Mine car dispatching report data generation method and system based on cloud computing
Through the cloud-based mining car scheduling report data generation method, the machine learning model is used to predict future task requirements, which solves the problem that traditional data processing methods are difficult to achieve efficient mining car scheduling, and realizes the intelligence and efficiency of mining car scheduling.
Patent Information
- Application Number
- CN202510002589.7
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-01-02
- Publication Date
- 2025-05-02
AI Technical Summary
Traditional data processing methods are difficult to achieve efficient and accurate mine car scheduling in mine environments, and cannot effectively improve mine car scheduling efficiency.
The cloud-based mining car scheduling report data generation method is adopted to collect historical task data and real-time mining car operation status data, use machine learning models to predict future task requirements, and combine mining car data and task requirements to generate optimized mining car scheduling report data.
The intelligent and efficient mining vehicle scheduling has been realized, scheduling efficiency and resource utilization have been improved, costs have been reduced and security have been improved.
Smart Images
Figure CN119918874A_ABST
Abstract
Description
Technical Field
[0001] The present invention belongs to the technical field of mine car dispatch report data generation, and in particular relates to a mine car dispatch report data generation method and system based on cloud computing. Background Art
[0002] In the complex environment of mineral mining, data processing and analysis play a vital role. However, traditional data statistics and analysis methods are unable to cope with the large-scale and high-efficiency requirements of modern mining. Especially in the mine environment, due to the special environment and complex conditions, traditional data processing methods have exposed many problems.
[0003] Therefore, it is particularly important to develop an efficient and accurate data report generation algorithm and data analysis algorithm. This new algorithm needs to be able to systematically process and analyze the dispatching data in the mine environment, extract valuable information from it, and provide scientific decision-making basis for dispatchers. At the same time, the new algorithm also needs to be efficient and real-time, and be able to quickly respond to changes in the mine to ensure the dispatching efficiency of mine cars. Summary of the invention
[0004] The purpose of the present invention is to provide a method and system for generating mine car dispatch report data based on cloud computing, so as to solve the technical problem that the efficiency of mine car dispatch cannot be ensured in the prior art.
[0005] In order to achieve the above object, the present invention adopts the following technical solutions:
[0006] The method for generating mine car dispatch report data based on cloud computing includes:
[0007] Step 1: Collect historical task data and the operation status data and location data of the mine car during the mine car dispatching process;
[0008] Historical task data, including task workload, task time and task location;
[0009] The running status data of the mine car, including the real-time speed and load of the mine car;
[0010] The minecart position data refers to the real-time position of the minecart;
[0011] The running status data and location data of the mine car are collected in real time, collectively referred to as sensor data, and the sensor data are processed, sorted and grouped to obtain a mine car data set;
[0012] Step 2: Use machine learning models to predict future task requirements based on historical task data;
[0013] Step 3: Integrate the mine car data and task requirements stored in the cloud database to generate mine car scheduling report data.
[0014] Furthermore, the running status data and the location data of the mine car are collected in real time, collectively referred to as the mine car sensor data, and the mine car sensor data are processed, sorted and grouped to obtain the mine car data set. The specific method is as follows:
[0015] The Kalman filter system is used to eliminate the influence of noise interference on sensor data, and the sensor data after Kalman filtering is formatted and verified to obtain sensor data with a unified format. Then, data cleaning technology is used to identify and remove duplicate, invalid or abnormal sensor data records, and missing sensor data is completed or marked. Finally, the sensor data after data cleaning are sorted and grouped according to time sequence and mine car number to obtain a mine car data set containing key information of each mine car, wherein each mine car corresponds to a mine car number, and the mine car data set corresponding to each mine car number contains the vehicle position, speed, and load data of the mine car at each time.
[0016] Furthermore, the future task requirements are predicted based on the historical task data through the machine learning model. The specific method is as follows:
[0017] Based on historical task data, extract features related to task requirements, geographical location features of task locations, and time series features of historical task quantities, standardize or normalize the features, select an LSTM machine learning model to predict future task requirements, use some historical task data in the cloud database as training data for the LSTM machine learning model, and use other historical task data as control data to train the LSTM machine learning model. Adjust and optimize the model based on the comparison results between the control data and the model prediction data, and use the trained model to predict task requirements in the future to obtain the predicted value of future task requirements.
[0018] Furthermore, the mine car data and task requirements stored in the cloud database are integrated to generate mine car dispatch report data. The specific method is as follows:
[0019] According to the task requirements, the task information is used as the state variable. The task information only includes the task location and the amount of work to be completed. The formula S(i) = (lo(i), w(i)) is used to represent the state variable of task i, where lo(i) represents the location of task i and w(i) represents the amount of work required for task i.
[0020] The scheduling method is used as the action variable, and the action variable of scheduling method j is expressed by the formula P(j)=(x1(j), ..., xk(j), ..., xn(j)), where x1 represents the first minecart type, x1(j) represents the number of minecarts of the first minecart type in scheduling method j, xk represents the kth minecart type, xk(j) represents the number of minecarts of the kth minecart type in scheduling method j, and n represents a total of n minecart types;
[0021] Combining different tasks and scheduling methods, different scheduling methods are used to target the driving costs and task completion times required for different tasks to determine the scheduling effect indicators. The maximum scheduling effect indicator is used as the objective function. The constraints of the scheduling method are determined according to actual conditions. After determining the above objective function and constraints, the optimization algorithm is used to solve the optimal solution of the objective function. For the scheduling method that maximizes the scheduling effect indicators for different tasks under their corresponding constraints, the mine car scheduling report data is further generated.
[0022] Furthermore, the scheduling effect indicators include:
[0023] Using the formula xg(i, j)=k d *d(i, j)+k t *t(i, j) represents the scheduling effect index, where xg(i, j) represents the scheduling effect index of scheduling method j for task i, i represents the i-th task, j represents the j-th scheduling method, d(i, j) represents the driving cost required by scheduling method j for task i, t(i, j) represents the task completion time required by scheduling method j for task i, and k d Represents the weight coefficient of driving cost, k t The weight coefficient representing the task completion time.
[0024] Among them, the different positions of different mine cars in the scheduling method are determined, and the cost required for different mine cars to reach the designated work area of the task is determined according to the task location. The cost required for all mine cars in the comprehensive scheduling method to reach the designated work area of the task is determined to determine the driving cost of different tasks for different scheduling methods;
[0025] Determine the working load and speed of different types of mine cars in the scheduling method, determine the amount of work that can be completed per unit time by different types of mine cars, the number of mine cars in the comprehensive scheduling method, and determine the task completion time required to complete the task using different scheduling methods.
[0026] The present invention also provides a mine car dispatch report data generation system based on cloud computing, comprising:
[0027] The data collection module is used to collect historical task data and the operation status data and location data of the mine car during the mine car scheduling process;
[0028] The task prediction module is used to predict future task requirements based on historical task data through machine learning models;
[0029] The mine car dispatch report data generation module is used to integrate the mine car data and task requirements stored in the cloud database to generate mine car dispatch report data.
[0030] In summary, due to the adoption of the above technical solution, the beneficial effects of the present invention are:
[0031] 1. The present invention determines the scheduling effect index by comprehensively considering the driving cost and the task completion time, and determines the scheduling method that maximizes the scheduling effect index under the constraint conditions, which is helpful to achieve the optimal solution for mine car scheduling and improve the scheduling efficiency and resource utilization. By integrating the mine car data and task requirements in the cloud database, the optimized mine car scheduling report data is generated, and the intelligent and efficient mine car scheduling is realized, which not only improves the scheduling efficiency and resource utilization, but also reduces costs and improves safety. BRIEF DESCRIPTION OF THE DRAWINGS
[0032] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.
[0033] Figure 1 A step diagram of a method for generating mine car dispatch report data based on cloud computing is shown. DETAILED DESCRIPTION
[0034] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.
[0035] Embodiment 1: Figure 1 The method for generating mining car dispatch report data based on cloud computing specifically includes the following steps:
[0036] Step 1: Collect historical task data and the operation status data and location data of the mine car during the mine car scheduling process.
[0037] Collect historical task data, including the workload, task time and task location required for the task, based on the sensor devices pre-installed inside the mine car, such as GPS modules, speed sensor devices and load sensor devices, the running status data of the mine car, including the real-time speed and load of the mine car, the mine car location data refers to the real-time location of the mine car, and collect the running status data and mine car location data of the mine car in real time, collectively referred to as the mine car sensor data, through the communication module inside the mine car, such as Wi-Fi devices, 4G or 5G communication devices and LoRa devices, to ensure that the sensor data is transmitted to the cloud server in real time, and the cloud server receives the sensor data from the mine car;
[0038] The Kalman filter system is used to eliminate the influence of noise interference on sensor data, and the sensor data after Kalman filtering is formatted and verified to obtain sensor data with a unified format. Then, data cleaning technology is used to identify and remove duplicate, invalid or abnormal sensor data records, and missing sensor data is completed or marked. Finally, the sensor data after data cleaning is sorted and grouped according to time sequence and mine car number to obtain a mine car data set containing key information of each mine car, where each mine car corresponds to a mine car number, and the mine car data set corresponding to each mine car number contains the vehicle position, speed, and load data of the mine car at each time. The sorted mine car data set and historical task data are stored in the cloud database.
[0039] Step 2: Use machine learning models to predict future task requirements based on historical task data.
[0040] Based on historical task data, extract features related to task requirements, such as the time when the task occurred, the geographical location features of the task location, such as longitude and latitude, area type, and time series features of historical task volume, standardize or normalize the features, select an LSTM machine learning model to predict future task requirements, use some historical task data in the cloud database as training data for the LSTM machine learning model, and use other historical task data as control data to train the LSTM machine learning model. By comparing the control data with the model prediction data, evaluate the prediction accuracy of the model, and adjust and optimize the model based on the comparison results between the control data and the model prediction data. For example, improve the accuracy of the model prediction results by adding features and adjusting model parameters, and use the trained model to predict task requirements in the future to obtain the predicted value of future task requirements.
[0041] Step 3: Integrate the mine car data and task requirements stored in the cloud database to generate mine car scheduling report data.
[0042] Determine the minecart data stored in the cloud database. According to the task requirements, take the task information as the state variable. The task information only includes the task location and the amount of work to be completed. For task i, use the formula S(i) = (lo(i), w(i)) to represent the state variable of task i, where lo(i) represents the location of task i, and w(i) represents the amount of work required for task i. Use the scheduling method as the action variable. The scheduling method includes the type and number of minecarts to be scheduled. For scheduling method j, use the formula to represent the action variable P(j) of scheduling method j = (x1(j), ..., xk(j), ..., xn(j)), where x1 represents the first minecart type, x1(j) represents the dispatch quantity of the first minecart type in dispatch method j, where xk represents the kth minecart type, xk(j) represents the dispatch quantity of the kth minecart type in dispatch method j, and n represents a total of n minecart types. Combining different tasks and dispatch methods, the dispatch effect index is determined based on the driving cost and task completion time required for different tasks using different dispatch methods. The specific formula of the dispatch effect index is as follows:
[0043] xg(i,j)=k d *d(i, j)+k t *t(i,j);
[0044] Where xg(i, j) represents the scheduling effect index of scheduling method j for task i, i represents the i-th task, j represents the j-th scheduling method, d(i, j) represents the driving cost required by scheduling method j for task i, t(i, j) represents the task completion time required by scheduling method j for task i, and k d Represents the weight coefficient of driving cost, k t The weight coefficient representing the task completion time.
[0045] Among them, the different positions of different mine cars in the scheduling method are determined, and the cost required for different mine cars to reach the designated work area of the task is determined according to the task location. The cost required for all mine cars in the comprehensive scheduling method to reach the designated work area of the task is determined to determine the driving cost of different tasks for different scheduling methods;
[0046] Determine the working load and speed of different types of mine cars in the scheduling method, determine the amount of work that can be completed per unit time by different types of mine cars, the number of mine cars in the comprehensive scheduling method, and determine the task completion time required to complete the task using different scheduling methods.
[0047] Taking the maximum scheduling effect index as the objective function, the constraints of the scheduling method are determined according to the actual situation. The constraints include:
[0048] minxk≤xk(j)≤maxxk;
[0049] Among them, xk(j) represents the dispatch quantity of the k-th minecart type in the dispatch method j, maxxk represents the maximum number of the k-th minecart type that can be dispatched, and minxk represents the minimum number of the k-th minecart type that needs to be dispatched.
[0050] t(i, j)≤maxt(i);
[0051] Among them, t(i, j) represents the driving cost required by scheduling method j for task i, and maxd(i) represents the upper limit of the driving cost required by task i.
[0052] d(i, j)≤maxd(i);
[0053] Wherein, d(i, j) represents the task completion time required by scheduling method j for task i, and maxt(i) represents the upper limit of the task completion time required by task i.
[0054] After determining the above objective function and constraints, an optimization algorithm, such as a genetic algorithm or a particle swarm optimization algorithm, is used to solve the optimal solution of the objective function. In this embodiment, a genetic algorithm is used to solve the objective function to find a scheduling method that can maximize the scheduling effect index under the constraints. Based on the predicted values of all current task requirements and future task requirements, the above scheduling method is used to generate mine car scheduling report data to realize dynamic adjustment of mine car to mine car scheduling.
[0055] Embodiment 2: A mine car dispatch report data generation system based on cloud computing specifically comprises: a mine car data collection module, a task prediction module, and a mine car dispatch report data generation module.
[0056] Among them, the mine car data collection module is used to collect historical task data and the operation status data and location data of the mine car during the mine car scheduling process, collect historical task data, including the workload required for the task, the task time and the task location, based on the sensor devices pre-installed inside the mine car, such as GPS modules, speed sensor devices and load sensor devices, collect the operation status data and location data of the mine car in real time, collectively referred to as mine car sensor data, through the communication module inside the mine car, such as Wi-Fi devices, 4G or 5G communication devices and LoRa devices, ensure that the sensor data is transmitted to the cloud server in real time, and the cloud server receives the sensor data from the mine car;
[0057] The Kalman filter system is used to eliminate the influence of noise interference on sensor data, and the sensor data after Kalman filtering is formatted and verified to obtain sensor data with a unified format. Then, data cleaning technology is used to identify and remove duplicate, invalid or abnormal sensor data records, and missing sensor data is completed or marked. Finally, the sensor data after data cleaning is sorted and grouped according to time sequence and mine car number to obtain a mine car data set containing key information of each mine car, where each mine car corresponds to a mine car number, and the mine car data set corresponding to each mine car number contains the vehicle position, speed, and load data of the mine car at each time. The sorted mine car data set and historical task data are stored in the cloud database.
[0058] The task prediction module is used to predict future task requirements through a machine learning model based on historical task data. Based on the historical task data, features related to task requirements are extracted, such as the time when the task occurred, the geographical location features of the task location, such as longitude and latitude, area type, and time series features of the historical task volume. The features are standardized or normalized, and an LSTM machine learning model is selected to predict future task requirements. Some historical task data in the cloud database are used as training data for the LSTM machine learning model, and other historical task data are used as control data to train the LSTM machine learning model. The prediction accuracy of the model is evaluated by comparing the control data with the model prediction data. The model is adjusted and optimized according to the comparison results between the control data and the model prediction data, for example, the accuracy of the model prediction results is improved by adding features and adjusting model parameters. The trained model is used to predict task requirements in the future to obtain the predicted value of future task requirements.
[0059] The mine car scheduling report data generation module is used to integrate the mine car data stored in the cloud database with the task requirements to generate the mine car scheduling report data, determine the mine car data stored in the cloud database, and according to the task requirements, use the task information as the state variable. The task information only includes the task location and the workload required to complete the task. For task i, the formula S(i) = (lo(i), w(i)) is used to represent the state variable of task i, where lo(i) represents the location of task i, and w(i) represents the workload required for task i. The scheduling method is used as the action variable. The scheduling method includes the type of mine car to be scheduled and the number of mine cars. For the scheduling method j, the action variable P(j) of scheduling method j is expressed by the formula P(j)=(x1(j), ..., xk(j), ..., xn(j)), where x1 represents the first minecart type, x1(j) represents the number of minecarts of the first type in scheduling method j, xk represents the kth minecart type, xk(j) represents the number of minecarts of the kth type in scheduling method j, and n represents a total of n minecart types. Combining different tasks and scheduling methods, the scheduling effect index is determined based on the driving cost and task completion time required for different tasks by different scheduling methods. The specific formula of the scheduling effect index is as follows:
[0060] xg(i,j)=k d *d(i, j)+k t *t(i,j);
[0061] Where xg(i, j) represents the scheduling effect index of scheduling method j for task i, i represents the i-th task, j represents the j-th scheduling method, d(i, j) represents the driving cost required by scheduling method j for task i, t(i, j) represents the task completion time required by scheduling method j for task i, and k d Represents the weight coefficient of driving cost, k t The weight coefficient representing the task completion time.
[0062] Among them, the different positions of different mine cars in the scheduling method are determined, and the cost required for different mine cars to reach the designated work area of the task is determined according to the task location. The cost required for all mine cars in the comprehensive scheduling method to reach the designated work area of the task is determined to determine the driving cost of different tasks for different scheduling methods;
[0063] Determine the working load and speed of different types of mine cars in the scheduling method, determine the amount of work that can be completed per unit time by different types of mine cars, the number of mine cars in the comprehensive scheduling method, and determine the task completion time required to complete the task using different scheduling methods.
[0064] Taking the maximum scheduling effect index as the objective function, the constraints of the scheduling method are determined according to the actual situation. The constraints include:
[0065] minxk≤xk(j)≤maxxk;
[0066] Among them, xk(j) represents the dispatch quantity of the k-th minecart type in the dispatch method j, maxxk represents the maximum number of the k-th minecart type that can be dispatched, and minxk represents the minimum number of the k-th minecart type that needs to be dispatched.
[0067] t(i, j)≤maxt(i);
[0068] Among them, t(i, j) represents the driving cost required by scheduling method j for task i, and maxd(i) represents the upper limit of the driving cost required by task i.
[0069] d(i, j)≤maxd(i);
[0070] Wherein, d(i, j) represents the task completion time required by scheduling method j for task i, and maxt(i) represents the upper limit of the task completion time required by task i.
[0071] After determining the above objective function and constraints, an optimization algorithm, such as a genetic algorithm or a particle swarm optimization algorithm, is used to solve the optimal solution of the objective function. In this embodiment, a genetic algorithm is used to solve the objective function to find a scheduling method that can maximize the scheduling effect index under the constraints. Based on the predicted values of all current task requirements and future task requirements, the above scheduling method is used to generate mine car scheduling report data to realize dynamic adjustment of mine car to mine car scheduling.
[0072] The above description is only a preferred specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any technician familiar with the technical field can make equivalent replacements or changes according to the technical scheme and inventive concept of the present invention within the technical scope disclosed by the present invention, which should be covered by the protection scope of the present invention.
[0073] The preferred embodiments of the present invention disclosed above are only used to help explain the present invention. The preferred embodiments do not describe all the details in detail, nor do they limit the invention to only specific implementation methods. Obviously, many modifications and changes can be made according to the content of this specification. This specification selects and specifically describes these embodiments in order to better explain the principles and practical applications of the present invention, so that those skilled in the art can understand and use the present invention well. The present invention is limited only by the claims and their full scope and equivalents.
Claims
1. A method for generating mine car dispatch report data based on cloud computing, characterized in that: include: Step 1: Collect historical task data and the operation status data and location data of the mine car during the mine car dispatching process; Historical task data, including task workload, task time and task location; The running status data of the mine car, including the real-time speed and load of the mine car; The minecart position data refers to the real-time position of the minecart; The running status data and location data of the mine car are collected in real time, collectively referred to as sensor data, and the sensor data are processed, sorted and grouped to obtain a mine car data set; Step 2: Use machine learning models to predict future task requirements based on historical task data; Step 3: Integrate the mine car data and task requirements stored in the cloud database to generate mine car scheduling report data.
2. The method for generating mine car dispatch report data based on cloud computing according to claim 1, characterized in that: The running status data and location data of the mine car are collected in real time, collectively referred to as the mine car sensor data. The mine car sensor data is processed, sorted and grouped to obtain the mine car data set. The specific method is as follows: The Kalman filter system is used to eliminate the influence of noise interference on sensor data, and the sensor data after Kalman filtering is formatted and verified to obtain sensor data with a unified format. Then, data cleaning technology is used to identify and remove duplicate, invalid or abnormal sensor data records, and missing sensor data is completed or marked. Finally, the sensor data after data cleaning are sorted and grouped according to time sequence and mine car number to obtain a mine car data set containing key information of each mine car, wherein each mine car corresponds to a mine car number, and the mine car data set corresponding to each mine car number contains the vehicle position, speed, and load data of the mine car at each time.
3. The method for generating mine car dispatch report data based on cloud computing according to claim 1, characterized in that: Based on historical task data, the machine learning model is used to predict future task requirements. The specific method is as follows: Based on historical task data, extract features related to task requirements, geographical location features of task locations, and time series features of historical task quantities, standardize or normalize the features, select an LSTM machine learning model to predict future task requirements, use some historical task data in the cloud database as training data for the LSTM machine learning model, and use other historical task data as control data to train the LSTM machine learning model. Adjust and optimize the model based on the comparison results between the control data and the model prediction data, and use the trained model to predict task requirements in the future to obtain the predicted value of future task requirements.
4. The method for generating mine car dispatch report data based on cloud computing according to claim 1, characterized in that: The mine car dispatch report data is generated by integrating the mine car data and task requirements stored in the cloud database. The specific method is as follows: According to the task requirements, the task information is used as the state variable. The task information only includes the task location and the amount of work to be completed. The formula S(i) = (lo(i), w(i)) is used to represent the state variable of task i, where lo(i) represents the location of task i and w(i) represents the amount of work required for task i. The scheduling method is used as the action variable, and the action variable of scheduling method j is expressed by the formula P(j)=(x1(j), ..., xk(j), ..., xn(j)), where x1 represents the first minecart type, x1(j) represents the number of minecarts of the first minecart type in scheduling method j, xk represents the kth minecart type, xk(j) represents the number of minecarts of the kth minecart type in scheduling method j, and n represents a total of n minecart types; Combining different tasks and scheduling methods, different scheduling methods are used to target the driving costs and task completion times required for different tasks to determine the scheduling effect indicators. The maximum scheduling effect indicator is used as the objective function. The constraints of the scheduling method are determined according to actual conditions. After determining the above objective function and constraints, the optimization algorithm is used to solve the optimal solution of the objective function. For the scheduling method that maximizes the scheduling effect indicators for different tasks under their corresponding constraints, the mine car scheduling report data is further generated.
5. The method for generating mine car dispatch report data based on cloud computing according to claim 4 is characterized in that: Scheduling effect indicators include: Using the formula xg(i, j)=k d *d(i, j)+k t *t(i, j) represents the scheduling effect index, where xg(i, j) represents the scheduling effect index of scheduling method j for task i, i represents the i-th task, j represents the j-th scheduling method, d(i, j) represents the driving cost required by scheduling method j for task i, t(i, j) represents the task completion time required by scheduling method j for task i, and k d Represents the weight coefficient of driving cost, k t The weight coefficient representing the task completion time; Among them, the different positions of different mine cars in the scheduling method are determined, and the cost required for different mine cars to reach the designated work area of the task is determined according to the task location. The cost required for all mine cars in the comprehensive scheduling method to reach the designated work area of the task is determined to determine the driving cost of different tasks for different scheduling methods; Determine the working load and speed of different types of mine cars in the scheduling method, determine the amount of work that can be completed per unit time by different types of mine cars, the number of mine cars in the comprehensive scheduling method, and determine the task completion time required to complete the task using different scheduling methods.
6. A cloud computing-based mine car dispatch report data generation system, applied to a cloud computing-based mine car dispatch report data generation method according to any one of claims 1 to 5, characterized in that: include: The data collection module is used to collect historical task data and the operation status data and location data of the mine car during the mine car scheduling process; The task prediction module is used to predict future task requirements based on historical task data through machine learning models; The mine car dispatch report data generation module is used to integrate the mine car data and task requirements stored in the cloud database to generate mine car dispatch report data.