pre-calibrated amount of water required for floor washing per unit volume of dust.

[0079] It is worth noting that Formula 1 comprehensively considers multiple key factors of the grid to be cleaned, including the ground area, time interval, dust particle size, dust particle concentration, and the product of the dust deposition rate factor and the required water volume for floor washing per unit volume of dust. These factors jointly determine the specific floor washing water demand of each grid, thereby realizing the accurate calculation of the required water volume. Through the floor washing water demand calculated by Formula 1, the scheduling cloud platform can plan the floor washing operation more scientifically. On the one hand, it can reasonably allocate floor washing devices and water resources according to the actual needs of each grid to avoid waste or shortage of resources; on the other hand, it further improves the automation and intelligence level of floor washing operations, so that the floor washing device scheduling system can automatically calculate the floor washing water demand based on real-time dust monitoring data, reducing the uncertainty caused by manual intervention and subjective judgment, and improving the response speed and accuracy of the system. In addition, since each parameter in Formula 1 can be adjusted and optimized according to actual conditions, it has strong adaptability and flexibility, and can be customized according to the specific pollution conditions of the industrial area to be cleaned and the performance characteristics of the floor washing device, so as to ensure the efficient and smooth progress of the floor washing operation.

[0080] S203: The scheduling cloud platform determines the dust particle concentration deviation characteristic value of the industrial area to be cleaned according to the dust monitoring data corresponding to each grid to be cleaned in the grid array to be cleaned in the dust monitoring data set.

[0081] In a possible implementation, the scheduling cloud platform uses Formula 2 and determines the dust particle concentration deviation characteristic value of the industrial area to be cleaned according to the dust monitoring data corresponding to each to-be-cleaned grid in the to-be-cleaned grid array in the dust monitoring data set. Formula 2 is:

[0082]

[0083] in, is the characteristic value of dust particle concentration deviation, is the number of grids to be cleaned in the grid array to be cleaned.

[0084] It is worth noting that by calculating the characteristic value of the dust particle concentration deviation in the entire industrial area to be cleaned, a global perspective of pollution assessment can be provided. This indicator not only reflects the average level of dust concentration in the area, but also reveals the degree of discreteness of the concentration distribution, which helps the scheduling cloud platform to grasp the pollution status of the entire area from a macro perspective. When formulating floor washing strategies, the scheduling cloud platform can make more scientific decisions based on the characteristic value of the dust particle concentration deviation. For example, when the deviation characteristic value is large, it may indicate that there are local high-concentration pollution points in the area, which need to be focused on and more resources allocated for cleaning. Then, combined with the target planning path of the target floor washing device, the scheduling cloud platform can use the characteristic value of the dust particle concentration deviation to optimize the floor washing path and water storage allocation. For example, for areas with large concentration deviations, the floor washing water storage capacity and the number of cleaning times can be appropriately increased to ensure the cleaning effect.

[0085] S204. The scheduling cloud platform determines the dust particle size deviation characteristic value of the industrial area to be cleaned according to the dust monitoring data corresponding to each grid to be cleaned in the grid array to be cleaned in the dust monitoring data set.

[0086] In a possible implementation, the scheduling cloud platform uses Formula 3 and determines the dust particle size deviation characteristic value of the industrial area to be cleaned according to the dust monitoring data corresponding to each to-be-cleaned grid in the to-be-cleaned grid array in the dust monitoring data set. Formula 3 is:

[0087]

[0088] in, is the characteristic value of dust particle size deviation.

[0089] It is worth noting that the characteristic value of the dust particle size deviation calculated by Formula 3 provides another dimension of pollution analysis. It reveals the distribution of dust particle sizes in the area, which helps the scheduling cloud platform to have a more comprehensive understanding of the pollution characteristics. Dust particles of different sizes may have different requirements for cleaning effects. By calculating the characteristic value of the dust particle size deviation, the scheduling cloud platform can formulate more targeted cleaning strategies. For example, for areas with more large-sized dust particles, a stronger flushing force or more cleaning times may be required. Combining the characteristic value of the dust particle size deviation and the performance characteristics of the target floor scrubber, the scheduling cloud platform can further optimize resource allocation. For example, different types or specifications of floor scrubbers are allocated according to cleaning needs to improve cleaning efficiency and effectiveness.

[0090] S205. The scheduling cloud platform determines the target floor washing water storage capacity according to the dust particle concentration deviation characteristic value, the dust particle size deviation characteristic value, the target planning path and the dust monitoring data set.

[0091] In this step, the scheduling cloud platform determines the target to-be-cleaned grid sequence from the to-be-cleaned grid array according to the target planning path;

[0092] The scheduling cloud platform determines the required water compensation coefficient based on the dust particle concentration deviation characteristic value and the dust particle size deviation characteristic value;

[0093] The scheduling cloud platform determines the target floor washing water storage capacity according to the floor washing water demand corresponding to each grid to be cleaned in the grid to be cleaned sequence and the grid to be cleaned array and the water demand compensation coefficient.

[0094] In one case, the scheduling cloud platform determines the target to-be-cleaned grid sequence from the to-be-cleaned grid array according to the target planning path. ;

[0095] If the dust particle concentration deviation characteristic value is less than the preset dust particle concentration deviation threshold, and the dust particle size deviation characteristic value is less than the preset dust particle size deviation threshold, the scheduling cloud platform uses formula 4 and according to the sequence of grids to be cleaned The target floor washing water storage volume is determined by the floor washing water demand corresponding to each grid to be cleaned in the grid array to be cleaned. , Formula 4 is:

[0096]

[0097] in, The first grid in the sequence to be cleaned The ground area of ​​the grid to be cleaned, The first grid in the sequence to be cleaned The amount of water required for washing the floor corresponding to the grid to be cleaned, To preset the cleaning area, It is the preset demand water compensation coefficient corresponding to the preset calibrated cleaning area.

[0098] It is worth noting that Formula 4 is mainly used in the industrial dust-contaminated floor washing data processing method of the present invention to calculate the target floor washing water storage under specific conditions (i.e., when the dust particle concentration deviation characteristic value and the dust particle size deviation characteristic value are both less than the preset threshold value). Specifically, when the dust pollution level of the entire industrial area to be cleaned is relatively uniform, that is, when the concentration and size deviation characteristic values ​​are both small, the use of Formula 4 can simplify the calculation process and improve the calculation efficiency. By directly calculating based on the grid sequence to be cleaned and its corresponding floor washing demand water volume, more complex deviation adjustment steps are avoided. In addition, in the case of uniform pollution distribution, Formula 4 can more accurately calculate the target floor washing water storage volume by accumulating the floor washing demand water volume of each grid in the target grid sequence to be cleaned, and considering the preset calibrated cleaning area and the corresponding demand water volume compensation coefficient. This calculation method fully considers the actual needs of each grid and ensures the reasonable allocation of water storage volume. In addition, with the target floor washing water storage volume calculated by Formula 4, the scheduling cloud platform can more accurately arrange the water storage volume of the floor washing device to avoid waste or shortage of water resources. This helps optimize resource allocation and improve the economy and sustainability of floor scrubbing operations. Based on accurate floor scrubbing water storage arrangements, floor scrubbing devices can carry the right amount of water when performing tasks, which not only ensures the cleaning effect, but also avoids increased energy consumption and reduced operating efficiency caused by excessive water storage. This efficient resource allocation method helps improve overall floor scrubbing efficiency.

[0099] In another case, if the dust particle concentration deviation characteristic value is greater than or equal to the preset dust particle concentration deviation threshold, or the dust particle size deviation characteristic value is greater than or equal to the preset dust particle size deviation threshold, the scheduling cloud platform uses formula 5 and according to the sequence of grids to be cleaned The target floor washing water storage volume is determined by the floor washing water demand corresponding to each grid to be cleaned in the grid array to be cleaned. , Formula 5 is:

[0100]

[0101] in, The first grid in the sequence to be cleaned The ground area of ​​the grid to be cleaned, The first grid in the sequence to be cleaned The amount of water required for washing the floor corresponding to the grid to be cleaned, The first grid in the sequence to be cleaned The preset calibrated cleaning area corresponding to the grid to be cleaned, The first grid in the sequence to be cleaned The water demand compensation coefficient of the preset calibrated cleaning area corresponding to the grid to be cleaned.

[0102] It is worth noting that Formula 5 is mainly used to calculate the target floor washing water storage when the dust pollution distribution is uneven, that is, the dust particle concentration deviation characteristic value or the dust particle size deviation characteristic value is greater than or equal to the preset threshold. When the pollution distribution in the industrial area to be cleaned is uneven, Formula 5 can more accurately reflect the actual floor washing demand of each grid by introducing the demand water compensation coefficient, and adjust the target floor washing water storage accordingly. This differentiated treatment method enables floor washing operations to more effectively cope with complex and changeable pollution conditions. For areas with more serious pollution, Formula 5 increases the floor washing water storage to ensure that these areas can be fully cleaned, thereby improving the overall cleaning effect. This targeted adjustment helps to reduce residual pollution after cleaning and protect the environment and equipment. When calculating the target floor washing water storage, Formula 5 not only takes into account the actual needs of each grid, but also takes into account the overall pollution characteristics and floor washing planning. This enables the scheduling cloud platform to allocate water resources more reasonably, avoid wasting resources in areas with less pollution, and provide sufficient support in areas with serious pollution. By accurately calculating the target floor washing water storage volume, the floor washing device can carry an appropriate amount of water when performing tasks, reducing the number of shutdowns and water additions caused by insufficient water storage, thereby improving the overall efficiency of the floor washing operation. At the same time, focused cleaning of severely polluted areas also helps to shorten the overall cleaning cycle. The application of formula 5 reflects the intelligence of the floor washing device scheduling system. The system can automatically adjust the calculation method of the floor washing water storage volume according to real-time dust monitoring data to cope with different pollution conditions. In addition, combined with the above formula 4, the above steps can automatically adjust the calculation method according to the pollution condition of the area to be cleaned, and use a simplified calculation formula when the pollution is evenly distributed, which reflects the system's flexible response to different pollution conditions and enhances the adaptability and robustness of the system.

[0103] In addition, the scheduling cloud platform uses Formula 5 and according to the sequence of grids to be cleaned The target floor washing water storage volume is determined by the floor washing water demand corresponding to each grid to be cleaned in the grid array to be cleaned. Previously, it also included:

[0104] The scheduling cloud platform uses formula 6 and according to the sequence of grids to be cleaned Determine the first The water demand compensation coefficient of the preset calibrated cleaning area corresponding to the grid to be cleaned , Formula 6 is:

[0105]

[0106] in, is the preset dust particle concentration compensation coefficient, is the preset dust particle size compensation coefficient, is the calibrated dust particle concentration corresponding to the preset dust particle concentration compensation coefficient, It is the calibrated dust particle size corresponding to the preset dust particle size compensation coefficient.

[0107] It is worth noting that Formula 6 generates a refined demand water compensation coefficient for each grid by comprehensively considering the preset dust particle concentration compensation coefficient, the preset dust particle size compensation coefficient, and the corresponding calibrated dust particle concentration and calibrated dust particle size. This compensation coefficient can more accurately reflect the changes in the demand water volume of floor washing caused by differences in pollution levels in different grids, and realize the refined adjustment of the floor washing water storage volume. The introduction of Formula 6 enables the scheduling cloud platform to dynamically adjust the floor washing water demand of each grid according to the specific dust monitoring data, enhancing the adaptability and flexibility of the system to different pollution conditions. Whether facing slight pollution or severe pollution, the system can calculate a reasonable compensation coefficient to ensure the reasonable allocation of floor washing water storage volume. In addition, through the demand water compensation coefficient calculated by Formula 6, the scheduling cloud platform can more scientifically plan the floor washing path and water storage volume allocation. For severely polluted grids, appropriately increasing the floor washing water storage volume can ensure the cleaning effect; while for grids with less pollution, the water storage volume can be appropriately reduced to save resources. This differentiated treatment method helps to improve cleaning efficiency and effect. The above dynamic compensation mechanism based on actual pollution conditions avoids the "one-size-fits-all" water storage allocation method in traditional floor washing operations, reducing unnecessary waste of resources and energy consumption. The scheduling cloud platform can accurately calculate the floor washing water storage capacity of each grid according to actual needs, ensuring that while achieving the cleaning effect, the waste of resources and energy consumption is minimized. It can be seen that the application of Formula 6 further improves the intelligence level of the floor washing device scheduling system. By automatically calculating and allocating floor washing water storage capacity, the system can reduce dependence on manual intervention and improve response speed and decision-making accuracy. At the same time, intelligent resource allocation methods also provide strong support for future automation and intelligent upgrades.

[0108] S206: The target floor scrubbing device generates a water adding instruction.

[0109] If the target scrubbing device determines that the current remaining water storage volume uploaded by the target scrubbing device is less than the target scrubbing water storage volume, the target scrubbing device generates a water adding instruction to instruct to perform a water adding operation on the target scrubbing device.

[0110] Specifically, before executing the task, the target scrubbing device will check whether its current remaining water storage capacity meets the target scrubbing water storage capacity requirements sent by the scheduling cloud platform. If the current remaining water storage capacity is less than the target scrubbing water storage capacity, the target scrubbing device will automatically generate a water addition instruction and notify the operator through the scheduling cloud platform to perform the water addition operation in a timely manner. This process improves the automation and response speed of the scrubbing operation and reduces manual intervention and downtime waiting time.

[0111] On the basis of the above scheme, after obtaining the dust monitoring data set of the industrial area to be cleaned through the dust monitoring sensor array and sending the dust monitoring data set to the scheduling cloud platform, it also includes:

[0112] The scheduling cloud platform determines a target grid to be cleaned from the grid array to be cleaned according to the dust monitoring data set, and determines the target grid to be cleaned as the starting grid of the target planning path, wherein the target grid to be cleaned is a dust pollution source grid in the grid array to be cleaned.

[0113] The scheduling cloud platform determines the target grid to be cleaned from the array of grids to be cleaned according to the dust monitoring data set, which may include:

[0114] The scheduling cloud platform obtains first dust particle concentration characteristic data of a first grid to be cleaned in the grid array to be cleaned;

[0115] The scheduling cloud platform determines a set of adjacent grids to be cleaned from the array of grids to be cleaned according to the first grid to be cleaned, wherein the grids to be cleaned in the set of adjacent grids to be cleaned are adjacent grids to the first grid to be cleaned;

[0116] If the scheduling cloud platform determines that the first dust particle concentration characteristic data is greater than the dust particle concentration characteristic data of any grid to be cleaned in the set of adjacent grids to be cleaned, the first grid to be cleaned is determined to be the target grid to be cleaned.

[0117] Furthermore, after determining the target to-be-cleaned grid as the starting grid of the target planning path, the method may further include:

[0118] The scheduling cloud platform determines the grid to be cleaned with the highest dust particle concentration characteristic data in the set of adjacent grids to be cleaned as the second grid to be cleaned, and determines the second grid to be cleaned as the next grid to be cleaned of the target grid to be cleaned on the target planning path;

[0119] The scheduling cloud platform takes the target grid to be cleaned as the center, the second grid to be cleaned as the starting point, and a first direction (for example, a clockwise direction or a counterclockwise direction) around the target grid to be cleaned as the planning direction, and sequentially sorts the set of adjacent grids to be cleaned to generate the target planning path.

[0120] Specifically, in the floor scrubber scheduling system, the scheduling cloud platform first obtains the dust monitoring data set of the industrial area to be cleaned in real time through the dust monitoring sensor array. These data include the dust particle concentration characteristic data and dust particle size characteristic data in each grid to be cleaned. After receiving the data, the scheduling cloud platform first performs data preprocessing to ensure the accuracy and completeness of the data. Then, by comparing the dust particle concentration characteristic data of each grid and its adjacent grids, the grid with the highest dust concentration, that is, the dust pollution source grid, is quickly located. Once the dust pollution source grid is identified, the scheduling cloud platform immediately determines it as the starting grid of the target planning path. The advantage of this is that the floor scrubber can clean the most polluted areas from the beginning, thereby significantly improving the efficiency of the floor scrubbing operation.

[0121] Optionally, the scheduling cloud platform may first obtain the first dust particle concentration characteristic data of the first grid to be cleaned (which may be an arbitrarily selected grid) in the grid array to be cleaned. Based on the spatial position relationship, the scheduling cloud platform determines all adjacent grids of the grid to form a set of adjacent grids to be cleaned. The scheduling cloud platform compares the dust particle concentration characteristic data of the first grid with each grid in the set of adjacent grids one by one. If the dust concentration of the first grid is higher than that of all adjacent grids, the grid is determined to be a dust pollution source grid, that is, the target grid to be cleaned. If the first comparison fails to determine the pollution source grid, the scheduling cloud platform may select the grid with the highest concentration among the adjacent grids as a new starting point and repeat the above steps until the pollution source grid is found.

[0122] After determining the target grid to be cleaned (the pollution source grid) as the starting grid, the scheduling cloud platform needs to further plan the target planning path of the floor scrubber. In the set of adjacent grids to be cleaned, the scheduling cloud platform selects the grid with the second highest concentration as the second grid to be cleaned according to the order of the dust particle concentration characteristic data, and determines it as the next grid to be cleaned of the target grid to be cleaned on the target planning path. With the target grid to be cleaned (the pollution source grid) as the center and the second grid to be cleaned as the starting point, the scheduling cloud platform sorts the set of adjacent grids to be cleaned in an orderly manner according to the first direction (such as clockwise or counterclockwise) around the center grid. This step ensures that the floor scrubber can cover all adjacent grids according to a certain rule to avoid omissions and repeated cleaning. Through the above sorting process, the scheduling cloud platform generates a continuous and non-omitted floor scrubbing planning path. This path starts with the pollution source grid and gradually expands outward to ensure that the floor scrubber can perform cleaning operations in the order of high to low pollution levels, thereby improving the efficiency and effect of floor scrubbing.

[0123] Figure 3 FIG. 1 is a schematic diagram of a floor cleaning data processing device for industrial dust-contaminated floors according to an exemplary embodiment of the present application. Figure 3As shown, the floor cleaning data processing device 300 for industrial dust-contaminated floors provided in this embodiment includes:

[0124] An acquisition module 310 is used to acquire a dust monitoring data set of an industrial area to be cleaned, wherein the industrial area to be cleaned includes a grid array to be cleaned, each grid to be cleaned in the grid array to be cleaned is arranged with at least one dust monitoring sensor in the dust monitoring sensor array, and the dust monitoring data corresponding to each grid to be cleaned in the grid array to be cleaned in the dust monitoring data set includes dust particle concentration characteristic data and dust particle size characteristic data;

[0125] A processing module 320 is used to determine a target floor-washing water storage capacity according to a target planning path of a target floor-washing device and the dust monitoring data set, wherein the target planning path is a planning path for performing floor-washing operations on some of the grids to be cleaned in the grid array to be cleaned;

[0126] The indication module 330 is used to generate a water adding indication to instruct a water adding operation when it is determined that the current remaining water storage amount uploaded by the target scrubbing device is less than the target scrubbing water storage amount.

[0127] Optionally, the processing module 320 is further configured to determine the required floor washing water volume corresponding to each grid to be cleaned in the grid array according to the dust monitoring data corresponding to each grid to be cleaned in the dust monitoring data set.

[0128] Optionally, the processing module 320 is specifically configured to:

[0129] Determine the dust particle concentration deviation characteristic value of the industrial area to be cleaned according to the dust monitoring data corresponding to each to-be-cleaned grid in the to-be-cleaned grid array in the dust monitoring data set;

[0130] Determine the dust particle size deviation characteristic value of the industrial area to be cleaned according to the dust monitoring data corresponding to each to-be-cleaned grid in the to-be-cleaned grid array in the dust monitoring data set;

[0131] The target floor scrubbing water storage capacity is determined according to the dust particle concentration deviation characteristic value, the dust particle size deviation characteristic value, the target planning path, and the dust monitoring data set.

[0132] Optionally, the processing module 320 is specifically configured to:

[0133] Determining a target sequence of grids to be cleaned from the array of grids to be cleaned according to the target planning path;

[0134] Determine a required water volume compensation coefficient according to the dust particle concentration deviation characteristic value and the dust particle size deviation characteristic value;

[0135] The target floor-washing water storage volume is determined according to the to-be-washed grid sequence and the floor-washing water demand corresponding to each to-be-washed grid in the to-be-washed grid array and the required water compensation coefficient.

[0136] Optionally, the processing module 320 is also used to determine a target grid to be cleaned from the grid array to be cleaned according to the dust monitoring data set, and determine the target grid to be cleaned as the starting grid of the target planning path, wherein the target grid to be cleaned is a dust pollution source grid in the grid array to be cleaned.

[0137] Optionally, the processing module 320 is specifically configured to:

[0138] Acquire first dust particle concentration characteristic data of a first grid to be cleaned in the grid array to be cleaned;

[0139] Determine a set of adjacent grids to be cleaned from the array of grids to be cleaned according to the first grid to be cleaned, wherein the grids to be cleaned in the set of adjacent grids to be cleaned are adjacent grids to the first grid to be cleaned;

[0140] If it is determined that the first dust particle concentration characteristic data is greater than the dust particle concentration characteristic data of any grid to be cleaned in the set of adjacent grids to be cleaned, the first grid to be cleaned is determined to be the target grid to be cleaned.

[0141] Optionally, the processing module 320 is specifically configured to:

[0142] Determine the grid to be cleaned with the highest dust particle concentration characteristic data in the set of adjacent grids to be cleaned as the second grid to be cleaned, and determine the second grid to be cleaned as the next grid to be cleaned of the target grid to be cleaned on the target planning path;

[0143] Taking the target to-be-cleaned grid as the center, the second to-be-cleaned grid as the starting point, and the first direction around the target to-be-cleaned grid as the planning direction, the adjacent to-be-cleaned grid sets are sequentially sorted to generate the target planning path.

[0144] Figure 4 is a schematic diagram of the structure of an electronic device according to an exemplary embodiment of the present application. Figure 4 As shown, an electronic device 400 provided in this embodiment includes: a processor 401 and a memory 402; wherein:

[0145] The memory 402 is used to store computer programs, and the memory may also be a flash memory.

[0146] The processor 401 is used to execute the execution instructions stored in the memory to implement each step in the above method. For details, please refer to the relevant description in the above method embodiment.

[0147] Optionally, the memory 402 may be independent or integrated with the processor 401 .

[0148] When the memory 402 is a device independent of the processor 401, the electronic device 400 may further include:

[0149] The bus 403 is used to connect the memory 402 and the processor 401 .

[0150] This embodiment further provides a readable storage medium, in which a computer program is stored. When at least one processor of an electronic device executes the computer program, the electronic device executes the methods provided in the above-mentioned various implementation modes.

[0151] This embodiment also provides a program product, which includes a computer program stored in a readable storage medium. At least one processor of the electronic device can read the computer program from the readable storage medium, and at least one processor executes the computer program so that the electronic device implements the methods provided in the above various embodiments.

[0152] Those skilled in the art will readily appreciate other embodiments of the present application after considering the specification and practicing the invention disclosed herein. The present application is intended to cover any modification, use or adaptation of the present application, which follows the general principles of the present application and includes common knowledge or customary techniques in the art that are not disclosed in the present application. The specification and examples are intended to be exemplary only, and the true scope and spirit of the present application are indicated by the claims.

[0153] It should be understood that the present application is not limited to the precise structures that have been described above and shown in the drawings, and that various modifications and changes may be made without departing from the scope thereof. The scope of the present application is limited only by the appended claims.

Claims

1. A method for processing floor cleaning data of industrial dust-contaminated ground, characterized in that: The method is applied to a floor scrubber scheduling system, wherein the floor scrubber scheduling system comprises a floor scrubber cluster, a scheduling cloud platform, and a dust monitoring sensor array, wherein each floor scrubber in the floor scrubber cluster and each dust monitoring sensor in the dust monitoring sensor array are respectively connected to the scheduling cloud platform for communication; the method comprises: Acquire a dust monitoring data set of the industrial area to be cleaned through the dust monitoring sensor array, and send the dust monitoring data set to the scheduling cloud platform, the industrial area to be cleaned includes a grid array to be cleaned, each grid to be cleaned in the grid array to be cleaned is arranged with at least one dust monitoring sensor in the dust monitoring sensor array, and the dust monitoring data corresponding to each grid to be cleaned in the grid array to be cleaned in the dust monitoring data set includes dust particle concentration characteristic data and dust particle size characteristic data; The scheduling cloud platform determines a target floor-washing water storage capacity according to a target planning path of a target floor-washing device in the floor-washing device cluster and the dust monitoring data set, and sends the target floor-washing water storage capacity to the target floor-washing device, wherein the target planning path is a planning path for performing floor-washing operations on some of the grids to be cleaned in the grid array to be cleaned; If the target floor scrubber determines that the current remaining water storage volume uploaded by the target floor scrubber is less than the target floor scrubber water storage volume, the target floor scrubber generates a water adding instruction to instruct to perform a water adding operation on the target floor scrubber; Before the scheduling cloud platform determines the target floor scrubbing water storage capacity according to the target planning path of the target floor scrubbing device in the floor scrubbing device cluster and the dust monitoring data set, the method further includes: The scheduling cloud platform uses formula 1 and determines the floor washing water demand corresponding to each to-be-cleaned grid in the to-be-cleaned grid array according to the dust monitoring data corresponding to each to-be-cleaned grid in the dust monitoring data set. , wherein the formula 1 is: in, is the first The ground area of ​​the grid to be cleaned, For the The time interval between the last cleaning completion of the grid to be cleaned and the current time. For the The dust particle size in the dust monitoring data corresponding to the grid to be cleaned, For the The dust particle concentration in the dust monitoring data corresponding to the grid to be cleaned, For the The dust deposition rate factor pre-calibrated in the grid to be cleaned, The pre-calibrated amount of water required for floor washing per unit volume of dust; The scheduling cloud platform determines the target floor-washing water storage capacity according to the target planning path of the target floor-washing device in the floor-washing device cluster and the dust monitoring data set, including: The scheduling cloud platform uses Formula 2 and determines the dust particle concentration deviation characteristic value of the industrial area to be cleaned according to the dust monitoring data corresponding to each to-be-cleaned grid in the to-be-cleaned grid array in the dust monitoring data set. Formula 2 is: in, is the dust particle concentration deviation characteristic value, is the number of grids to be cleaned in the grid array to be cleaned; The scheduling cloud platform uses Formula 3 and determines the dust particle size deviation characteristic value of the industrial area to be cleaned according to the dust monitoring data corresponding to each to-be-cleaned grid in the to-be-cleaned grid array in the dust monitoring data set. Formula 3 is: in, is the dust particle size deviation characteristic value; The scheduling cloud platform determines the target floor scrubbing water storage capacity according to the dust particle concentration deviation characteristic value, the dust particle size deviation characteristic value, the target planning path, and the dust monitoring data set; The scheduling cloud platform determines the target floor scrubbing water storage capacity according to the dust particle concentration deviation characteristic value, the dust particle size deviation characteristic value, the target planning path, and the dust monitoring data set, including: The scheduling cloud platform determines a target to-be-cleaned grid sequence from the to-be-cleaned grid array according to the target planning path. ; If the dust particle concentration deviation characteristic value is less than the preset dust particle concentration deviation threshold, and the dust particle size deviation characteristic value is less than the preset dust particle size deviation threshold, the scheduling cloud platform uses formula 4 and calculates the dust particle size deviation characteristic value according to the sequence of the grids to be cleaned. The target floor washing water storage volume is determined by the floor washing water demand corresponding to each grid to be cleaned in the grid array to be cleaned. , the formula 4 is: in, is the first The ground area of ​​the grid to be cleaned, is the first The amount of water required for washing the floor corresponding to the grid to be cleaned, To preset the cleaning area, The preset demand water volume compensation coefficient corresponding to the preset calibrated cleaning area; The scheduling cloud platform determines a target to-be-cleaned grid sequence from the to-be-cleaned grid array according to the target planning path. After that, it also includes: If the dust particle concentration deviation characteristic value is greater than or equal to the preset dust particle concentration deviation threshold, or the dust particle size deviation characteristic value is greater than or equal to the preset dust particle size deviation threshold, the scheduling cloud platform uses formula 5 and according to the sequence of the grids to be cleaned The target floor washing water storage volume is determined by the floor washing water demand corresponding to each grid to be cleaned in the grid array to be cleaned. , the formula 5 is: in, is the first The ground area of ​​the grid to be cleaned, is the first The amount of water required for washing the floor corresponding to the grid to be cleaned, is the first The preset calibrated cleaning area corresponding to the grid to be cleaned, is the first The water demand compensation coefficient of the preset calibrated cleaning area corresponding to the grid to be cleaned; Formula 5 is used in the scheduling cloud platform, and according to the sequence of the grids to be cleaned The target floor washing water storage volume is determined by the floor washing water demand corresponding to each grid to be cleaned in the grid array to be cleaned. Previously, it also included: The scheduling cloud platform uses formula 6 and according to the sequence of grids to be cleaned Determine the first The water demand compensation coefficient of the preset calibrated cleaning area corresponding to the grid to be cleaned , the formula 6 is: in, is the preset dust particle concentration compensation coefficient, is the preset dust particle size compensation coefficient, is the calibrated dust particle concentration corresponding to the preset dust particle concentration compensation coefficient, The calibrated dust particle size corresponding to the preset dust particle size compensation coefficient.

2. The method for processing floor cleaning data of industrial dust-contaminated ground according to claim 1, characterized in that: After obtaining the dust monitoring data set of the industrial area to be cleaned through the dust monitoring sensor array and sending the dust monitoring data set to the scheduling cloud platform, it also includes: The scheduling cloud platform determines a target grid to be cleaned from the grid array to be cleaned according to the dust monitoring data set, and determines the target grid to be cleaned as the starting grid of the target planning path, wherein the target grid to be cleaned is a dust pollution source grid in the grid array to be cleaned.

3. The method for processing floor cleaning data of industrial dust-contaminated ground according to claim 2 is characterized in that: The scheduling cloud platform determines the target grid to be cleaned from the grid array to be cleaned according to the dust monitoring data set, including: The scheduling cloud platform obtains first dust particle concentration characteristic data of a first grid to be cleaned in the grid array to be cleaned; The scheduling cloud platform determines a set of adjacent grids to be cleaned from the array of grids to be cleaned according to the first grid to be cleaned, wherein the grids to be cleaned in the set of adjacent grids to be cleaned are adjacent grids to the first grid to be cleaned; If the scheduling cloud platform determines that the first dust particle concentration characteristic data is greater than the dust particle concentration characteristic data of any grid to be cleaned in the set of adjacent grids to be cleaned, the first grid to be cleaned is determined to be the target grid to be cleaned.

4. The method for processing floor cleaning data of industrial dust-contaminated ground according to claim 3 is characterized in that: After determining the target to-be-cleaned grid as the starting point grid of the target planning path, the method further includes: The scheduling cloud platform determines the grid to be cleaned with the highest dust particle concentration characteristic data in the set of adjacent grids to be cleaned as the second grid to be cleaned, and determines the second grid to be cleaned as the next grid to be cleaned of the target grid to be cleaned on the target planning path; The scheduling cloud platform takes the target grid to be cleaned as the center, the second grid to be cleaned as the starting point, and the first direction around the target grid to be cleaned as the planning direction, and sequentially sorts the adjacent grids to be cleaned to generate the target planning path.

5. An electronic device, characterized in that: include: processor; as well as, A memory, configured to store executable instructions of the processor; The processor is configured to perform the method of any one of claims 1 to 4 by executing the executable instructions.

6. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer-executable instructions, which are used to implement the method according to any one of claims 1 to 4 when executed by a processor.

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