Electronic signature device control method, device, equipment and medium for quantity survey on-site acceptance

Abstract

Embodiments of the present disclosure disclose an electronic signature device control method, device, equipment and medium for quantity survey on-site acceptance. A specific implementation of the method comprises: in response to receiving an electronic signature signal sent by an electronic signature device, obtaining a three-level tree of engineering construction; performing engineering deviation checking on each actual engineering quantity data obtained in advance; performing objection node screening processing on the three-level tree of engineering construction to obtain an objection node set, an objection related node set and a no objection node set; performing objection detection processing on the objection node set to obtain an objection detection result; performing inspection and right confirmation processing on the objection node set, the objection related node set and the no objection node set to obtain an inspection and right confirmation result; generating an engineering acceptance report; and controlling the electronic signature device to perform an electronic signature task on the engineering acceptance report. The implementation can reduce the time consumed during acceptance, reduce the waiting time of the electronic signature device and reduce waste of hardware resources.

Description

Technical Field

[0001] The embodiments disclosed herein relate to the field of computer technology, and more specifically to a method, apparatus, device, and medium for controlling electronic signature devices for online acceptance of engineering quantities. Background Technology

[0002] The control method for electronic signature devices used for online acceptance of engineering quantities involves online acceptance of engineering quantities and electronically signing the acceptance report after acceptance. Currently, the common approach for controlling electronic signature devices is as follows: the system accepts all engineering data; once all engineering data has passed acceptance, an acceptance report is generated; and then the electronic signature device is controlled to electronically sign the acceptance report.

[0003] However, when using the above method to control electronic signature devices, the following technical problems often arise: All engineering data will be rejected if there are any problematic nodes. Even after the problematic nodes are corrected and re-uploaded, all engineering data still needs to be re-accepted. This means that each review requires re-acceptance of the data that is not problematic, resulting in a long acceptance time. Consequently, the generation of acceptance reports is delayed, and the electronic signature device waits too long for signatures. This leads to the electronic signature device being idle for a long time, resulting in a waste of hardware resources.

[0004] The information disclosed in this background section is only intended to enhance the understanding of the background of the present disclosure concept, and therefore may contain information that does not constitute prior art known to those skilled in the art. Summary of the Invention

[0005] The summary portion of this disclosure is intended to provide a brief overview of the concepts, which will be described in detail in the detailed description portion. This summary portion is not intended to identify key or essential features of the claimed technical solutions, nor is it intended to limit the scope of the claimed technical solutions.

[0006] Some embodiments of this disclosure provide methods, apparatus, electronic devices, and computer-readable media for controlling electronic signature devices for online acceptance of engineering quantities, in order to solve one or more of the technical problems mentioned in the background section above.

[0007] In a first aspect, some embodiments of this disclosure provide a control method for an electronic signature device for online acceptance of engineering quantities. The method includes: in response to receiving an electronic signature signal sent by the electronic signature device, acquiring a three-level engineering construction tree, wherein the three-level engineering construction tree corresponds to various expected engineering quantity data; based on the expected engineering quantity data corresponding to the three-level engineering construction tree, performing engineering deviation verification on various pre-acquired actual engineering quantity data to obtain various deviation verification values; based on the various deviation verification values, performing objection node filtering processing on the three-level engineering construction tree to obtain an objection node set, an objection-related node set, and a non-objection node set; performing objection detection processing on the objection node set to obtain objection detection results; in response to determining that the objection detection results meet preset objection detection conditions, performing verification and confirmation processing on the objection node set, the objection-related node set, and the non-objection node set to obtain verification and confirmation results; generating an engineering acceptance report based on the verification and confirmation results; and controlling the electronic signature device to perform an electronic signature task on the engineering acceptance report based on the engineering acceptance report.

[0008] Secondly, some embodiments of this disclosure provide an electronic signature device control apparatus for online acceptance of engineering quantities. The apparatus includes: an acquisition unit configured to acquire a three-level engineering construction tree in response to receiving an electronic signature signal sent by an electronic signature device, wherein the three-level engineering construction tree corresponds to various expected engineering quantity data; an engineering deviation verification unit configured to perform engineering deviation verification on various pre-acquired actual engineering quantity data based on the various expected engineering quantity data corresponding to the three-level engineering construction tree, and obtain various deviation verification values; and an objection node filtering unit configured to filter objection nodes in the three-level engineering construction tree based on the various deviation verification values. The process involves filtering to obtain a set of objectionable nodes, a set of objection-related nodes, and a set of nodes without objection. An objection detection unit is configured to perform objection detection processing on the aforementioned set of objectionable nodes to obtain an objection detection result. An verification and confirmation unit is configured to, in response to determining that the objection detection result meets preset objection detection conditions, perform verification and confirmation processing on the aforementioned set of objectionable nodes, the aforementioned set of objection-related nodes, and the aforementioned set of nodes without objection to obtain a verification and confirmation result. A generation unit is configured to generate an engineering acceptance report based on the aforementioned verification and confirmation result. A control unit is configured to control the aforementioned electronic signature device to perform an electronic signature task on the aforementioned engineering acceptance report based on the aforementioned engineering acceptance report.

[0009] Thirdly, some embodiments of this disclosure provide an electronic device, including: one or more processors; and a storage device having one or more programs stored thereon, wherein when the one or more programs are executed by the one or more processors, the one or more processors implement the method described in any implementation of the first aspect above.

[0010] Fourthly, some embodiments of this disclosure provide a computer-readable medium having a computer program stored thereon, wherein the program, when executed by a processor, implements the method described in any of the implementations of the first aspect above.

[0011] The above embodiments of this disclosure have the following beneficial effects: The electronic signature device control method for online acceptance of engineering quantities according to some embodiments of this disclosure can reduce acceptance time and the waiting time for electronic signature devices to sign. Specifically, the reason for the long acceptance time and the long waiting time for electronic signature devices to sign is that: if there is a problematic node in the engineering data, all engineering data will be rejected. After the problematic node is corrected and re-uploaded, all engineering data still needs to be re-accepted. This results in the need to re-accept data without problems during each review, leading to a long acceptance time, which in turn causes a delay in the generation of acceptance reports and an excessively long waiting time for electronic signature devices to sign, resulting in long-term idle operation of electronic signature devices and wasted hardware resources. Based on this, the electronic signature device control method for online acceptance of engineering quantities according to some embodiments of this disclosure firstly, in response to receiving the electronic signature signal sent by the electronic signature device, obtains a three-level engineering construction tree, wherein the three-level engineering construction tree corresponds to various expected engineering quantity data. Thus, the three-level engineering construction tree can be obtained. Secondly, based on the expected project quantity data corresponding to the aforementioned three-level construction tree, the pre-acquired actual project quantity data are checked for project deviations to obtain various deviation check values. This allows for deviation checks on each node in the three-level construction tree. Then, based on these deviation check values, the aforementioned three-level construction tree is processed to filter disputed nodes, resulting in a set of disputed nodes, a set of dispute-related nodes, and a set of undisputed nodes. This allows for the classification of each leaf node in the three-level construction tree, resulting in a set of disputed nodes, a set of dispute-related nodes, and a set of undisputed nodes. Then, the disputed node set is processed for dispute detection, resulting in dispute detection results. This allows for further detection of disputed leaf nodes, resulting in dispute detection results. Then, in response to the determination that the dispute detection results meet preset dispute detection conditions, the disputed node set, the dispute-related node set, and the undisputed node set are subjected to verification and confirmation processing, resulting in verification and confirmation results. Therefore, when no disputes are detected in any node of the disputed node set, the disputed node set, the dispute-related node set, and the undisputed node set can be further verified and confirmed. Then, based on the above verification and confirmation results, an engineering acceptance report is generated. Thus, when all nodes in the objection node set, the aforementioned objection-related node set, and the aforementioned unobjection node set are found to be without objection, an engineering acceptance report can be generated. Finally, based on the engineering acceptance report, the aforementioned electronic signature device is controlled to perform an electronic signature task on the engineering acceptance report. Therefore, the engineering acceptance report can be electronically signed.Because we can first filter out the objection node set, objection-related node set, and non-objection node set from the three-level tree of engineering construction, and then perform objection detection on the objection node set, and only after the objection node set passes the objection detection can we perform the final acceptance of the three-level tree of engineering construction, instead of still performing objection detection on all leaf nodes in the three-level tree of engineering construction after filtering out the objection node set, we can reduce the time consumed during acceptance, reduce the waiting time of electronic signature equipment, reduce the idle time of electronic signature equipment, and thus reduce the waste of hardware resources. Attached Figure Description

[0012] The above and other features, advantages, and aspects of the embodiments of this disclosure will become more apparent from the accompanying drawings and the following detailed description. Throughout the drawings, the same or similar reference numerals denote the same or similar elements. It should be understood that the drawings are schematic, and elements are not necessarily drawn to scale.

[0013] Figure 1 This is a flowchart of some embodiments of the electronic signature device control method for online acceptance of engineering quantities according to the present disclosure; Figure 2 This is a structural schematic diagram of some embodiments of the electronic signature device control method for online acceptance of engineering quantities according to the present disclosure; Figure 3 This is a schematic diagram of the structure of an electronic device suitable for implementing some embodiments of the present disclosure. Detailed Implementation

[0014] Embodiments of this disclosure will now be described in more detail with reference to the accompanying drawings. While some embodiments of this disclosure are shown in the drawings, it should be understood that this disclosure can be implemented in various forms and should not be construed as limited to the embodiments set forth herein. Rather, these embodiments are provided to provide a more thorough and complete understanding of this disclosure. It should be understood that the accompanying drawings and embodiments of this disclosure are for illustrative purposes only and are not intended to limit the scope of protection of this disclosure.

[0015] It should also be noted that, for ease of description, only the parts relevant to the invention are shown in the accompanying drawings. Unless otherwise specified, the embodiments and features described in this disclosure can be combined with each other.

[0016] It should be noted that the concepts of "first" and "second" mentioned in this disclosure are used only to distinguish different devices, modules or units, and are not used to limit the order of functions performed by these devices, modules or units or their interdependencies.

[0017] It should be noted that the terms "a" and "a plurality of" used in this disclosure are illustrative rather than restrictive, and those skilled in the art should understand that, unless otherwise expressly indicated in the context, they should be understood as "one or more".

[0018] The names of messages or information exchanged between multiple devices in the embodiments of this disclosure are for illustrative purposes only and are not intended to limit the scope of such messages or information.

[0019] This disclosure will now be described in detail with reference to the accompanying drawings and embodiments.

[0020] Figure 1 A flow 100 of some embodiments of an electronic signature device control method for online acceptance of engineering quantities according to the present disclosure is shown. The electronic signature device control method for online acceptance of engineering quantities includes the following steps: Step 101: In response to receiving the electronic signature signal sent by the electronic signature device, obtain the three-level tree of the project construction.

[0021] In some embodiments, the executing entity (e.g., a computing device) of the electronic signature device control method for online acceptance of engineering quantities can obtain a three-level tree of engineering construction in response to receiving an electronic signature signal sent by the electronic signature device. The executing entity can be a server. The electronic signature device can be a device for electronically signing documents. For example, the electronic signature device can be a computing device with an electronic signature client installed. The electronic signature client can be a client capable of electronic signing. For example, the electronic signature client can be Microsoft Office. The electronic signature signal can be a signal used to prompt the executing entity that the electronic signature device can begin electronic signing.

[0022] The aforementioned three-level construction tree can be used to represent the quantities of work during construction. This three-level construction tree can have a three-layer structure. The first layer of the tree can be the root node. The value corresponding to the root node can be the name of the project. For example, the value corresponding to the root node could be "Construction Project for Floors 123".

[0023] The second level of the aforementioned three-level construction tree can include various intermediate nodes. Each of these intermediate nodes is a child node of the root node. The value corresponding to each of these intermediate nodes can be a material quantity label. This material quantity label can be a label used to characterize the total amount of materials used during construction. This material quantity label corresponds to a unit. For example, the material quantity label can be, but is not limited to, "concrete area" or "reinforcement weight." "Concrete area" can characterize the area covered by the concrete pour, and the corresponding unit can be square meters. "Reinforcement weight" can characterize the weight of the reinforcement used, and the corresponding unit can be kilograms.

[0024] The third level of the aforementioned construction tree can include leaf nodes. Each leaf node can be a child node of an intermediate node. An intermediate node can correspond to any of the leaf nodes. The value corresponding to each leaf node can be a component name. The component name can be the name of the building component during construction. For example, the component name can be, but is not limited to, "103-1 Wall" or "204 Floor Slab". "103-1 Wall" can represent the wall numbered 1 in interior room 103. "204 Floor Slab" can represent the floor slab in interior room 204.

[0025] Each of the aforementioned leaf nodes corresponds to expected project quantity data. This expected project quantity data can be the numerical value of the amount of materials used during construction, predicted before construction begins. For example, when the material quantity label of the intermediate node corresponding to the leaf node is "concrete area", the component name corresponding to the leaf node is "103-1 wall", and the corresponding expected project quantity data is "10", it can be represented that the area of ​​concrete poured for wall number 1 in interior 103 is 10 square meters.

[0026] Step 102: Based on the expected engineering quantity data corresponding to the three-level engineering construction tree, perform engineering deviation verification on the pre-acquired actual engineering quantity data to obtain the deviation verification values.

[0027] In some embodiments, the aforementioned execution entity can perform engineering deviation verification on each pre-acquired actual engineering quantity data based on the expected engineering quantity data corresponding to the aforementioned three-level engineering construction tree, obtaining each deviation verification value. Each actual engineering quantity data can be a numerical value representing the quantity of materials actually used during construction. Each actual engineering quantity data corresponds to a material total amount label and a component name. For example, when the material total amount label corresponding to the actual engineering quantity data is "reinforcing steel weight", the corresponding component name is "204 floor slab", and the actual engineering quantity data is "11", it can represent that the weight of the reinforcing steel used in the 204 interior floor slab is 11 kg. Each deviation verification value can be a numerical value representing the degree of deviation between the expected engineering quantity data and the corresponding actual engineering quantity data. Each deviation verification value corresponds to one expected engineering quantity data, one actual engineering quantity data, one intermediate node, and one leaf node.

[0028] In some optional implementations of certain embodiments, the aforementioned execution entity may perform engineering deviation verification on each pre-acquired actual engineering quantity data based on the expected engineering quantity data corresponding to the aforementioned three-level engineering construction tree, thereby obtaining each deviation verification value: For each of the above actual project quantity data, perform the following steps: The first step is to determine the intermediate nodes of the project based on the aforementioned three-level construction tree and the material quantity labels corresponding to the actual project quantity data. Specifically, the intermediate nodes can be those in the three-level construction tree whose corresponding material quantity labels match the actual material quantity labels. The actual material quantity labels can be the material quantity labels corresponding to the actual project quantity data. In practice, firstly, the material quantity labels corresponding to the actual project quantity data can be determined as the actual material quantity labels. Secondly, the intermediate nodes in the three-level construction tree whose corresponding material quantity labels match the actual material quantity labels can be determined as the intermediate nodes of the project.

[0029] The second step is to determine the leaf nodes of the project based on the intermediate nodes of the project and the component names corresponding to the actual project quantity data. These leaf nodes can be the leaf nodes in the project's three-level construction tree whose component names match the actual component names. The actual component names can be the component names corresponding to the actual project quantity data.

[0030] In practice, the aforementioned implementing entity can identify the leaf nodes whose component names are the same as the actual component names among the leaf nodes included in the intermediate nodes of the project as the project leaf nodes.

[0031] The third step is to determine the target expected project quantity data based on the expected project quantity data corresponding to the leaf nodes of the aforementioned project and the three-level construction tree of the aforementioned project. The target expected project quantity data can be the expected project quantity data corresponding to the leaf nodes of the aforementioned project. In practice, the executing entity can determine the expected project quantity data corresponding to the leaf nodes of the aforementioned project as the target expected project quantity data.

[0032] The fourth step involves determining the engineering correction coefficients based on a pre-defined correction coefficient correspondence table and the aforementioned intermediate nodes. The correction coefficient correspondence table can be a table representing the relationship between correction coefficients and material quantity labels. This table can include each correction coefficient and each material quantity label. There is a one-to-one correspondence between correction coefficients and material quantity labels. Each correction coefficient can be a numerical value used to correct the expected engineering quantity data. For example, a correction coefficient could be "1.015". The engineering correction coefficients can be the correction coefficients corresponding to the aforementioned intermediate nodes.

[0033] In practice, firstly, the total material quantity labels corresponding to the intermediate nodes of the above-mentioned project can be identified as target material labels. Secondly, the total material quantity labels in the correction coefficient correspondence table that are the same as the target material labels can be identified as labels to be processed. Then, the correction coefficients corresponding to the labels to be processed can be identified as project correction coefficients.

[0034] The fifth step is to determine the corrected expected project quantity by multiplying the above-mentioned target expected project quantity data with the above-mentioned project correction coefficient.

[0035] The sixth step is to determine the difference between the actual engineering quantity data and the revised expected engineering quantity as the engineering quantity difference value.

[0036] The seventh step is to determine the ratio of the above-mentioned difference in the amount of work to the above-mentioned revised expected amount of work as the deviation verification value.

[0037] Step 103: Based on each deviation verification value, the objection nodes of the three-level tree of engineering construction are screened to obtain the objection node set, the objection-related node set, and the no-objection node set.

[0038] In some embodiments, the executing entity may, based on the aforementioned deviation verification values, perform objection node screening on the aforementioned three-level engineering construction tree to obtain a set of objection nodes, a set of objection-related nodes, and a set of unobjection nodes. Each objection node in the objection node set may be a leaf node with objections. Each objection-related node in the set of objection-related nodes may be a leaf node associated with an objection node. Each unobjection node in the set of unobjection nodes may be a leaf node without objections.

[0039] In some optional implementations of certain embodiments, the aforementioned execution entity may perform objection node filtering on the aforementioned three-level engineering construction tree based on the aforementioned deviation verification values ​​through the following steps, thereby obtaining a set of objection nodes, a set of objection-related nodes, and a set of nodes without objections: The first step is to perform the following steps for each of the above deviation verification values: The first sub-step involves determining the target deviation intermediate node and target deviation leaf node based on the aforementioned deviation verification values ​​and the intermediate and leaf nodes included in the aforementioned three-level engineering construction tree. Specifically, the target deviation intermediate node can be the intermediate node corresponding to the aforementioned deviation verification value, and the target deviation leaf node can be the leaf node corresponding to the aforementioned deviation verification value.

[0040] In practice, firstly, the executing entity can determine the intermediate node corresponding to the above deviation verification value as the target deviation intermediate node. Secondly, it can determine the leaf node corresponding to the above deviation verification value as the target deviation leaf node.

[0041] The second sub-step involves determining the objection deviation threshold and objection-related threshold based on a preset node threshold correspondence table and the aforementioned target deviation intermediate nodes. The node threshold correspondence table can be a table representing the correspondence between each deviation threshold, each related threshold, and each material total amount label. This table can include each deviation threshold, each related threshold, and each material total amount label. The deviation threshold, related threshold, and material total amount label correspond one-to-one. The deviation threshold can be a value used to determine whether there is an objection at the leaf node corresponding to the aforementioned deviation verification value. For example, the deviation threshold can be 0.05. The related threshold can be a value less than the corresponding deviation threshold. For example, the related threshold can be 0.03. The objection deviation threshold can be the deviation threshold corresponding to the aforementioned target deviation intermediate node. The objection-related threshold can be the related threshold corresponding to the aforementioned target deviation intermediate node.

[0042] In practice, firstly, the implementing entity can determine the total material quantity label corresponding to the intermediate node of the target deviation as the target deviation label. Then, it can determine the total material quantity label that is identical to the target deviation label among the various total material quantity labels included in the node threshold correspondence table as the target threshold label. Next, the deviation threshold corresponding to the target threshold label can be determined as the objection deviation threshold. Finally, the relevant threshold corresponding to the target threshold label can be determined as the objection-related threshold.

[0043] The third sub-step is to determine the absolute value of the above deviation verification value as the absolute value of the deviation verification.

[0044] The fourth sub-step is to determine the target deviation leaf node as an objection node in response to the determination that the above deviation verification value is greater than the above objection deviation threshold.

[0045] The fifth sub-step, in response to determining that the above deviation check value is less than or equal to the above objection deviation threshold, performs the following steps: Sub-step one: In response to determining that the above deviation verification value is greater than the above objection-related threshold, the above target deviation leaf node is determined as an objection-related node.

[0046] Sub-step two: In response to determining that the above deviation verification value is less than or equal to the above objection-related threshold, the above target deviation leaf node is determined as an objection-free node.

[0047] The second step is to combine the identified objection nodes into an objection node set.

[0048] The third step is to combine the identified objection-related nodes into an objection-related node set.

[0049] The fourth step is to combine the identified undisputed nodes into a set of undisputed nodes.

[0050] In some optional implementations of certain embodiments, the aforementioned execution entity may perform objection node filtering on the aforementioned three-level engineering construction tree based on the aforementioned deviation verification values ​​through the following steps, thereby obtaining a set of objection nodes, a set of objection-related nodes, and a set of nodes without objections: The first step is to perform the following steps for each intermediate node in the three-level tree of the above-mentioned construction project: The first sub-step involves identifying each leaf node included in the aforementioned intermediate nodes as a leaf node to be processed.

[0051] The second sub-step is to determine the number of each of the above-mentioned leaf nodes to be processed as the number of leaf nodes.

[0052] The third sub-step involves determining each pending verification value based on the aforementioned leaf nodes to be processed and each aforementioned deviation verification value. Each pending verification value can be the deviation verification value corresponding to the leaf node to be processed. In practice, the deviation verification values ​​corresponding to the aforementioned leaf nodes to be processed can be used as the pending verification values.

[0053] The fourth sub-step involves generating the mean value and standard value of the verification values ​​to be processed, based on the aforementioned values ​​to be processed. The mean value can be the average of the values ​​to be processed, and the standard value can be the standard deviation of the values ​​to be processed.

[0054] The fifth sub-step involves performing outlier detection processing on each of the aforementioned unprocessed verification values ​​based on the mean and standard values ​​of the unprocessed verification values, thereby obtaining individual unprocessed outlier values. Each unprocessed outlier value can be a numerical value characterizing the degree to which each unprocessed verification value deviates from the aforementioned unprocessed verification values.

[0055] In practice, for each of the above-mentioned checksums to be processed, firstly, the difference between the checksum to be processed and the mean of the checksums to be processed is determined as the checksum difference to be processed. The ratio of the checksum difference to the standard checksum to be processed is determined as the outlier to be processed.

[0056] The sixth sub-step involves performing deviation magnitude detection processing on each of the aforementioned verification values ​​based on the mean value to be processed, thereby obtaining various deviation magnitude values. Each of these deviation magnitude values ​​can be a numerical value characterizing the degree to which the verification value deviates from the mean value to be processed.

[0057] In practice, for each of the above-mentioned check values ​​to be processed, firstly, the difference between the above-mentioned check value to be processed and the average of the above-mentioned check values ​​to be processed can be determined as the deviation difference. Secondly, the absolute value of the above-mentioned deviation difference can be determined as the absolute deviation value.

[0058] Then, the sum of the individual deviations from absolute values ​​can be determined as the total deviation from absolute values.

[0059] Then, for each of the above-mentioned check values ​​to be processed, the ratio of the absolute deviation value corresponding to the above-mentioned check value to the total absolute deviation data can be determined as the deviation magnitude value.

[0060] The seventh sub-step generates a group of objection nodes, a group of objection-related nodes, and a group of unobjection nodes based on the aforementioned leaf nodes to be processed, the number of leaf nodes, the aforementioned verification values ​​to be processed, the aforementioned outliers to be processed, and the aforementioned deviation magnitude values.

[0061] In practice, firstly, in response to determining that the number of leaf nodes is less than or equal to a preset node number threshold, each of the leaf nodes to be processed can be identified as a dissenting node, and the dissenting nodes can be grouped into a dissenting node group. The node number threshold can be a pre-set value. For example, the node number threshold can be 2.

[0062] Secondly, in response to determining that the number of leaf nodes is greater than the threshold number of nodes, for each of the leaf nodes to be processed, the following steps are performed: First, the unprocessed verification value, unprocessed outlier value, and deviation magnitude value corresponding to the leaf node to be processed are respectively determined as the target unprocessed verification value, the target unprocessed outlier value, and the target deviation magnitude value. Second, the absolute value of the target unprocessed outlier value can be determined as the target outlier absolute value. Third, in response to determining that the target outlier absolute value is greater than or equal to a preset outlier threshold, the leaf node to be processed can be determined as an objection node. The preset outlier threshold can be 1. Fourth, the absolute value of the target unprocessed verification value can be determined as the absolute verification value. Fifth, in response to determining that the target outlier absolute value is less than the preset outlier threshold, the following steps are performed: In response to determining that the absolute verification value is greater than or equal to the preset verification threshold, and the target deviation magnitude value is greater than or equal to the preset magnitude threshold, the leaf node to be processed can be determined as an objection node. The preset verification threshold can be a pre-set value. For example, the preset verification threshold can be 0.1. The aforementioned preset amplitude threshold can be a pre-set value. For example, the aforementioned preset amplitude threshold can be 0.3. In response to determining that the aforementioned absolute verification value is less than the aforementioned preset verification threshold and the aforementioned target deviation amplitude value is less than the aforementioned preset amplitude threshold, the aforementioned leaf node to be processed can be determined as a node to be classified.

[0063] Then, the identified dissenting nodes can be grouped into dissenting node groups. Similarly, the identified nodes to be classified can be grouped into unclassified node groups.

[0064] Then, in response to determining that the above-mentioned objection node group is empty, each unclassified node included in the above-mentioned unclassified node group can be determined as an unobjection node, and the above-mentioned unobjection nodes can be combined into an unobjection node group.

[0065] Then, in response to determining that the aforementioned objection node group is not empty, for each unclassified node in the aforementioned unclassified node group, the following steps are performed: In response to determining that the target deviation magnitude value and the target outlier absolute value corresponding to the aforementioned unclassified node meet preset objection-related conditions, the aforementioned unclassified node can be determined as an objection-related node. Wherein, the aforementioned objection-related conditions can be that the target deviation magnitude value corresponding to the aforementioned unclassified node is greater than the aforementioned preset magnitude threshold or the target outlier absolute value corresponding to the aforementioned unclassified node is less than the preset absolute outlier threshold. Wherein, the aforementioned absolute outlier threshold can be a preset value. For example, the aforementioned absolute outlier threshold can be 0.5. Then, in response to determining that the target deviation magnitude value and the target outlier absolute value corresponding to the aforementioned unclassified node do not meet the aforementioned objection-related conditions, the aforementioned unclassified node can be determined as a no-objection node.

[0066] Then, each determined objection-related node can be combined into an objection-related node group. Each determined non-objection node can be combined into a non-objection node group.

[0067] In the second step, based on each generated objection node group, each generated objection-related node group and each generated non-objection node group, an objection node set, an objection-related node set and a non-objection node set are generated. In practice, all objection nodes included in each generated objection node group can be combined into an objection node set. Then, all objection-related nodes included in each generated objection-related node group can be combined into an objection-related node set. Then, all non-objection nodes included in each generated non-objection node group can be combined into a non-objection node set.

[0068] Step 104: performing objection detection processing on the objection node set to obtain an objection detection result.

[0069] In some embodiments, the aforementioned execution subject may perform objection detection processing on the aforementioned objection node set to obtain an objection detection result. Wherein, the aforementioned objection detection result may be a label used to characterize whether the aforementioned objection node set has an objection. For example, the aforementioned objection detection result may be "objection" or "non-objection".

[0070] In the process of using the technical solution to solve the technical problem in the above background art, for the application scenario: the acceptance of people's livelihood projects (schools, hospitals) is often accompanied by the following technical problem: the acceptance system has poor ability to identify the authenticity of materials, and it is easy to determine the objection nodes corresponding to forged materials as qualified nodes, and complete the signature and storage of the acceptance approval report after all project data are qualified. This leads to the need to consume a large amount of computing power to re-conduct secondary verification of all project data to trace the counterfeiting problem in the subsequent review stage, and the completed electronic signatures also lose legal effect, resulting in waste of computing resources and signature resource waste of electronic signature equipment during secondary verification. In view of the following demand characteristics of this application scenario: people's livelihood projects are directly related to social public safety. If forged materials are not identified in the acceptance link, these buildings with potential quality hazards will be put into use, bringing great safety hazards. In addition, when a counterfeiting problem is found in the subsequent review, relevant personnel must conduct a secondary verification on all project data, which increases the labor cost of verification. Moreover, secondary verification requires calling high-performance servers for large-scale data comparison and correlation analysis, which consumes a great deal of computing resources. At the same time, the acceptance documents that have been electronically signed also need to go through the whole process of approval and signature again, resulting in intensive repeated calls to the electronic signature equipment, and the signature requests of normal projects can only queued and waited, which seriously delays the acceptance progress of normal projects. We decide to adopt the following solution: In some optional implementations of certain embodiments, the aforementioned execution entity may perform objection detection processing on the aforementioned objection node set through the following steps to obtain the objection detection result: The first step, for each dissenting node in the above set of dissenting nodes, is to perform the following steps: The first sub-step involves acquiring the material identification image and construction record data corresponding to the aforementioned objection node. The material identification image can be an image of the material's measurement readings taken by construction personnel during construction of the component corresponding to the objection node. The material identification image includes the material to be measured, the measuring instrument, and the instrument's readings during the measurement. The construction record data can be data representing the time and location of construction on the component corresponding to the objection node. The construction record data can include the construction time and location. The construction time can be a timestamp indicating the start of construction on the component corresponding to the objection node. The construction location can be the geographical coordinates representing the location of construction on the component corresponding to the objection node.

[0071] In practice, the aforementioned implementing entity can send pre-set construction data acquisition information, the component name corresponding to the disputed node, and the total material quantity tag corresponding to the disputed node to the target terminal. The target terminal can be the terminal corresponding to a technical personnel. The construction data acquisition information can be information used to obtain the material identification image and construction record data of the disputed node. For example, the construction data acquisition information could be "Return the material identification image and construction record data of the disputed node." Then, the entity can receive the material identification image and construction record data returned by the target terminal.

[0072] The second sub-step involves performing spatiotemporal data verification on the aforementioned material identification images based on the construction record data, thereby obtaining a spatiotemporal verification result. This result can be a label used to characterize whether the time and location when the material identification images were taken match the aforementioned construction record data. For example, the spatiotemporal verification result can be "matched" or "not matching".

[0073] In practice, firstly, a time string can be read from the metadata of the aforementioned material identification image using a time reading command as the image time string. This time reading command can be any command capable of reading the time when the image was captured. For example, when the material identification image is named "image", the time reading command could be "exiftool -DateTimeOriginal -s3 image". The aforementioned image time string can be a string representing time. For example, the image time string could be "2026:01:15 14:30:25". Then, the aforementioned image time string can be converted into a timestamp using a time conversion function as the image timestamp. This time conversion function can be any function capable of converting a time string into a timestamp. For example, the time conversion function could be the datetime.strptime() function.

[0074] Then, the latitude and longitude in decimal form can be read from the metadata of the aforementioned material identification image using location reading commands, and the latitude and longitude can be combined into geographic coordinates as the image's location coordinates. The aforementioned location reading commands can be commands that can read the latitude and longitude at the time the image was captured. For example, when the material identification image is named "image", the aforementioned location reading command can be "exiftool -GPSLatitude -GPSLongitude -n -s3image".

[0075] Then, the difference between the image timestamp and the construction time included in the construction record data can be determined as the time difference data. In response to determining that the absolute value of the time difference data is greater than a preset time threshold, "discrepancy" can be determined as the spatiotemporal verification result. The preset time threshold can be a pre-set value. Here, the specific setting of the preset time threshold is not limited.

[0076] In response to determining that the absolute value of the aforementioned time difference data is less than or equal to the aforementioned preset time threshold, the distance between the aforementioned construction site and the aforementioned image location coordinates can be determined as the distance difference data. In response to determining that the aforementioned distance difference data is greater than the aforementioned preset distance threshold, "discordation" can be determined as the spatiotemporal verification result. The aforementioned preset distance threshold can be a pre-set value. Here, the specific setting of the aforementioned preset distance threshold is not limited. In response to determining that the aforementioned distance difference data is less than or equal to the aforementioned preset distance threshold, "match" can be determined as the spatiotemporal verification result.

[0077] The third sub-step involves, in response to determining that the spatiotemporal verification result meets the preset spatiotemporal verification conditions, performing text extraction processing on the material identification image to obtain the engineering quantity data for each image. The spatiotemporal verification condition can be that the spatiotemporal verification result is "matching". Each image engineering quantity data can be text data extracted from the material identification image. Each image engineering quantity data corresponds to engineering quantity border data. The engineering quantity border data can be data used to describe the position of the image bounding box. For example, the engineering quantity border data can be [1,5,4,2], which can represent that the image coordinates of the upper left corner vertex of the image bounding box are (1,5), and the image coordinates of the lower right corner vertex are (4,2). The image bounding box can be a rectangular bounding box in the material identification image used to mark the position of the image engineering quantity data.

[0078] In practice, the aforementioned execution entity can use a text recognition engine to identify each text in the material identification image as engineering quantity data for each image, and can also identify the engineering quantity bounding box data for each text. The text recognition engine can be a tool capable of extracting text and its bounding box from an image. For example, the text recognition engine could be Tesseract OCR.

[0079] The fourth sub-step involves determining the material quantity data and material border data based on the aforementioned image quantity data and their corresponding border data. The material quantity data can be the image quantity data that best represents the quantity of the disputed node. The material border data can be the border data corresponding to the material quantity data.

[0080] In practice, firstly, at least one image engineering quantity data that is numerical can be identified as at least one image engineering quantity to be processed. Secondly, the expected engineering quantity data corresponding to the objection node can be identified as the objection node engineering quantity. Then, for each of the at least one image engineering quantity to be processed, the difference between the image engineering quantity to be processed and the objection node engineering quantity can be identified as the objection engineering difference value. The absolute value of the objection engineering difference value can be identified as the objection engineering absolute value.

[0081] Then, the absolute value of the objection project with the smallest absolute value among the determined objection project absolute values ​​can be determined as the target objection project absolute value.

[0082] Then, the image engineering quantity corresponding to the absolute value of the aforementioned target objection engineering can be determined as material engineering quantity data. The engineering quantity border data corresponding to the aforementioned material engineering quantity data can be determined as material border data.

[0083] The fifth sub-step involves performing tampering detection on the material identification image based on the aforementioned material border data, obtaining a tampering detection result. This tampering detection result can be a label used to characterize whether the material identification image has been tampered with. For example, the tampering detection result can be "tampered with" or "not tampered with".

[0084] In practice, firstly, the difference between the third and first values ​​in the material border data can be determined as the width data. Then, the absolute value of the width data can be determined as the image width. Secondly, the difference between the second and fourth values ​​in the material border data can be determined as the height data. Then, the absolute value of the height data can be determined as the image height. Finally, the first and second values ​​in the material border data, the image width, and the image height can be combined into an array as a cropping array.

[0085] Then, the aforementioned material identification image and the aforementioned cropping array can be input into a cropping function to crop the image region corresponding to the cropping array from the aforementioned material identification image as the target region image. The cropping function can be a function capable of cropping a specified region in an image. For example, the cropping function can be the imcrop() function. Then, the aforementioned target region image can be saved as a new image as a copy region image. Then, the image matrix corresponding to the aforementioned target region image can be determined as the target region image matrix. The image matrix corresponding to the aforementioned copy region image can be determined as the copy region image matrix. Then, the difference between the aforementioned target region image matrix and the aforementioned copy region image matrix can be determined as an image difference matrix. Then, the individual element values ​​included in the aforementioned image difference matrix can be determined as individual image element values. Then, the absolute values ​​corresponding to the aforementioned individual image element values ​​can be determined as individual image absolute element values. Then, the average value of the aforementioned individual image absolute element values ​​can be determined as the average image change value. Then, in response to determining that the average image change value is greater than a preset change threshold, "tampering" can be determined as a tampering trace detection result. The aforementioned change threshold can be a preset value. Here, the specific setting of the aforementioned change threshold is not limited. Then, in response to determining that the average value of the aforementioned image changes is less than or equal to the aforementioned change threshold, "no tampering" can be determined as the tampering trace detection result.

[0086] The sixth sub-step involves, in response to determining that the aforementioned tampering detection result meets a preset tampering detection condition, identifying the expected engineering quantity data corresponding to the disputed node as the disputed engineering quantity data. The aforementioned tampering detection condition can be that the aforementioned tampering detection result is "no tampering". The seventh sub-step involves verifying the reasonableness of the disputed project quantity data and the aforementioned material project quantity data to obtain a correctness verification result. This correctness verification result can be a label indicating whether the disputed project quantity data may be falsely reported. For example, the correctness verification result could be "false report" or "no false report".

[0087] In practice, the difference between the disputed engineering quantity data and the material engineering quantity data can be determined as the material difference value. Then, the absolute value of the material difference value can be determined as the material absolute value. Then, in response to determining that the absolute value of the material is less than a preset false alarm threshold, "no false alarm" can be determined as the correctness verification result. The preset false alarm threshold can be a pre-set value. For example, the preset false alarm threshold can be 1. In response to determining that the absolute value of the material is greater than or equal to the preset false alarm threshold, "false alarm" can be determined as the correctness verification result.

[0088] The second step involves generating average deviation data and standard deviation data based on the deviation verification values ​​corresponding to the aforementioned set of objection nodes, in response to the determination that each obtained correctness verification result meets a preset reasonableness condition. The reasonableness condition can be that the correctness verification result is "no false alarms". The average deviation data can be the average of the deviation verification values ​​corresponding to the aforementioned set of objection nodes. The standard deviation data can be the standard deviation of the deviation verification values ​​corresponding to the aforementioned set of objection nodes.

[0089] In practice, the deviation check values ​​corresponding to the aforementioned set of objection nodes can be determined as individual objection check values. Secondly, the average value of these objection check values ​​can be determined as the average deviation data. Finally, the standard deviation of these objection check values ​​can be determined as the standard deviation data.

[0090] The third step is to generate a deviation range interval based on the aforementioned average deviation data and standard deviation data. This deviation range interval can be an interval composed of the aforementioned average deviation data and standard deviation data.

[0091] In practice, the product of the aforementioned standard deviation data and a preset value can be used to determine the deviation change value. The preset value can be 3. Then, the sum of the aforementioned average deviation data and the aforementioned deviation change value can be used to determine the maximum change value. Then, the difference between the aforementioned average deviation data and the aforementioned deviation change value can be used to determine the minimum change value. Finally, the minimum change value and the maximum change value can be combined to form a closed interval as the deviation range.

[0092] Fourth, based on the aforementioned deviation range, perform a comprehensive verification of each deviation check value corresponding to the aforementioned set of objection nodes to obtain the overall verification result. This overall verification result can serve as a label characterizing whether each deviation check value corresponding to the aforementioned set of objection nodes belongs to the aforementioned deviation range. For example, the overall verification result could be "fully belongs" or "not entirely belongs".

[0093] In practice, in response to determining that all deviation verification values ​​corresponding to the aforementioned set of objection nodes belong to the aforementioned deviation range, "all belong" can be determined as the overall verification result. Then, in response to determining that one deviation verification value among the aforementioned set of objection nodes does not belong to the aforementioned deviation range, "not entirely belong" can be determined as the overall verification result.

[0094] Fifth, in response to the determination that the overall verification result meets the preset overall verification conditions, the preset verification pass data is determined as the objection detection result. The overall verification conditions can be that the overall verification result is "all belong". The verification pass data can be "no objection".

[0095] The above technical solution and its related content, combined with step 107, serve as an inventive point of this disclosure, solving the problem of "waste of signature resources." Factors leading to this waste of signature resources often include: the acceptance system has poor ability to identify the authenticity of materials, easily misclassifying the objection nodes corresponding to counterfeit materials as qualified nodes, and completing the signature retention of the acceptance report after all engineering data is qualified. This leads to a subsequent review stage requiring significant computing power to re-verify all engineering data to trace the forgery problem, and the completed electronic signature loses its legal validity, resulting in a waste of computing resources during the secondary verification and a waste of signature resources for the electronic signature device. Solving these factors can reduce the waste of signature resources. To achieve this effect, this disclosure firstly, for each objection node in the aforementioned objection node set, performs the following steps: Secondly, obtain the material identification image and construction record data corresponding to the aforementioned objection node. Thus, the material identification image and construction record data can be obtained. Then, based on the aforementioned construction record data, spatiotemporal data verification is performed on the aforementioned material identification image to obtain the spatiotemporal verification result. Thus, spatiotemporal data verification can be performed on the material identification image to detect its correctness. Then, in response to determining that the spatiotemporal verification result meets the preset spatiotemporal verification conditions, text extraction processing is performed on the material identification image to obtain the engineering quantity data for each image. Each of the engineering quantity data for each image corresponds to engineering quantity border data. Therefore, when the spatiotemporal verification result indicates that the material identification image is correct, the engineering quantity data captured and stored during construction can be extracted from the material identification image. Next, based on the engineering quantity data for each image and the corresponding engineering quantity border data, the material engineering quantity data and material border data are determined. Thus, the material engineering quantity data and material border data can be obtained. Then, based on the material border data, tampering detection is performed on the material identification image to obtain tampering detection results. Thus, tampering detection can be performed on the material identification image to determine whether the material identification image has been tampered with later. Then, in response to determining that the tampering detection results meet the preset tampering detection conditions, the expected engineering quantity data corresponding to the objection node is determined as the objection engineering quantity data. Therefore, when the tampering detection results indicate that the material identification image has not been tampered with, the expected engineering quantity data corresponding to the disputed node can be verified based on the material identification image. Then, the reasonableness of the disputed engineering quantity data and the material engineering quantity data is verified to obtain the correctness verification result. Then, in response to determining that each obtained correctness verification result meets the preset reasonableness conditions, average deviation data and standard deviation data are generated based on the deviation verification values ​​corresponding to the disputed node set.Therefore, average deviation data and standard deviation data can be obtained. Then, based on the average deviation data and standard deviation data, a deviation range interval is generated. Then, based on the deviation range interval, a holistic verification is performed on each deviation check value corresponding to the above-mentioned objection node set to obtain the overall verification result. Finally, in response to determining that the above-mentioned overall verification result meets the preset overall verification conditions, the preset verification pass data is determined as the objection detection result. Because spatiotemporal comparison and tamper detection steps can be performed on the material identification images and construction record data corresponding to the objection nodes, after determining that the material identification images and construction record data are correct, objection detection is performed on the objection nodes using the material identification images and construction record data. This improves the reliability of the material identification images and construction record data, reduces the probability of secondary verification and traceability of falsification issues in all engineering data during the review stage due to low reliability of the material identification images and construction record data, reduces the waste of computational resources during secondary verification, and reduces the waste of signature resources of electronic signature devices.

[0096] Optionally, after performing objection detection processing on the aforementioned objection node set and obtaining the objection detection result, the aforementioned executing entity may also perform the following steps: The first step, in response to the determination that the above-mentioned objection detection results do not meet the above-mentioned objection detection conditions, is to designate the preset acceptance return data as the acceptance data to be sent. The above-mentioned acceptance return data can be data used to characterize that the project corresponding to the above-mentioned three-level construction tree has not met the standards. For example, the above-mentioned acceptance return data can be "does not meet the standards".

[0097] The second step is to send the aforementioned acceptance data to the target terminal. The target terminal can be the terminal used by the technical personnel.

[0098] Step 105: In response to determining that the objection detection result meets the preset objection detection conditions, the objection node set, the objection-related node set, and the non-objection node set are subjected to verification and confirmation processing to obtain the verification and confirmation result.

[0099] In some embodiments, the executing entity may, in response to determining that the objection detection result meets a preset objection detection condition, perform verification and confirmation processing on the objection node set, the objection-related node set, and the non-objection node set to obtain a verification and confirmation result. The objection detection condition may be that the objection detection result is "no objection".

[0100] In addressing the aforementioned technical problems by employing technical solutions, the application scenario of inspecting building construction projects often presents the following challenges: During inspection, the system performs indiscriminate traversal testing on all building components, but the testing depth for core components affecting structural safety is insufficient. This leads to low inspection accuracy, resulting in low reliability of the acceptance report generated based on the inspection results. Consequently, when initiating the signing process based on a low-reliability acceptance report, the resulting signed document lacks genuine and valid verification evidence, leading to a waste of electronic signature resources. The following characteristics are required for this application scenario: Building construction projects involve numerous building components, among which core components such as columns and walls directly affect the overall safety of the building. Insufficient testing depth may lead to serious quality problems, such as insufficient load-bearing capacity, going undetected and potentially causing major safety accidents. Furthermore, each use of electronic signature devices consumes significant network resources. When multiple projects are being accepted simultaneously, the normal signing process is blocked by a large number of signed documents lacking genuine and valid verification evidence, causing serious delays in the acceptance progress of normal projects. Therefore, we have decided to adopt the following solution: In some optional implementations of certain embodiments, the aforementioned executing entity may perform verification and confirmation processing on the aforementioned objection node set, the aforementioned objection-related node set, and the aforementioned non-objection node set through the following steps to obtain the verification and confirmation results: The first step is to combine the above-mentioned set of objection nodes and the above-mentioned set of objection-related nodes into a set of nodes to be tested.

[0101] The second step involves identifying the nodes that meet the preset concrete component conditions as target inspection nodes. These concrete component conditions can be defined as follows: the intermediate node corresponding to the node to be inspected is a concrete node, and the component name corresponding to the node to be inspected contains any one of the preset strings. The concrete node can be an intermediate node whose corresponding material quantity label contains "concrete area." Each of the preset strings can be a string corresponding to a component that requires concrete pouring. For example, the preset strings can include, but are not limited to, "shear wall," "floor slab," and "column."

[0102] Third, for each of the above target verification nodes, perform the following steps: The first sub-step involves obtaining the total concrete volume data and component thickness data corresponding to the aforementioned target inspection node. The total concrete volume data can be a numerical value representing the volume of concrete used when pouring concrete for the area represented by the aforementioned target inspection node. The component thickness data can be the thickness of the component corresponding to the aforementioned target inspection node.

[0103] In practice, the aforementioned executing entity can determine the component name corresponding to the target inspection node as the component name to be returned. Secondly, it can send the component name to be returned and the component usage information to the target terminal. The component usage information can be used to obtain total concrete data and component thickness data. For example, the component usage information could be "Please return the total concrete data and component thickness data corresponding to the aforementioned component name." Then, it can receive the total concrete data and component thickness data returned by the target terminal.

[0104] The second sub-step is to determine the ratio of the total concrete volume data to the component thickness data as the maximum area of ​​the component.

[0105] The third sub-step involves determining the actual area of ​​the component by multiplying its maximum area by a preset maximum loss coefficient. The maximum loss coefficient can be a value representing the maximum allowable loss of concrete. For example, the maximum loss coefficient could be 0.95.

[0106] The fourth sub-step involves comparing the actual area of ​​the aforementioned component with the expected quantity of work corresponding to the aforementioned target inspection node to obtain the node deviation rate. This node deviation rate can be a percentage representing the degree of deviation between the actual area of ​​the aforementioned component and the expected quantity of work corresponding to the aforementioned target inspection node.

[0107] In practice, the expected quantity of work corresponding to the aforementioned target inspection nodes can be determined as the target inspection quantity. Then, the difference between the target inspection quantity and the actual area of ​​the component can be determined as the area difference value. Next, the ratio of the area difference value to the target inspection quantity can be determined as the node deviation value. Finally, the percentage of the node deviation value can be determined as the node deviation rate.

[0108] The fifth sub-step involves determining the rebar usage data corresponding to the target inspection node in response to the determination that the node deviation rate meets a preset node deviation condition. The node deviation condition can be that the absolute value of the node deviation rate is less than a preset percentage threshold. This percentage threshold can be a pre-set percentage. The specific setting of this percentage threshold is not limited. The rebar usage data can be a numerical value representing the weight of the rebar used to characterize the component corresponding to the target inspection node.

[0109] In practice, firstly, the component name corresponding to the aforementioned target inspection node can be determined as the target inspection component name. Secondly, the intermediate nodes in the aforementioned three-level construction tree whose total material quantity label is "reinforcement weight" can be determined as reinforcement intermediate nodes. Then, for each leaf node included in the aforementioned reinforcement intermediate nodes, these leaf nodes can be determined as reinforcement leaf nodes. Next, the reinforcement leaf nodes whose component names are the same as the aforementioned target inspection component names can be determined as target reinforcement leaf nodes. Finally, the expected quantity data corresponding to the aforementioned target reinforcement leaf nodes can be determined as reinforcement usage data.

[0110] The sixth sub-step involves determining the steel reinforcement quantity data as the steel reinforcement volume data in response to the determination that the aforementioned steel reinforcement quantity data meets preset data validity conditions. The ratio of the aforementioned steel reinforcement quantity data to preset steel reinforcement density data is then used as the steel reinforcement volume data. The data validity condition can be that the aforementioned steel reinforcement quantity data is not empty. The aforementioned steel reinforcement density data can be the density of the steel reinforcement.

[0111] The seventh sub-step involves generating the engineering volumetric reinforcement ratio based on the aforementioned maximum loss coefficient, the aforementioned steel reinforcement volume data, and the aforementioned total concrete volume data. The engineering volumetric reinforcement ratio can be the steel reinforcement ratio corresponding to the aforementioned steel reinforcement volume data and the aforementioned total concrete volume data.

[0112] In practice, the implementing entity can determine the actual total concrete volume by multiplying the total concrete volume data by the maximum loss coefficient. Then, the ratio of the steel reinforcement volume data to the actual total concrete volume can be determined as the volumetric reinforcement ratio. Finally, the percentage of the volumetric reinforcement ratio can be determined as the project's volumetric reinforcement rate.

[0113] The eighth sub-step, in response to determining that the aforementioned engineering volume reinforcement ratio is less than a preset reinforcement ratio threshold, determines a set of associated nodes based on the aforementioned target inspection node and the aforementioned set of undisputed nodes. The aforementioned reinforcement ratio threshold can be a percentage representing the maximum allowable steel reinforcement ratio of the building. For example, the aforementioned reinforcement ratio threshold can be 5%. Each associated node in the aforementioned set of associated nodes can be an undisputed node in the aforementioned set of undisputed nodes that is associated with the aforementioned target inspection node.

[0114] In practice, firstly, the component name corresponding to the aforementioned target inspection node can be determined as the node name to be processed. Secondly, for each of the aforementioned preset strings, in response to determining that the node name to be processed contains the aforementioned preset string, the aforementioned preset string can be determined as the target string. Then, the component names corresponding to the aforementioned set of no-dispute nodes can be determined as each no-dispute name. Then, the no-dispute names containing the aforementioned target string can be determined as each associated name. Then, the no-dispute nodes corresponding to the aforementioned associated names can be determined as each associated node. Finally, the aforementioned associated nodes can be combined into an associated node set.

[0115] The ninth sub-step involves determining the quantity of each target inspection task based on the aforementioned target inspection nodes and the set of associated nodes. Each of these target inspection quantities can be the expected quantity data corresponding to a target inspection node or an associated node.

[0116] In practice, the aforementioned implementing entities can determine the expected engineering quantity data corresponding to the aforementioned target inspection nodes and the expected engineering quantity data corresponding to the aforementioned associated node sets as the respective target inspection engineering quantities.

[0117] The tenth sub-step involves generating target inspection average data and target inspection standard data based on the aforementioned target inspection quantities. The target inspection average data can be the average of the aforementioned target inspection quantities. The target inspection standard data can be the standard deviation of the aforementioned target inspection quantities.

[0118] In practice, the implementing entity can determine the average value of each of the aforementioned target inspection quantities as the target inspection average data. The standard deviation of each of the aforementioned target inspection quantities can be determined as the target inspection standard data.

[0119] The eleventh sub-step involves generating a target test interval based on the aforementioned target test average data and target test standard data. This target test interval can be an interval generated from the aforementioned target test average data and target test standard data.

[0120] In practice, firstly, the product of the aforementioned preset value and the aforementioned target test standard data can be determined as the test range data. Then, the sum of the aforementioned target test average data and the aforementioned test range data can be determined as the maximum test value. Next, the difference between the aforementioned target test average data and the aforementioned test range data can be determined as the minimum test value. Finally, the aforementioned minimum test value and the aforementioned maximum test value can be combined into a closed interval as the target test interval.

[0121] The twelfth sub-step generates a correlation deviation verification result based on the aforementioned target inspection nodes and target inspection intervals. This correlation deviation verification result can be a label used to characterize whether the expected engineering quantity data corresponding to the aforementioned target inspection node belongs to the aforementioned target inspection interval. For example, the correlation deviation verification result can be "belongs to" or "does not belong to".

[0122] In practice, the expected quantity of work corresponding to the aforementioned target inspection nodes can be determined as the quantity of work at the inspection nodes. In response to determining that the quantity of work at the aforementioned inspection nodes belongs to the aforementioned target inspection interval, "belongs to" can be determined as the result of the correlation deviation verification. In response to determining that the quantity of work at the aforementioned inspection nodes does not belong to the aforementioned target inspection interval, "does not belong to" can be determined as the result of the correlation deviation verification.

[0123] The fourth step involves generating a verification and weighting result based on the generated correlation deviation verification results. This verification and weighting result can be a label characterizing whether each node in the set of nodes to be verified meets the requirements. For example, the verification and weighting result can be "compliant" or "non-compliant". In practice, in response to determining that all the correlation deviation verification results are "belongs to", "compliant" can be determined as the verification and weighting result. In response to determining that any one of the correlation deviation verification results is "does not belong to", "non-compliant" can be determined as the verification and weighting result.

[0124] The above technical solution and its related content, combined with step 107, serve as an inventive point of this disclosure, solving the problem of "waste of signature resources." Factors leading to this waste of signature resources often include: during engineering inspections, the system performs indiscriminate traversal testing on all building components, but the testing depth for core components affecting structural safety is insufficient, resulting in low inspection accuracy. This leads to low reliability of the acceptance report generated based on the inspection results, and consequently, when the signature process is initiated based on a low-reliability acceptance report, the resulting signature document lacks genuine and valid verification evidence, resulting in wasted signature resources when calling electronic signature devices for electronic signatures. Solving these factors can reduce the waste of signature resources. To achieve this effect, this disclosure first combines the aforementioned objection node set and the aforementioned objection-related node set into a set of nodes to be inspected. Second, each node in the set of nodes to be inspected that meets the preset concrete component conditions is identified as a target inspection node. Thus, the nodes requiring concrete for inspection can be selected. Then, for each of the aforementioned target inspection nodes, the following steps are performed: First, obtain the total concrete volume data and component thickness data corresponding to the target inspection node. This yields the total concrete volume data and component thickness data. Then, determine the maximum area of ​​the component by the ratio of the total concrete volume data to the component thickness data. This yields the theoretical pouring area corresponding to the total concrete volume data and the component thickness data. Then, determine the actual area of ​​the component by the product of the maximum component area and a preset maximum loss coefficient. This yields the pouring area corresponding to the total concrete volume data and the component thickness data after loss. Next, compare the deviation between the actual component area and the expected quantity data corresponding to the target inspection node to obtain the node deviation rate. This yields the degree of deviation between the actual component area and the expected quantity data corresponding to the target inspection node. Then, in response to determining that the node deviation rate meets a preset node deviation condition, determine the steel reinforcement quantity data corresponding to the target inspection node. Thus, when the node deviation rate meets the preset node deviation condition, the steel reinforcement quantity data for the target inspection node is obtained. Then, in response to determining that the above-mentioned steel reinforcement quantity data meets the preset data validity conditions, the ratio of the above-mentioned steel reinforcement quantity data to the preset steel reinforcement density data is determined as the steel reinforcement volume data. Thus, the steel reinforcement volume data can be obtained. Next, based on the above-mentioned maximum loss coefficient, the above-mentioned steel reinforcement volume data, and the above-mentioned total concrete volume data, the engineering volume reinforcement ratio is generated. Thus, the engineering volume reinforcement ratio can be obtained. Then, in response to determining that the above-mentioned engineering volume reinforcement ratio is less than a preset reinforcement ratio threshold, based on the above-mentioned target inspection node and the above-mentioned set of no-dispute nodes, a set of associated nodes is determined. Thus, the set of associated nodes can be formed by filtering out each no-dispute node associated with the above-mentioned target inspection node from the set of no-dispute nodes.Then, based on the aforementioned target inspection nodes and the aforementioned set of associated nodes, the quantity of each target inspection project is determined. This allows us to determine the quantity of each target inspection project. Next, based on the aforementioned quantity of each target inspection project, target inspection average data and target inspection standard data are generated. This allows us to obtain the target inspection average data and target inspection standard data. Then, based on the aforementioned target inspection average data and the aforementioned target inspection standard data, target inspection intervals are generated. This allows us to generate target inspection intervals. Then, based on the aforementioned target inspection nodes and the aforementioned target inspection intervals, associated deviation verification results are generated. This allows us to obtain associated deviation verification results. Finally, based on the generated associated deviation verification results, inspection confirmation results are generated. This allows us to obtain inspection confirmation results. Because it allows for in-depth inspection of key component nodes affecting building safety in the project, rather than simply performing indiscriminate traversal inspections on all building components, the accuracy of the inspection can be improved. This, in turn, can improve the reliability of the generated acceptance report, reducing the probability of electronically signed documents lacking genuine and valid verification evidence due to low reliability of the acceptance report, and reducing the waste of signing resources when using electronic signing devices.

[0125] Step 106: Based on the inspection and confirmation results, generate the project acceptance report.

[0126] In some embodiments, the aforementioned executing entity can generate an engineering acceptance report based on the aforementioned inspection and confirmation results. The engineering acceptance report can be a PDF file representing that the project corresponding to the aforementioned engineering construction three-level tree has passed acceptance. For example, the content of the engineering acceptance report may include: "Project 123 was accepted on April 12, 2026, with the acceptance opinion: Passed." In practice, in response to determining that the aforementioned inspection and confirmation results are "compliant," the aforementioned engineering construction three-level tree and preset acceptance information can be sent to the aforementioned target terminal. The aforementioned acceptance information can be information used to notify technical personnel that the acceptance has been passed. Then, the PDF file returned by the aforementioned target terminal can be received as the engineering acceptance report.

[0127] Step 107: Based on the project acceptance report, control the electronic signature device to perform the electronic signature task on the project acceptance report.

[0128] In some embodiments, the aforementioned execution entity may, based on the aforementioned project acceptance report, control the aforementioned electronic signature device to perform an electronic signature task on the aforementioned project acceptance report.

[0129] In some optional implementations of certain embodiments, the aforementioned execution entity may control the aforementioned electronic signature device to perform an electronic signature task on the aforementioned project acceptance report based on the following steps: The first step is to encode the aforementioned project acceptance report to obtain a project acceptance string. This project acceptance string can be the encoded version of the project acceptance report. In practice, the executing entity can use a hash algorithm to encode the project acceptance report to obtain the project acceptance string. The hash algorithm can be SHA-256.

[0130] The second step involves obtaining the corresponding page number and position data for each of the preset signature numbers. The signature number can be the number corresponding to the seal to be affixed in the electronic signature device. The page number can be the page number on the project acceptance report where the seal corresponding to the signature number is affixed. The position data can be data representing the position of the seal corresponding to the signature number when affixed to the corresponding page in the project acceptance report. For example, the position data could be [215.5, 465.0], representing the center coordinates of the seal corresponding to the signature number as (215.5, 465.0).

[0131] In practice, for each of the aforementioned signature numbers, the signature acquisition information and the signature number can be sent to the target terminal. The signature acquisition information can be used to obtain the corresponding signature page number and signature position data. Then, the signature page number and signature position data returned by the target terminal can be received.

[0132] The third step is to combine each of the above signature numbers, the corresponding signature page number, and the signature position data into signature data for each signature number.

[0133] The fourth step involves controlling the electronic signature device to perform an electronic signature task on the project acceptance report, based on the aforementioned project acceptance report, the aforementioned project acceptance string, and the obtained signature data. In practice, a preset start signature signal, the aforementioned project acceptance report, the aforementioned project acceptance string, and the obtained signature data can be sent to the electronic signature device to control the electronic signature device to perform an electronic signature on the project acceptance report. The aforementioned start signature signal can be a signal used to prompt the electronic signature device to begin the electronic signature process.

[0134] The above embodiments of this disclosure have the following beneficial effects: The electronic signature device control method for online acceptance of engineering quantities according to some embodiments of this disclosure can reduce acceptance time and the waiting time for electronic signature devices to sign. Specifically, the reason for the long acceptance time and the long waiting time for electronic signature devices to sign is that: if there is a problematic node in the engineering data, all engineering data will be rejected. After the problematic node is corrected and re-uploaded, all engineering data still needs to be re-accepted. This results in the need to re-accept data without problems during each review, leading to a long acceptance time, which in turn causes a delay in the generation of acceptance reports and an excessively long waiting time for electronic signature devices to sign, resulting in long-term idle operation of electronic signature devices and wasted hardware resources. Based on this, the electronic signature device control method for online acceptance of engineering quantities according to some embodiments of this disclosure firstly, in response to receiving the electronic signature signal sent by the electronic signature device, obtains a three-level engineering construction tree, wherein the three-level engineering construction tree corresponds to various expected engineering quantity data. Thus, the three-level engineering construction tree can be obtained. Secondly, based on the expected project quantity data corresponding to the aforementioned three-level construction tree, the pre-acquired actual project quantity data are checked for project deviations to obtain various deviation check values. This allows for deviation checks on each node in the three-level construction tree. Then, based on these deviation check values, the aforementioned three-level construction tree is processed to filter disputed nodes, resulting in a set of disputed nodes, a set of dispute-related nodes, and a set of undisputed nodes. This allows for the classification of each leaf node in the three-level construction tree, resulting in a set of disputed nodes, a set of dispute-related nodes, and a set of undisputed nodes. Then, the disputed node set is processed for dispute detection, resulting in dispute detection results. This allows for further detection of disputed leaf nodes, resulting in dispute detection results. Then, in response to the determination that the dispute detection results meet preset dispute detection conditions, the disputed node set, the dispute-related node set, and the undisputed node set are subjected to verification and confirmation processing, resulting in verification and confirmation results. Therefore, when no disputes are detected in any node of the disputed node set, the disputed node set, the dispute-related node set, and the undisputed node set can be further verified and confirmed. Then, based on the above verification and confirmation results, an engineering acceptance report is generated. Thus, when all nodes in the objection node set, the aforementioned objection-related node set, and the aforementioned unobjection node set are found to be without objection, an engineering acceptance report can be generated. Finally, based on the engineering acceptance report, the aforementioned electronic signature device is controlled to perform an electronic signature task on the engineering acceptance report. Therefore, the engineering acceptance report can be electronically signed.Because we can first filter out the objection node set, objection-related node set, and non-objection node set from the three-level tree of engineering construction, and then perform objection detection on the objection node set, and only after the objection node set passes the objection detection can we perform the final acceptance of the three-level tree of engineering construction, instead of still performing objection detection on all leaf nodes in the three-level tree of engineering construction after filtering out the objection node set, we can reduce the time consumed during acceptance, reduce the waiting time of electronic signature equipment, reduce the idle time of electronic signature equipment, and thus reduce the waste of hardware resources.

[0135] Further reference Figure 2 As an implementation of the methods shown in the above figures, this disclosure provides some embodiments of an electronic signature device control device for online acceptance of engineering quantities. These device embodiments are similar to... Figure 1 Corresponding to the method embodiments shown, the device can be specifically applied to various electronic devices.

[0136] like Figure 2 As shown, an electronic signature device control device 200 for online acceptance of engineering quantities in some embodiments includes: an acquisition unit 201, an engineering deviation verification unit 202, an objection node filtering unit 203, an objection detection unit 204, an inspection and confirmation unit 205, a generation unit 206, and a control unit 207. The acquisition unit 201 is configured to acquire a three-level engineering construction tree in response to receiving an electronic signature signal sent by the electronic signature device, wherein the three-level engineering construction tree corresponds to various expected engineering quantity data; the engineering deviation verification unit 202 is configured to perform engineering deviation verification on various pre-acquired actual engineering quantity data based on the expected engineering quantity data corresponding to the three-level engineering construction tree, obtaining various deviation verification values; the objection node filtering unit 203 is configured to perform objection node filtering processing on the three-level engineering construction tree based on the various deviation verification values, obtaining an objection node set, an objection-related node set, and a set of no objections. The system includes: a set of objection nodes; an objection detection unit 204 configured to perform objection detection processing on the aforementioned set of objection nodes to obtain objection detection results; an verification and confirmation unit 205 configured to, in response to determining that the aforementioned objection detection results meet preset objection detection conditions, perform verification and confirmation processing on the aforementioned set of objection nodes, the aforementioned set of objection-related nodes, and the aforementioned set of nodes without objections to obtain verification and confirmation results; a generation unit 206 configured to generate an engineering acceptance report based on the aforementioned verification and confirmation results; and a control unit 207 configured to control the aforementioned electronic signature device to perform an electronic signature task on the aforementioned engineering acceptance report based on the aforementioned engineering acceptance report.

[0137] It is understandable that the units described in the device 200 are related to the reference. Figure 1The steps in the described method correspond to each other. Therefore, the operations, features, and beneficial effects described above for the method also apply to the device 200 and the units contained therein, and will not be repeated here.

[0138] The following is for reference. Figure 3 It shows a schematic diagram of the structure of an electronic device (such as a computing device) 300 suitable for implementing some embodiments of the present disclosure. Figure 3 The electronic device shown is merely an example and should not be construed as limiting the functionality and scope of the embodiments of this disclosure.

[0139] like Figure 3 As shown, the electronic device 300 may include a processing unit (e.g., a central processing unit, a graphics processing unit, etc.) 301, which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 302 or a program loaded from a storage device 308 into a random access memory (RAM) 303. The RAM 303 also stores various programs and data required for the operation of the electronic device 300. The processing unit 301, ROM 302, and RAM 303 are interconnected via a bus 304. An input / output (I / O) interface 305 is also connected to the bus 304.

[0140] Typically, the following devices can be connected to I / O interface 305: input devices 306 including, for example, touchscreens, touchpads, keyboards, mice, cameras, microphones, accelerometers, gyroscopes, etc.; output devices 307 including, for example, liquid crystal displays (LCDs), speakers, vibrators, etc.; storage devices 308 including, for example, magnetic tapes, hard disks, etc.; and communication devices 309. Communication device 309 allows electronic device 300 to communicate wirelessly or wiredly with other devices to exchange data. Although Figure 3 An electronic device 300 with various devices is shown; however, it should be understood that it is not required to implement or possess all of the devices shown. More or fewer devices may be implemented or possessed alternatively. Figure 3 Each box shown can represent a device or multiple devices as needed.

[0141] In particular, according to some embodiments of this disclosure, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, some embodiments of this disclosure include a computer program product comprising a computer program carried on a computer-readable medium, the computer program containing program code for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via communication device 309, or installed from storage device 308, or installed from ROM 302. When the computer program is executed by processing device 301, it performs the functions defined in the methods of some embodiments of this disclosure.

[0142] It should be noted that, in some embodiments of this disclosure, the computer-readable medium may be a computer-readable signal medium or a computer-readable storage medium, or any combination thereof. A computer-readable storage medium may be, for example,—but not limited to—an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of a computer-readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination thereof. In some embodiments of this disclosure, a computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device. In some embodiments of this disclosure, a computer-readable signal medium may include a data signal propagated in baseband or as part of a carrier wave, carrying computer-readable program code. Such propagated data signals may take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. A computer-readable signal medium can be any computer-readable medium other than a computer-readable storage medium, which can send, propagate, or transmit a program for use by or in connection with an instruction execution system, apparatus, or device. The program code contained on the computer-readable medium can be transmitted using any suitable medium, including but not limited to: wires, optical fibers, RF (radio frequency), etc., or any suitable combination thereof.

[0143] In some implementations, clients and servers can communicate using any currently known or future-developed network protocol such as HTTP (Hypertext Transfer Protocol) and can interconnect with digital data communication (e.g., communication networks) of any form or medium. Examples of communication networks include local area networks (“LANs”), wide area networks (“WANs”), the Internet (e.g., the Internet of Things), and peer-to-peer networks (e.g., ad hoc peer-to-peer networks), as well as any currently known or future-developed networks.

[0144] The aforementioned computer-readable medium may be included in the aforementioned electronic device; or it may exist independently and not assembled into the electronic device. The aforementioned computer-readable medium carries one or more programs. When the electronic device executes the aforementioned one or more programs, the electronic device causes the following actions: In response to receiving an electronic signature signal from an electronic signature device, it acquires a three-level engineering construction tree, wherein the three-level engineering construction tree corresponds to various expected engineering quantity data; based on the various expected engineering quantity data corresponding to the three-level engineering construction tree, it performs engineering deviation verification on various pre-acquired actual engineering quantity data to obtain various deviation verification values; based on the various deviation verification values, it performs objection node screening processing on the aforementioned three-level engineering construction tree to obtain an objection node set, an objection-related node set, and a non-objection node set; it performs objection detection processing on the aforementioned objection node set to obtain objection detection results; in response to determining that the aforementioned objection detection results meet preset objection detection conditions, it performs verification and confirmation processing on the aforementioned objection node set, the aforementioned objection-related node set, and the aforementioned non-objection node set to obtain verification and confirmation results; based on the aforementioned verification and confirmation results, it generates an engineering acceptance report; and based on the aforementioned engineering acceptance report, it controls the aforementioned electronic signature device to perform an electronic signature task on the aforementioned engineering acceptance report.

[0145] Computer program code for performing operations of some embodiments of this disclosure can be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, and C++, and conventional procedural programming languages ​​such as the "C" language or similar programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving remote computers, the remote computer can be connected to the user's computer via any type of network—including a local area network (LAN) or a wide area network (WAN)—or can be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0146] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of this disclosure. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, can be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0147] The units described in some embodiments of this disclosure can be implemented in software or hardware. The described units can also be housed in a processor; for example, a processor may be described as including an acquisition unit, an engineering deviation verification unit, an objection node filtering unit, an objection detection unit, an inspection and confirmation unit, a generation unit, and a control unit. The names of these units do not necessarily limit the specific unit; for example, the acquisition unit may also be described as a "unit for acquiring the three-level tree of engineering construction."

[0148] The functions described above in this document can be performed at least in part by one or more hardware logic components. For example, exemplary types of hardware logic components that can be used, without limitation, include: field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), application-specific standard products (ASSPs), system-on-a-chip (SoCs), complex programmable logic devices (CPLDs), and so on.

[0149] The above description is merely a selection of preferred embodiments of this disclosure and an explanation of the technical principles employed. Those skilled in the art should understand that the scope of the invention involved in the embodiments of this disclosure is not limited to technical solutions formed by specific combinations of the above-described technical features, but should also cover other technical solutions formed by arbitrary combinations of the above-described technical features or their equivalents without departing from the above-described inventive concept. For example, technical solutions formed by substituting the above-described features with (but not limited to) technical features with similar functions disclosed in the embodiments of this disclosure.

Claims

1. A control method for electronic signature devices used in online acceptance of engineering quantities, characterized in that, include: In response to receiving an electronic signature signal sent by an electronic signature device, a three-level engineering construction tree is obtained, wherein the three-level engineering construction tree corresponds to various expected engineering quantity data; Based on the expected engineering quantity data corresponding to the three-level tree of engineering construction, the engineering deviation is checked on the actual engineering quantity data obtained in advance to obtain the deviation check value. Based on the aforementioned deviation verification values, the objection node screening process is performed on the three-level tree of the engineering construction to obtain the objection node set, the objection-related node set, and the no-objection node set. The objection node set is subjected to objection detection processing to obtain the objection detection result; In response to determining that the objection detection result meets the preset objection detection conditions, the objection node set, the objection-related node set, and the non-objection node set are subjected to verification and confirmation processing to obtain the verification and confirmation result; Based on the inspection and confirmation results, an engineering acceptance report is generated; Based on the project acceptance report, the electronic signature device is controlled to perform an electronic signature task on the project acceptance report.

2. The method according to claim 1, characterized in that, in After performing objection detection processing on the objection node set to obtain the objection detection result, the method further includes: In response to determining that the objection detection result does not meet the objection detection conditions, the preset acceptance return data is determined as acceptance data to be sent. The acceptance data to be sent is sent to the target terminal.

3. The method according to claim 1, characterized in that, Each actual quantity data point in the aforementioned actual quantity data points corresponds to a material quantity label and a component name; and based on the expected quantity data points corresponding to the three-level construction tree, the pre-acquired actual quantity data points are used to perform engineering deviation verification to obtain various deviation verification values, including: For each actual quantity data in the aforementioned actual quantity data, perform the following steps: Based on the three-level tree of engineering construction and the material total quantity labels corresponding to the actual engineering quantity data, the intermediate nodes of the project are determined. Based on the component names corresponding to the intermediate nodes of the project and the actual project quantity data, determine the leaf nodes of the project; Based on the expected project quantity data corresponding to the leaf nodes of the project and the three-level tree of the project construction, the target expected project quantity data is determined. Based on the preset correction coefficient correspondence table and the intermediate nodes of the project, the project correction coefficient is determined; The product of the target expected project quantity data and the project correction coefficient is determined as the corrected expected project quantity; The difference between the actual engineering quantity data and the corrected expected engineering quantity is determined as the engineering quantity difference value; The ratio of the difference in the amount of work to the corrected expected amount of work is determined as the deviation verification value.

4. The method according to claim 1, characterized in that, The engineering construction three-level tree includes each intermediate node and each leaf node; and based on each deviation verification value, the engineering construction three-level tree is subjected to objection node screening processing to obtain an objection node set, an objection-related node set, and a no-objection node set, including: For each of the aforementioned deviation verification values, perform the following steps: Based on the deviation verification value and the intermediate nodes and leaf nodes included in the engineering construction three-level tree, the target deviation intermediate node and the target deviation leaf node are determined. Based on the preset node threshold correspondence table and the target deviation intermediate node, the objection deviation threshold and objection-related threshold are determined; The absolute value of the deviation verification value is determined as the absolute value of the deviation verification. In response to determining that the deviation check value is greater than the objection deviation threshold, the target deviation leaf node is identified as an objection node; In response to determining that the deviation check value is less than or equal to the objection deviation threshold, the following steps are performed: In response to determining that the deviation check value is greater than the objection-related threshold, the target deviation leaf node is determined as an objection-related node; In response to determining that the deviation check value is less than or equal to the objection-related threshold, the target deviation leaf node is determined as a no-objection node; Combine the identified objection nodes into an objection node set; The identified objection-related nodes are combined into an objection-related node set; The identified undisputed nodes are combined into a set of undisputed nodes.

5. The method according to claim 1, characterized in that, The engineering construction three-level tree includes intermediate nodes and leaf nodes, and each intermediate node includes leaf nodes; and based on the deviation verification values, the engineering construction three-level tree is subjected to objection node screening processing to obtain an objection node set, an objection-related node set, and a no-objection node set, including: For each intermediate node in the three-level tree of the construction project, perform the following steps: Each leaf node included in the intermediate node is determined as a leaf node to be processed. The number of each leaf node to be processed is determined as the number of leaf nodes; Based on each leaf node to be processed and each deviation verification value, determine each verification value to be processed; Based on the various verification values ​​to be processed, generate the average value of the verification values ​​to be processed and the standard value of the verification values ​​to be processed. Based on the mean value of the verification to be processed and the standard value of the verification to be processed, outlier detection processing is performed on each of the verification values ​​to be processed to obtain each outlier value to be processed. Based on the mean value of the verification to be processed, deviation magnitude detection processing is performed on each of the verification values ​​to be processed to obtain each deviation magnitude value; Based on each leaf node to be processed, the number of leaf nodes, each verification value to be processed, each outlier value to be processed, and each deviation magnitude value, a group of objection nodes, a group of objection-related nodes, and a group of no objection nodes are generated. Based on the generated groups of dissenting nodes, groups of nodes related to dissenting issues, and groups of nodes without dissenting issues, a set of dissenting nodes, a set of nodes related to dissenting issues, and a set of nodes without dissenting issues are generated.

6. The method according to claim 1, characterized in that, The step of controlling the electronic signature device to perform an electronic signature task on the project acceptance report based on the project acceptance report includes: The project acceptance report is processed by data encoding to obtain the project acceptance string; For each of the preset signature numbers, obtain the corresponding signature page number and signature position data; For each of the signature numbers, the signature number, the corresponding signature page number, and the signature position data are combined into signature data. Based on the project acceptance report, the project acceptance string, and the obtained signature data, the electronic signature device is controlled to perform an electronic signature task on the project acceptance report.

7. A control device for electronic signature equipment used in online acceptance of engineering quantities, characterized in that, include: The acquisition unit is configured to acquire a three-level engineering construction tree in response to receiving an electronic signature signal sent by an electronic signature device, wherein the three-level engineering construction tree corresponds to various expected engineering quantity data. The engineering deviation verification unit is configured to perform engineering deviation verification on each of the pre-acquired actual engineering quantity data based on the expected engineering quantity data corresponding to the three-level engineering construction tree, and obtain each deviation verification value. The objection node filtering unit is configured to perform objection node filtering processing on the three-level tree of engineering construction based on the various deviation verification values, and obtain an objection node set, an objection-related node set, and a no-objection node set. The objection detection unit is configured to perform objection detection processing on the objection node set to obtain the objection detection result; The verification and confirmation unit is configured to perform verification and confirmation processing on the objection node set, the objection-related node set, and the non-objection node set in response to determining that the objection detection result meets the preset objection detection conditions, and to obtain the verification and confirmation result. The generation unit is configured to generate an engineering acceptance report based on the inspection and confirmation results; The control unit is configured to control the electronic signature device to perform an electronic signature task on the project acceptance report based on the project acceptance report.

8. An electronic device, characterized in that, include: One or more processors; A storage device on which one or more programs are stored; When the one or more programs are executed by the one or more processors, the one or more processors implement the method as described in any one of claims 1 to 6.

9. A computer-readable medium having a computer program stored thereon, characterized in that, When the program is executed by the processor, it implements the method as described in any one of claims 1 to 6.

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